# include <cstdlib>
# include <iostream>
# include <iomanip>
# include <cmath>
# include <ctime>

using namespace std;

# include "blas1_s.hpp"
# include "linpack_s.hpp"

//****************************************************************************80

int schdc ( float a[], int lda, int p, float work[], int ipvt[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SCHDC computes the Cholesky decomposition of a positive definite matrix.
//
//  Discussion:
//
//    A pivoting option allows the user to estimate the condition of a
//    positive definite matrix or determine the rank of a positive
//    semidefinite matrix.
//
//    For positive definite matrices, INFO = P is the normal return.
//
//    For pivoting with positive semidefinite matrices, INFO will
//    in general be less than P.  However, INFO may be greater than
//    the rank of A, since rounding error can cause an otherwise zero
//    element to be positive.  Indefinite systems will always cause
//    INFO to be less than P.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*P].
//    On input, A contains the matrix whose decomposition is to
//    be computed.  Only the upper half of A need be stored.
//    The lower part of the array a is not referenced.
//    On output, A contains in its upper half the Cholesky factor
//    of the input matrix, as it has been permuted by pivoting.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int P, the order of the matrix.
//
//    Input, float WORK[P] is a work array.
//
//    Input/output, int IPVT[P].
//    On input, IPVT contains integers that control the selection
//    of the pivot elements, if pivoting has been requested.
//    Each diagonal element A(K,K) is placed in one of three classes
//    according to the value of IPVT(K).
//
//      > 0, then X(K) is an initial element.
//      = 0, then X(K) is a free element.
//      < 0, then X(K) is a final element.
//
//    Before the decomposition is computed, initial elements are moved by
//    symmetric row and column interchanges to the beginning of the array A
//    and final elements to the end.  Both initial and final elements are
//    frozen in place during the computation and only free elements are moved.
//    At the K-th stage of the reduction, if A(K,K) is occupied by a free
//    element, it is interchanged with the largest free element A(L,L) with
//    K <= L.  IPVT is not referenced if JOB is 0.
//
//    On output, IPVT(J) contains the index of the diagonal element
//    of A that was moved into the J-th position, if pivoting was requested.
//
//    Input, int JOB, initiates column pivoting.
//    0, no pivoting is done.
//    nonzero, pivoting is done.
//
//    Output, int SCHDC, contains the index of the last positive diagonal
//    element of the Cholesky factor.
//
{
  int info;
  int j;
  int jp;
  int jt;
  int k;
  int l;
  float maxdia;
  int maxl;
  bool negk;
  int pl;
  int pu;
  bool swapk;
  float temp;

  pl = 1;
  pu = 0;
  info = p;
//
//  Pivoting has been requested.
//  Rearrange the the elements according to IPVT.
//
  if ( job != 0 )
  {
    for ( k = 1; k <= p; k++ )
    {
      swapk = ( 0 < ipvt[k-1] );
      negk = ( ipvt[k-1] < 0 );

      if ( negk )
      {
        ipvt[k-1] = -k;
      }
      else
      {
        ipvt[k-1] = k;
      }

      if ( swapk )
      {
        if ( k != pl )
        {
          sswap ( pl-1, a+0+(k-1)*lda, 1, a+0+(pl-1)*lda, 1 );

          temp = a[k-1+(k-1)*lda];
          a[k-1+(k-1)*lda] = a[pl-1+(pl-1)*lda];
          a[pl-1+(pl-1)*lda] = temp;

          for ( j = pl+1; j <= p; j++ )
          {
            if ( j < k )
            {
              temp = a[pl-1+(j-1)*lda];
              a[pl-1+(j-1)*lda] = a[j-1+(k-1)*lda];
              a[j-1+(k-1)*lda] = temp;
            }
            else if ( k < j )
            {
              temp = a[k-1+(j-1)*lda];
              a[k-1+(j-1)*lda] = a[pl-1+(j-1)*lda];
              a[pl-1+(j-1)*lda] = temp;
            }
          }
          ipvt[k-1] = ipvt[pl-1];
          ipvt[pl-1] = k;
        }
        pl = pl + 1;
      }
    }
    pu = p;

    for ( k = p; pl <= k; k-- )
    {
      if ( ipvt[k-1] < 0 )
      {
        ipvt[k-1] = -ipvt[k-1];

        if ( pu != k )
        {
          sswap ( k-1, a+0+(k-1)*lda, 1, a+0+(pu-1)*lda, 1 );

          temp = a[k-1+(k-1)*lda];
          a[k-1+(k-1)*lda] = a[pu-1+(pu-1)*lda];
          a[pu-1+(pu-1)*lda] = temp;

          for ( j = k+1; j <= p; j++ )
          {
            if ( j < pu )
            {
              temp = a[k-1+(j-1)*lda];
              a[k-1+(j-1)*lda] = a[j-1+(pu-1)*lda];
              a[j-1+(pu-1)*lda] = temp;
            }
            else if ( pu < j )
            {
              temp = a[k-1+(j-1)*lda];
              a[k-1+(j-1)*lda] = a[pu-1+(j-1)*lda];
              a[pu-1+(j-1)*lda] = temp;
            }
          }
          jt = ipvt[k-1];
          ipvt[k-1] = ipvt[pu-1];
          ipvt[pu-1] = jt;
        }
        pu = pu - 1;
      }
    }
  }

  for ( k = 1; k <= p; k++ )
  {
//
//  Reduction loop.
//
    maxdia = a[k-1+(k-1)*lda];
    maxl = k;
//
//  Determine the pivot element.
//
    if ( pl <= k && k < pu )
    {
      for ( l = k+1; l <= pu; l++ )
      {
        if ( maxdia < a[l-1+(l-1)*lda] )
        {
          maxdia = a[l-1+(l-1)*lda];
          maxl = l;
        }
      }
    }
//
//  Quit if the pivot element is not positive.
//
    if ( maxdia <= 0.0 )
    {
      info = k - 1;
      return info;
    }
//
//  Start the pivoting and update IPVT.
//
    if ( k != maxl )
    {
      sswap ( k-1, a+0+(k-1)*lda, 1, a+0+(maxl-1)*lda, 1 );
      a[maxl-1+(maxl-1)*lda] = a[k-1+(k-1)*lda];
      a[k-1+(k-1)*lda] = maxdia;
      jp = ipvt[maxl-1];
      ipvt[maxl-1] = ipvt[k-1];
      ipvt[k-1] = jp;
    }
//
//  Reduction step.
//  Pivoting is contained across the rows.
//
    work[k-1] = sqrt ( a[k-1+(k-1)*lda] );
    a[k-1+(k-1)*lda] = work[k-1];

    for ( j = k+1; j <= p; j++ )
    {
      if ( k != maxl )
      {
        if ( j < maxl )
        {
          temp = a[k-1+(j-1)*lda];
          a[k-1+(j-1)*lda] = a[j-1+(maxl-1)*lda];
          a[j-1+(maxl-1)*lda] = temp;
        }
        else if ( maxl < j )
        {
          temp = a[k-1+(j-1)*lda];
          a[k-1+(j-1)*lda] = a[maxl-1+(j-1)*lda];
          a[maxl-1+(j-1)*lda] = temp;
        }
      }
      a[k-1+(j-1)*lda] = a[k-1+(j-1)*lda] / work[k-1];
      work[j-1] = a[k-1+(j-1)*lda];
      temp = -a[k-1+(j-1)*lda];
      saxpy ( j-k, temp, work+k, 1, a+k+(j-1)*lda, 1 );
    }
  }

  return info;
}
//****************************************************************************80

int schdd ( float r[], int ldr, int p, float x[], float z[], int ldz, 
  int nz, float y[], float rho[], float c[], float s[] )

//****************************************************************************80
//
//  Purpose:
//
//    SCHDD downdates an augmented Cholesky decomposition.
//
//  Discussion:
//
//    SCHDD can also downdate the triangular factor of an augmented QR
//    decomposition.
//
//    Specifically, given an upper triangular matrix R of order P, a
//    row vector X, a column vector Z, and a scalar Y, SCHDD
//    determines an orthogonal matrix U and a scalar ZETA such that
//
//          (R   Z )     (RR  ZZ)
//      U * (      )  =  (      ),
//          (0 ZETA)     ( X   Y)
//
//    where RR is upper triangular.
//
//    If R and Z have been obtained from the factorization of a least squares
//    problem, then RR and ZZ are the factors corresponding to the problem
//    with the observation (X,Y) removed.  In this case, if RHO
//    is the norm of the residual vector, then the norm of
//    the residual vector of the downdated problem is
//    sqrt ( RHO * RHO - ZETA * ZETA ). SCHDD will simultaneously downdate
//    several triplets (Z, Y, RHO) along with R.
//
//    For a less terse description of what SCHDD does and how
//    it may be applied, see the LINPACK guide.
//
//    The matrix U is determined as the product U(1)*...*U(P)
//    where U(I) is a rotation in the (P+1,I)-plane of the form
//
//      ( C(I)      -S(I)    )
//      (                    ).
//      ( S(I)       C(I)    )
//
//    The rotations are chosen so that C(I) is real.
//
//    The user is warned that a given downdating problem may be impossible
//    to accomplish or may produce inaccurate results.  For example, this
//    can happen if X is near a vector whose removal will reduce the
//    rank of R.  Beware.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float R[LDR*P], the upper triangular matrix that
//    is to be  downdated.  The part of R below the diagonal is not referenced.
//
//    Input, int LDR, the leading dimension of the array R.
//    LDR must be at least P.
//
//    Input, int P, the order of the matrix R.
//
//    Input, float X[P], the row vector that is to be removed from R.
//
//    Input/output, float Z[LDZ*NZ], an array of NZ P-vectors
//    which are to be downdated along with R.
//
//    Input, int LDZ, the leading dimension of the array Z.
//    LDZ must be at least P.
//
//    Input, int NZ, the number of vectors to be downdated.
//    NZ may be zero, in which case Z, Y, and RHO are not referenced.
//
//    Input, float Y[NZ], the scalars for the downdating of
//    the vectors Z.
//
//    Input/output, float RHO[NZ], the norms of the residual vectors.
//    On output these have been changed along with R and Z.
//
//    Output, float C[P], S[P], the cosines and sines of the
//    transforming rotations.
//
//    Output, int SCHDD, return flag.
//     0, the entire downdating was successful.
//    -1, if R could not be downdated.  In this case, all quantities
//        are left unaltered.
//     1, if some RHO could not be downdated.  The offending RHO's are
//        set to -1.
//
{
  float a;
  float alpha;
  float azeta;
  float b;
  int i;
  int ii;
  int info;
  int j;
  float norm;
  float scale;
  float t;
  float xx;
  float zeta;
//
//  Solve R' * A = X, placing the result in the array S.
//
  info = 0;
  s[0] = x[0] / r[0+0*ldr];

  for ( j = 2; j <= p; j++ )
  {
    s[j-1] = x[j-1] - sdot ( j-1, r+0+(j-1)*ldr, 1, s, 1 );
    s[j-1] = s[j-1] / r[j-1+(j-1)*ldr];
  }

  norm = snrm2 ( p, s, 1 );

  if ( 1.0 <= norm )
  {
    info = -1;
    return info;
  }

  alpha = sqrt ( 1.0 - norm * norm );
//
//  Determine the transformations.
//
  for ( ii = 1; ii <= p; ii++ )
  {
    i = p - ii + 1;
    scale = alpha + r4_abs ( s[i-1] );
    a = alpha / scale;
    b = s[i-1] / scale;
    norm = sqrt ( a * a + b * b );
    c[i-1] = a / norm;
    s[i-1] = b / norm;
    alpha = scale * norm;
  }
//
//  Apply the transformations to R.
//
  for ( j = 1; j <= p; j++ )
  {
    xx = 0.0;
    for ( ii = 1; ii <= j; ii++ )
    {
      i = j - ii + 1;
      t = c[i-1] * xx + s[i-1] * r[i-1+(j-1)*ldr];
      r[i-1+(j-1)*ldr] = c[i-1] * r[i-1+(j-1)*ldr] - s[i-1] * xx;
      xx = t;
    }
  }
//
//  If required, downdate Z and RHO.
//
  for ( j = 1; j <= nz; j++ )
  {
    zeta = y[j-1];
    for ( i = 1; i <= p; i++ )
    {
      z[i-1+(j-1)*ldz] = ( z[i-1+(j-1)*ldz] - s[i-1] * zeta ) / c[i-1];
      zeta = c[i-1] * zeta - s[i-1] * z[i-1+(j-1)*ldz];
    }

    azeta = r4_abs ( zeta );

    if ( rho[j-1] < azeta )
    {
      info = 1;
      rho[j-1] = -1.0;
    }
    else
    {
      rho[j-1] = rho[j-1] * sqrt ( 1.0 - pow ( azeta / rho[j-1], 2 ) );
    }
  }

  return info;
}
//****************************************************************************80

void schex ( float r[], int ldr, int p, int k, int l, float z[], int ldz, 
  int nz, float c[], float s[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SCHEX updates the Cholesky factorization of a positive definite matrix.
//
//  Discussion:
//
//    The factorization has the form
//
//      A = R' * R
//
//    where A is a positive definite matrix of order P.
//
//    The updating involves diagonal permutations of the form
//
//      E' * A * E
//
//    where E is a permutation matrix.  Specifically, given
//    an upper triangular matrix R and a permutation matrix
//    E (which is specified by K, L, and JOB), SCHEX determines
//    an orthogonal matrix U such that
//
//      U * R * E = RR,
//
//    where RR is upper triangular.  At the user's option, the
//    transformation U will be multiplied into the array Z.
//    If A = X'*X, so that R is the triangular part of the
//    QR factorization of X, then RR is the triangular part of the
//    QR factorization of X*E, that is, X with its columns permuted.
//
//    For a less terse description of what SCHEX does and how
//    it may be applied, see the LINPACK guide.
//
//    The matrix Q is determined as the product U(L-K)*...*U(1)
//    of plane rotations of the form
//
//      (    C(I)       S(I) )
//      (                    ),
//      (   -S(I)       C(I) )
//
//    where C(I) is real, the rows these rotations operate on
//    are described below.
//
//    There are two types of permutations, which are determined
//    by the value of JOB.
//
//    1, right circular shift.  The columns are rearranged in the order:
//
//         1,...,K-1,L,K,K+1,...,L-1,L+1,...,P.
//
//       U is the product of L-K rotations U(I), where U(I)
//       acts in the (L-I,L-I+1)-plane.
//
//    2, left circular shift: the columns are rearranged in the order
//
//         1,...,K-1,K+1,K+2,...,L,K,L+1,...,P.
//
//       U is the product of L-K rotations U(I), where U(I)
//       acts in the (K+I-1,K+I)-plane.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float R[LDR*P].  On input, the upper
//    triangular factor that is to be updated.  Elements of R below the
//    diagonal are not referenced.  On output, R has been updated.
//
//    Input, int LDR, the leading dimension of the array R.
//    LDR must be at least P.
//
//    Input, int P, the order of the matrix R.
//
//    Input, int K, the first column to be permuted.
//
//    Input, int L, the last column to be permuted.
//    L must be strictly greater than K.
//
//    Input/output float Z[LDZ*NZ], an array of NZ P-vectors into
//    which the transformation U is multiplied.  Z is not referenced if NZ = 0.
//    On output, Z has been updated.
//
//    Input, int LDZ, the leading dimension of the array Z.
//    LDZ must be at least P.
//
//    Input, int NZ, the number of columns of the matrix Z.
//
//    Output, float C[P], S[P], the cosines and sines of the
//    transforming rotations.
//
//    Input, int JOB, determines the type of permutation.
//    1, right circular shift.
//    2, left circular shift.
//
{
  int i;
  int ii;
  int il;
  int iu;
  int j;
  int jj;
  int lm1;
  int lmk;
  float t;
//
//  Initialize
//
  lmk = l - k;
  lm1 = l - 1;
//
//  Right circular shift.
//
  if ( job == 1 )
  {
//
//  Reorder the columns.
//
    for ( i = 1; i <= l; i++ )
    {
      ii = l - i + 1;
      s[i-1] = r[ii-1+(l-1)*ldr];
    }

    for ( jj = k; jj <= lm1; jj++ )
    {
      j = lm1 - jj + k;
      for ( i = 1; i <= j; i++ )
      {
        r[i-1+(j)*ldr] = r[i-1+(j-1)*ldr];
      }
      r[j+(j)*ldr] = 0.0;
    }

    for ( i = 1; i <= k-1; i++ )
    {
      ii = l - i + 1;
      r[i-1+(k-1)*ldr] = s[ii-1];
    }
//
//  Calculate the rotations.
//
    t = s[0];
    for ( i = 1; i <= lmk; i++ )
    {
      srotg ( s+i, &t, c+i-1, s+i-1 );
      t = s[i];
    }

    r[k-1+(k-1)*ldr] = t;

    for ( j = k+1; j <= p; j++ )
    {
      il = i4_max ( 1, l-j+1 );
      for ( ii = il; ii <= lmk; ii++ )
      {
        i = l - ii;
        t = c[ii-1] * r[i-1+(j-1)*ldr] + s[ii-1] * r[i+(j-1)*ldr];
        r[i+(j-1)*ldr] = c[ii-1] * r[i+(j-1)*ldr] - s[ii-1] * r[i-1+(j-1)*ldr];
        r[i-1+(j-1)*ldr] = t;
      }
    }
//
//  If required, apply the transformations to Z.
//
    for ( j = 1; j <= nz; j++ )
    {
      for ( ii = 1; ii <= lmk; ii++ )
      {
        i = l - ii;
        t = c[ii-1] * z[i-1+(j-1)*ldr] + s[ii-1] * z[i+(j-1)*ldr];
        z[i+(j-1)*ldr] = c[ii-1] * z[i+(j-1)*ldr] - s[ii-1] * z[i-1+(j-1)*ldr];
        z[i-1+(j-1)*ldr] = t;
      }
    }
  }
//
//  Left circular shift.
//
  else
  {
//
//  Reorder the columns.
//
    for ( i = 1; i <= k; i++ )
    {
      ii = lmk + i;
      s[ii-1] = r[i-1+(k-1)*ldr];
    }

    for ( j = k; j <= lm1; j++ )
    {
      for ( i = 1; i <= j; i++ )
      {
        r[i-1+(j-1)*ldr] = r[i-1+(j)*ldr];
      }
      jj = j - k + 1;
      s[jj-1] = r[j+(j)*ldr];
    }

    for ( i = 1; i <= k; i++ )
    {
      ii = lmk + i;
      r[i-1+(l-1)*ldr] = s[ii-1];
    }

    for ( i = k+1; i <= l; i++ )
    {
      r[i-1+(l-1)*ldr] = 0.0;
    }
//
//  Reduction loop.
//
    for ( j = k; j <= p; j++ )
    {
//
//  Apply the rotations.
//
      if ( j != k )
      {
        iu = i4_min ( j-1, l-1 );

        for ( i = k; i <= iu; i++ )
        {
          ii = i - k + 1;
          t = c[ii-1] * r[i-1+(j-1)*ldr] + s[ii-1] * r[i+(j-1)*ldr];
          r[i+(j-1)*ldr] = c[ii-1] * r[i+(j-1)*ldr] 
            - s[ii-1] * r[i-1+(j-1)*ldr];
          r[i-1+(j-1)*ldr] = t;
        }
      }

      if ( j < l )
      {
        jj = j - k + 1;
        t = s[jj-1];
        srotg ( r+j-1+(j-1)*ldr, &t, c+jj-1, s+jj-1 );
      }
    }
//
//  Apply the rotations to Z.
//
    for ( j = 1; j <= nz; j++ )
    {
      for ( i = k; i <= lm1; i++ )
      {
        ii = i - k + 1;
        t = c[ii-1] * z[i-1+(j-1)*ldr] + s[ii-1] * z[i+(j-1)*ldr];
        z[i+(j-1)*ldr] = c[ii-1] * z[i+(j-1)*ldr] - s[ii-1] * z[i-1+(j-1)*ldr];
        z[i-1+(j-1)*ldr] = t;
      }
    }
  }

  return;
}
//****************************************************************************80

void schud ( float r[], int ldr, int p, float x[], float z[], int ldz, int nz, 
  float y[], float rho[], float c[], float s[] )

//****************************************************************************80
//
//  Purpose:
//
//    SCHUD updates an augmented Cholesky decomposition.
//
//  Discussion:
//
//    SCHUD can also update the triangular part of an augmented QR
//    decomposition.
//
//    Specifically, given an upper triangular matrix R of order P, a row vector
//    X, a column vector Z, and a scalar Y, SCHUD determines a unitary matrix
//    U and a scalar ZETA such that
//
//           (R  Z)     (RR   ZZ )
//      U  * (    )  =  (        ),
//           (X  Y)     ( 0  ZETA)
//
//    where RR is upper triangular.
//
//    If R and Z have been obtained from the factorization of a least squares
//    problem, then RR and ZZ are the factors corresponding to the problem
//    with the observation (X,Y) appended.  In this case, if RHO is the
//    norm of the residual vector, then the norm of the residual vector of
//    the updated problem is sqrt ( RHO * RHO + ZETA * ZETA ).  SCHUD will
//    simultaneously update several triplets (Z, Y, RHO).
//
//    For a less terse description of what SCHUD does and how
//    it may be applied, see the LINPACK guide.
//
//    The matrix U is determined as the product U(P)*...*U(1),
//    where U(I) is a rotation in the (I,P+1) plane of the form
//
//      (     C(I)      S(I) )
//      (                    ).
//      (    -S(I)      C(I) )
//
//    The rotations are chosen so that C(I) is real.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float R[LDR*P], the upper triangular matrix to be
//    updated.  The part of R below the diagonal is not referenced.
//    On output, the matrix has been updated.
//
//    Input, int LDR, the leading dimension of the array R.
//    LDR must be at least equal to P.
//
//    Input, int P, the order of the matrix R.
//
//    Input, float X[P], the row to be added to R.
//
//    Input/output, float Z[LDZ*NZ], contains NZ P-vectors
//    to be updated with R.
//
//    Input, int LDZ, the leading dimension of the array Z.
//    LDZ must be at least P.
//
//    Input, int NZ, the number of vectors to be updated.  NZ may be
//    zero, in which case Z, Y, and RHO are not referenced.
//
//    Input, float Y[NZ], the scalars for updating the vectors Z.
//
//    Input/output, float RHO[NZ].  On input, the norms of the
//    residual vectors to be updated.  If RHO(J) is negative, it is left
//    unaltered.
//
//    Output, float C[P], S[P], the cosines and sines of the
//    transforming rotations.
//
{
  float azeta;
  int i;
  int j;
  float scale;
  float t;
  float xj;
  float zeta;
//
//  Update R.
//
  for ( j = 1; j <= p; j++ )
  {
    xj = x[j-1];
//
//  Apply the previous rotations.
//
    for ( i = 1; i <= j-1; i++ )
    {
      t = c[i-1] * r[i-1+(j-1)*ldz] + s[i-1] * xj;
      xj = c[i-1] * xj - s[i-1] * r[i-1+(j-1)*ldz];
      r[i-1+(j-1)*ldz] = t;
    }
//
//  Compute the next rotation.
//
    srotg ( r+j-1+(j-1)*ldr, &xj, c+j-1, s+j-1 );
  }
//
//  If required, update Z and RHO.
//
  for ( j = 1; j <= nz; j++ )
  {
    zeta = y[j-1];
    for ( i = 1; i <= p; i++ )
    {
      t =    c[i-1] * z[i-1+(j-1)*ldz] + s[i-1] * zeta;
      zeta = c[i-1] * zeta   - s[i-1] * z[i-1+(j-1)*ldz];
      z[i-1+(j-1)*ldz] = t;
    }

    azeta = r4_abs ( zeta );

    if ( azeta != 0.0 && 0.0 <= rho[j-1] )
    {
      scale = azeta + rho[j-1];
      rho[j-1] = scale * sqrt ( 
        pow ( azeta / scale, 2 ) + pow ( rho[j-1] / scale, 2 ) );
    }

  }

  return;
}
//****************************************************************************80

float sgbco ( float abd[], int lda, int n, int ml, int mu, int ipvt[], 
  float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SGBCO factors a real band matrix and estimates its condition.
//
//  Discussion:
//
//    If RCOND is not needed, SGBFA is slightly faster.
//
//    To solve A*X = B, follow SGBCO by SGBSL.
//
//    To compute inverse(A)*C, follow SGBCO by SGBSL.
//
//    To compute determinant(A), follow SGBCO by SGBDI.
//
//  Example:
//
//    If the original matrix is
//
//      11 12 13  0  0  0
//      21 22 23 24  0  0
//       0 32 33 34 35  0
//       0  0 43 44 45 46
//       0  0  0 54 55 56
//       0  0  0  0 65 66
//
//    then for proper band storage,
//
//      N = 6, ML = 1, MU = 2, 5 <= LDA and ABD should contain
//
//       *  *  *  +  +  +      * = not used
//       *  * 13 24 35 46      + = used for pivoting
//       * 12 23 34 45 56
//      11 22 33 44 55 66
//      21 32 43 54 65  *
//
//  Band storage:
//
//    If A is a band matrix, the following program segment
//    will set up the input.
//
//      ml = (band width below the diagonal)
//      mu = (band width above the diagonal)
//      m = ml + mu + 1
//
//      do j = 1, n
//        i1 = max ( 1, j-mu )
//        i2 = min ( n, j+ml )
//        do i = i1, i2
//          k = i - j + m
//          abd(k,j) = a(i,j)
//        }
//      }
//
//    This uses rows ML+1 through 2*ML+MU+1 of ABD.  In addition, the first
//    ML rows in ABD are used for elements generated during the
//    triangularization.  The total number of rows needed in ABD is
//    2*ML+MU+1.  The ML+MU by ML+MU upper left triangle and the ML by ML
//    lower right triangle are not referenced.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float ABD[LDA*N].  On input, the matrix in band
//    storage.  The columns of the matrix are stored in the columns of ABD and
//    the diagonals of the matrix are stored in rows ML+1 through 2*ML+MU+1
//    of ABD.  On output, an upper triangular matrix in band storage and
//    the multipliers which were used to obtain it.  The factorization can
//    be written A = L*U where L is a product of permutation and unit lower
//    triangular matrices and U is upper triangular.
//
//    Input, int LDA, the leading dimension of the array ABD.
//    2*ML + MU + 1 <= LDA is required.
//
//    Input, int N, the order of the matrix.
//
//    Input, int ML, MU, the number of diagonals below and above the
//    main diagonal.  0 <= ML < N, 0 <= MU < N.
//
//    Output, int IPVT[N], the pivot indices.
//
//    Workspace, float Z[N], a work vector whose contents are
//    usually unimportant.  If A is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//
//    Output, float SGBCO, an estimate of the reciprocal condition number RCOND
//    of A.  For the system A*X = B, relative perturbations in A and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float anorm;
  float ek;
  int i;
  int info;
  int is;
  int j;
  int ju;
  int k;
  int l;
  int la;
  int lm;
  int lz;
  int m;
  int mm;
  float rcond;
  float s;
  float sm;
  float t;
  float wk;
  float wkm;
  float ynorm;
//
//  Compute the 1-norm of A.
//
  anorm = 0.0;
  l = ml + 1;
  is = l + mu;

  for ( j = 1; j <= n; j++ )
  {
    anorm = r4_max ( anorm, sasum ( l, abd+is-1+(j-1)*lda, 1 ) );
    if ( ml + 1 < is )
    {
      is = is - 1;
    }
    if ( j <= mu )
    {
      l = l + 1;
    }
    if ( n - ml <= j )
    {
      l = l - 1;
    }
  }
//
//  Factor.
//
  info = sgbfa ( abd, lda, n, ml, mu, ipvt );
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where  a*z = y  and A'*Y = E.
//
//  A' is the transpose of A.  The components of E are
//  chosen to cause maximum local growth in the elements of W where
//  U'*W = E.  The vectors are frequently rescaled to avoid
//  overflow.
//
//  Solve U' * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }
  m = ml + mu + 1;
  ju = 0;

  for ( k = 1; k <= n; k++ )
  {
    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( -z[k-1] );
    }

    if ( r4_abs ( abd[m-1+(k-1)*lda] ) < r4_abs ( ek - z[k-1] ) )
    {
      s = r4_abs ( abd[m-1+(k-1)*lda] ) / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }

    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );

    if ( abd[m-1+(k-1)*lda] != 0.0 )
    {
      wk = wk / abd[m-1+(k-1)*lda];
      wkm = wkm / abd[m-1+(k-1)*lda];
    }
    else
    {
      wk = 1.0;
      wkm = 1.0;
    }

    ju = i4_min ( i4_max ( ju, mu+ipvt[k-1] ), n );
    mm = m;

    if ( k+1 <= ju )
    {
      for ( j = k+1; j <= ju; j++ )
      {
        mm = mm - 1;
        sm = sm + r4_abs ( z[j-1] + wkm * abd[mm-1+(j-1)*lda] );
        z[j-1] = z[j-1] + wk * abd[mm-1+(j-1)*lda];
        s = s + r4_abs ( z[j-1] );
      }

      if ( s < sm )
      {
        t = wkm - wk;
        wk = wkm;
        mm = m;
        for ( j = k+1; j <= ju; ju++ )
        {
          mm = mm - 1;
          z[j-1] = z[j-1] + t * abd[mm-1+(j-1)*lda];
        }
      }
    }
    z[k-1] = wk;
  }

  s = sasum ( n, z, 1 );

  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve L' * Y = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    lm = i4_min ( ml, n-k );

    if ( k < m )
    {
      z[k-1] = z[k-1] + sdot ( lm, abd+m+(k-1)*lda, 1, z+k, 1 );
    }

    if ( 1.0 < r4_abs ( z[k-1] ) )
    {
      s = 1.0 / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
    }
    l = ipvt[k-1];
    t = z[l-1];
    z[l-1] = z[k-1];
    z[k-1] = t;
  }

  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = 1.0;
//
//  Solve L * V = Y.
//
  for ( k = 1; k <= n; k++ )
  {
    l = ipvt[k-1];
    t = z[l-1];
    z[l-1] = z[k-1];
    z[k-1] = t;
    lm = i4_min ( ml, n-k );

    if ( k < n )
    {
      saxpy ( lm, t, abd+m+(k-1)*lda, 1, z+k, 1 );
    }

    if ( 1.0 < r4_abs ( z[k-1] ) )
    {
      s = 1.0 / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
  }
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve U * Z = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( r4_abs ( abd[m-1+(k-1)*lda] ) < r4_abs ( z[k-1] ) )
    {
      s = r4_abs ( abd[m-1+(k-1)*lda] ) / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }

    if ( abd[m-1+(k-1)*lda] != 0.0 )
    {
      z[k-1] = z[k-1] / abd[m-1+(k-1)*lda];
    }
    else
    {
      z[k-1] = 1.0;
    }

    lm = i4_min ( k, m ) - 1;
    la = m - lm;
    lz = k - lm;
    t = -z[k-1];
    saxpy ( lm, t, abd+la-1+(k-1)*lda, 1, z+lz-1, 1 );
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void sgbdi ( float abd[], int lda, int n, int ml, int mu, int ipvt[], 
  float det[2] )

//****************************************************************************80
//
//  Purpose:
//
//    SGBDI computes the determinant of a band matrix factored by SGBCO or SGBFA.
//
//  Discussion:
//
//    If the inverse is needed, use SGBSL N times.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float ABD[LDA*N], the LU factor information from SGBCO 
//    or SGBFA.
//
//    Input, int LDA, the leading dimension of the array ABD.
//
//    Input, int N, the order of the matrix.
//
//    Input, int ML, MU, the number of diagonals below and above the
//    main diagonal.  0 <= ML < N, 0 <= MU < N.
//
//    Input, int IPVT[N], the pivot vector from SGBCO or SGBFA.
//
//    Output, float DET[2], the determinant of the original matrix,
//    if requested.
//      determinant = DET[0] * 10.0**DET[1]
//    with  1.0D+00 <= abs ( DET[0] ) < 10.0D+00 or DET[0] = 0.0D+00.
//
{
  int i;
  int m;

  m = ml + mu + 1;
  det[0] = 1.0;
  det[1] = 0.0;

  for ( i = 1; i <= n; i++ )
  {
    if ( ipvt[i-1] != i )
    {
      det[0] = -det[0];
    }

    det[0] = det[0] * abd[m-1+(i-1)*lda];

    if ( det[0] == 0.0 )
    {
      return;
    }

    while ( r4_abs ( det[0] ) < 1.0 )
    {
      det[0] = det[0] * 10.0;
      det[1] = det[1] - 1.0;
    }

    while ( 10.0 <= r4_abs ( det[0] ) )
    {
      det[0] = det[0] / 10.0;
      det[1] = det[1] + 1.0;
    }
  }
  return;
}
//****************************************************************************80

int sgbfa ( float abd[], int lda, int n, int ml, int mu, int ipvt[] )

//****************************************************************************80
//
//  Purpose:
//
//    SGBFA factors a real band matrix by elimination.
//
//  Discussion:
//
//    SGBFA is usually called by SGBCO, but it can be called
//    directly with a saving in time if RCOND is not needed.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float ABD[LDA*N].  On input, the matrix in band
//    storage.  The columns of the matrix are stored in the columns of ABD
//    and the diagonals of the matrix are stored in rows ML+1 through
//    2*ML+MU+1 of ABD.  On output, an upper triangular matrix in band storage
//    and the multipliers which were used to obtain it.  The factorization
//    can be written A = L*U where L is a product of permutation and unit lower
//    triangular matrices and U is upper triangular.
//
//    Input, int LDA, the leading dimension of the array ABD.
//    2*ML + MU + 1 <= LDA is required.
//
//    Input, int N, the order of the matrix.
//
//    Input, int ML, MU, the number of diagonals below and above the
//    main diagonal.  0 <= ML < N, 0 <= MU < N.
//
//    Output, int IPVT[N], the pivot indices.
//
//    Output, integer SGBFA, error flag.
//    0, normal value.
//    K, if U(K,K) == 0.0D+00.  This is not an error condition for this
//      subroutine, but it does indicate that SGBSL will divide by zero if
//      called.  Use RCOND in SGBCO for a reliable indication of singularity.
//
{
  int i;
  int i0;
  int info;
  int j;
  int j0;
  int j1;
  int ju;
  int jz;
  int k;
  int l;
  int lm;
  int m;
  int mm;
  float t;

  m = ml + mu + 1;
  info = 0;
//
//  Zero initial fill-in columns.
//
  j0 = mu + 2;
  j1 = i4_min ( n, m ) - 1;

  for ( jz = j0; jz <= j1; jz++ )
  {
    i0 = m + 1 - jz;
    for ( i = i0; i <= ml; i++ )
    {
      abd[i-1+(jz-1)*lda] = 0.0;
    }
  }

  jz = j1;
  ju = 0;
//
//  Gaussian elimination with partial pivoting.
//
  for ( k = 1; k <= n-1; k++ )
  {
//
//  Zero out the next fill-in column.
//
    jz = jz + 1;
    if ( jz <= n )
    {
      for ( i = 1; i <= ml; i++ )
      {
        abd[i-1+(jz-1)*lda] = 0.0;
      }
    }
//
//  Find L = pivot index.
//
    lm = i4_min ( ml, n-k );
    l = isamax ( lm+1, abd+m-1+(k-1)*lda, 1 ) + m - 1;
    ipvt[k-1] = l + k - m;
//
//  Zero pivot implies this column already triangularized.
//
    if ( abd[l-1+(k-1)*lda] == 0.0 )
    {
      info = k;
    }
//
//  Interchange if necessary.
//
    else
    {
      if ( l != m )
      {
        t = abd[l-1+(k-1)*lda];
        abd[l-1+(k-1)*lda] = abd[m-1+(k-1)*lda];
        abd[m-1+(k-1)*lda] = t;
      }
//
//  Compute multipliers.
//
      t = -1.0 / abd[m-1+(k-1)*lda];
      sscal ( lm, t, abd+m+(k-1)*lda, 1 );
//
//  Row elimination with column indexing.
//
      ju = i4_min ( i4_max ( ju, mu+ipvt[k-1] ), n );
      mm = m;

      for ( j = k+1; j <= ju; j++ )
      {
        l = l - 1;
        mm = mm - 1;
        t = abd[l-1+(j-1)*lda];
        if ( l != mm )
        {
          abd[l-1+(j-1)*lda] = abd[mm-1+(j-1)*lda];
          abd[mm-1+(j-1)*lda] = t;
        }
        saxpy ( lm, t, abd+m+(k-1)*lda, 1, abd+mm+(j-1)*lda, 1 );
      }

    }

  }

  ipvt[n-1] = n;

  if ( abd[m-1+(n-1)*lda] == 0.0 )
  {
    info = n;
  }

  return info;
}
//****************************************************************************80

void sgbsl ( float abd[], int lda, int n, int ml, int mu, int ipvt[], 
  float b[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SGBSL solves a real banded system factored by SGBCO or DGBFA.
//
//  Discussion:
//
//    SGBSL can solve either A * X = B  or  A' * X = B.
//
//    A division by zero will occur if the input factor contains a
//    zero on the diagonal.  Technically this indicates singularity
//    but it is often caused by improper arguments or improper
//    setting of LDA.  It will not occur if the subroutines are
//    called correctly and if SGBCO has set 0.0 < RCOND
//    or SGBFA has set INFO == 0.
//
//    To compute inverse(A) * C  where C is a matrix with P columns:
//
//      call sgbco ( abd, lda, n, ml, mu, ipvt, rcond, z )
//
//      if ( rcond is too small ) then
//        exit
//      end if
//
//      do j = 1, p
//        call sgbsl ( abd, lda, n, ml, mu, ipvt, c(1,j), 0 )
//      end do
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float ABD[LDA*N], the output from SGBCO or SGBFA.
//
//    Input, integer LDA, the leading dimension of the array ABD.
//
//    Input, int N, the order of the matrix.
//
//    Input, int ML, MU, the number of diagonals below and above the
//    main diagonal.  0 <= ML < N, 0 <= MU < N.
//
//    Input, int IPVT[N], the pivot vector from SGBCO or SGBFA.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
//    Input, int JOB, job choice.
//    0, solve A*X=B.
//    nonzero, solve A'*X=B.
//
{
  int k;
  int l;
  int la;
  int lb;
  int lm;
  int m;
  float t;

  m = mu + ml + 1;
//
//  JOB = 0, Solve A * x = b.
//
//  First solve L * y = b.
//
  if ( job == 0 )
  {
    if ( 0 < ml )
    {
      for ( k = 1; k <= n-1; k++ )
      {
        lm = i4_min ( ml, n-k );
        l = ipvt[k-1];
        t = b[l-1];
        if ( l != k )
        {
          b[l-1] = b[k-1];
          b[k-1] = t;
        }
        saxpy ( lm, t, abd+m+(k-1)*lda, 1, b+k, 1 );
      }
    }
//
//  Now solve U * x = y.
//
    for ( k = n; 1 <= k; k-- )
    {
      b[k-1] = b[k-1] / abd[m-1+(k-1)*lda];
      lm = i4_min ( k, m ) - 1;
      la = m - lm;
      lb = k - lm;
      t = -b[k-1];
      saxpy ( lm, t, abd+la-1+(k-1)*lda, 1, b+lb-1, 1 );
    }
  }
//
//  JOB nonzero, solve A' * x = b.
//
//  First solve U' * y = b.
//
  else
  {
    for ( k = 1; k <= n; k++ )
    {
      lm = i4_min ( k, m ) - 1;
      la = m - lm;
      lb = k - lm;
      t = sdot ( lm, abd+la-1+(k-1)*lda, 1, b+lb-1, 1 );
      b[k-1] = ( b[k-1] - t ) / abd[m-1+(k-1)*lda];
    }
//
//  Now solve L' * x = y.
//
    if ( 0 < ml )
    {
      for ( k = n-1; 1 <= k; k-- )
      {
        lm = i4_min ( ml, n-k );
        b[k-1] = b[k-1] + sdot ( lm, abd+m+(k-1)*lda, 1, b+k, 1 );
        l = ipvt[k-1];
        if ( l != k )
        {
          t = b[l-1];
          b[l-1] = b[k-1];
          b[k-1] = t;
        }
      }
    }
  }

  return;
}
//****************************************************************************80

float sgeco ( float a[], int lda, int n, int ipvt[], float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SGECO factors a real matrix and estimates its condition number.
//
//  Discussion:
//
//    If RCOND is not needed, SGEFA is slightly faster.
//
//    To solve A * X = B, follow SGECO by SGESL.
//
//    To compute inverse ( A ) * C, follow SGECO by SGESL.
//
//    To compute determinant ( A ), follow SGECO by SGEDI.
//
//    To compute inverse ( A ), follow SGECO by SGEDI.
//
//    For the system A * X = B, relative perturbations in A and B
//    of size EPSILON may cause relative perturbations in X of size
//    EPSILON/RCOND.
//
//    If RCOND is so small that the logical expression
//      1.0D+00 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate
//    underflows.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, a matrix to be
//    factored.  On output, the LU factorization of the matrix.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix A.
//
//    Output, int IPVT[N], the pivot indices.
//
//    Output, float Z[N], a work vector whose contents are usually
//    unimportant.  If A is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm ( A * Z ) = RCOND * norm ( A ) * norm ( Z ).
//
//    Output, float SGECO, the value of RCOND, an estimate 
//    of the reciprocal condition number of A.
//
{
  float anorm;
  float ek;
  int i;
  int info;
  int j;
  int k;
  int l;
  float rcond;
  float s;
  float sm;
  float t;
  float wk;
  float wkm;
  float ynorm;
//
//  Compute the L1 norm of A.
//
  anorm = 0.0;
  for ( j = 1; j <= n; j++ )
  {
    anorm = r4_max ( anorm, sasum ( n, a+0+(j-1)*lda, 1 ) );
  }
//
//  Compute the LU factorization.
//
  info = sgefa ( a, lda, n, ipvt );
//
//  RCOND = 1 / ( norm(A) * (estimate of norm(inverse(A))) )
//
//  estimate of norm(inverse(A)) = norm(Z) / norm(Y)
//
//  where
//    A * Z = Y
//  and
//    A' * Y = E
//
//  The components of E are chosen to cause maximum local growth in the
//  elements of W, where U'*W = E.  The vectors are frequently rescaled
//  to avoid overflow.
//
//  Solve U' * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  for ( k = 1; k <= n; k++ )
  {
    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( -z[k-1] );
    }

    if ( r4_abs ( a[k-1+(k-1)*lda] ) < r4_abs ( ek - z[k-1] ) )
    {
      s = r4_abs ( a[k-1+(k-1)*lda] ) / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }

    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );

    if ( a[k-1+(k-1)*lda] != 0.0 )
    {
      wk = wk / a[k-1+(k-1)*lda];
      wkm = wkm / a[k-1+(k-1)*lda];
    }
    else
    {
      wk = 1.0;
      wkm = 1.0;
    }

    if ( k+1 <= n )
    {
      for ( j = k+1; j <= n; j++ )
      {
        sm = sm + r4_abs ( z[j-1] + wkm * a[k-1+(j-1)*lda] );
        z[j-1] = z[j-1] + wk * a[k-1+(j-1)*lda];
        s = s + r4_abs ( z[j-1] );
      }

      if ( s < sm )
      {
        t = wkm - wk;
        wk = wkm;
        for ( i = k+1; i <= n; i++ )
        {
          z[i-1] = z[i-1] + t * a[k-1+(i-1)*lda];
        }
      }
    }
    z[k-1] = wk;
  }

  t = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / t;
  }
//
//  Solve L' * Y = W
//
  for ( k = n; 1 <= k; k-- )
  {
    z[k-1] = z[k-1] + sdot ( n - k, a+k+(k-1)*lda, 1, z+k, 1 );

    if ( 1.0 < r4_abs ( z[k-1] ) )
    {
      t = r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = z[i-1] / t;
      }
    }

    l = ipvt[k-1];

    t = z[l-1];
    z[l-1] = z[k-1];
    z[k-1] = t;
  }
  t = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / t;
  }

  ynorm = 1.0;
//
//  Solve L * V = Y.
//
  for ( k = 1; k <= n; k++ )
  {
    l = ipvt[k-1];

    t = z[l-1];
    z[l-1] = z[k-1];
    z[k-1] = t;

    for ( i = k+1; i <= n; i++ )
    {
      z[i-1] = z[i-1] + t * a[i-1+(k-1)*lda];
    }

    if ( 1.0 < r4_abs ( z[k-1] ) )
    {
      ynorm = ynorm / r4_abs ( z[k-1] );
      t = r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = z[i-1] / t;
      }
    }
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = ynorm / s;
//
//  Solve U * Z = V.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( r4_abs ( a[k-1+(k-1)*lda] ) < r4_abs ( z[k-1] ) )
    {
      s = r4_abs ( a[k-1+(k-1)*lda] ) / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }

    if ( a[k-1+(k-1)*lda] != 0.0 )
    {
      z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
    }
    else
    {
      z[k-1] = 1.0;
    }
    for ( i = 1; i <= k-1; i++ )
    {
      z[i-1] = z[i-1] - z[k-1] * a[i-1+(k-1)*lda];
    }
  }
//
//  Normalize Z in the L1 norm.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void sgedi ( float a[], int lda, int n, int ipvt[], float det[], 
  float work[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SGEDI: determinant and inverse of a matrix factored by SGECO or SGEFA.
//
//  Discussion:
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal and the inverse is requested.
//    It will not occur if the subroutines are called correctly
//    and if SGECO has set 0.0 < RCOND or SGEFA has set INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N], on input, the LU factor information,
//    as output by SGECO or SGEFA.  On output, the inverse
//    matrix if requested.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix A.
//
//    Input, int IPVT[N], the pivot vector from SGECO or SGEFA.
//
//    Workspace, float WORK[N].
//
//    Output, float DET[2], the determinant of original matrix if
//    requested.  The determinant = DET[0] * pow ( 10.0, DET[1] )
//    with  1.0 <= abs ( DET[0] ) < 10.0 or DET[0] == 0.0.
//
//    Input, int JOB, specifies what is to be computed.
//    11, both determinant and inverse.
//    01, inverse only.
//    10, determinant only.
//
{
  int i;
  int j;
  int k;
  int l;
  float t;
//
//  Compute the determinant.
//
  if ( job / 10 != 0 )
  {
    det[0] = 1.0;
    det[1] = 0.0;

    for ( i = 1; i <= n; i++ )
    {
      if ( ipvt[i-1] != i )
      {
        det[0] = -det[0];
      }
      det[0] = det[0] * a[i-1+(i-1)*lda];

      if ( det[0] == 0.0 )
      {
        break;
      }

      while ( r4_abs ( det[0] ) < 1.0 )
      {
        det[0] = det[0] * 10.0;
        det[1] = det[1] - 1.0;
      }
      while ( 10.0 <= r4_abs ( det[0] ) )
      {
        det[0] = det[0] / 10.0;
        det[1] = det[1] + 1.0;
      }
    }
  }
//
//  Compute inverse(U).
//
  if ( ( job % 10 ) != 0 )
  {
    for ( k = 1; k <= n; k++ )
    {
      a[k-1+(k-1)*lda] = 1.0 / a[k-1+(k-1)*lda];
      t = -a[k-1+(k-1)*lda];
      sscal ( k-1, t, a+0+(k-1)*lda, 1 );

      for ( j = k+1; j <= n; j++ )
      {
        t = a[k-1+(j-1)*lda];
        a[k-1+(j-1)*lda] = 0.0;
        saxpy ( k, t, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
      }
    }
//
//  Form inverse(U) * inverse(L).
//
    for ( k = n-1; 1 <= k; k-- )
    {
      for ( i = k+1; i <= n; i++ )
      {
        work[i-1] = a[i-1+(k-1)*lda];
        a[i-1+(k-1)*lda] = 0.0;
      }

      for ( j = k+1; j <= n; j++ )
      {
        t = work[j-1];
        saxpy ( n, t, a+0+(j-1)*lda, 1, a+0+(k-1)*lda, 1 );
      }

      l = ipvt[k-1];
      if ( l != k )
      {
        sswap ( n, a+0+(k-1)*lda, 1, a+0+(l-1)*lda, 1 );
      }
    }
  }

  return;
}
//****************************************************************************80

int sgefa ( float a[], int lda, int n, int ipvt[] )

//****************************************************************************80
//
//  Purpose:
//
//    SGEFA factors a real general matrix.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].
//    On intput, the matrix to be factored.
//    On output, an upper triangular matrix and the multipliers used to obtain
//    it.  The factorization can be written A=L*U, where L is a product of
//    permutation and unit lower triangular matrices, and U is upper triangular.
//
//    Input, int LDA, the leading dimension of A.
//
//    Input, int N, the order of the matrix A.
//
//    Output, int IPVT[N], the pivot indices.
//
//    Output, int SGEFA, singularity indicator.
//    0, normal value.
//    K, if U(K,K) == 0.  This is not an error condition for this subroutine,
//    but it does indicate that SGESL or SGEDI will divide by zero if called.
//    Use RCOND in SGECO for a reliable indication of singularity.
//
{
  int info;
  int j;
  int k;
  int l;
  float t;
//
//  Gaussian elimination with partial pivoting.
//
  info = 0;

  for ( k = 1; k <= n-1; k++ )
  {
//
//  Find L = pivot index.
//
    l = isamax ( n-k+1, a+(k-1)+(k-1)*lda, 1 ) + k - 1;
    ipvt[k-1] = l;
//
//  Zero pivot implies this column already triangularized.
//
    if ( a[l-1+(k-1)*lda] == 0.0 )
    {
      info = k;
      continue;
    }
//
//  Interchange if necessary.
//
    if ( l != k )
    {
      t = a[l-1+(k-1)*lda];
      a[l-1+(k-1)*lda] = a[k-1+(k-1)*lda];
      a[k-1+(k-1)*lda] = t;
    }
//
//  Compute multipliers.
//
    t = -1.0 / a[k-1+(k-1)*lda];

    sscal ( n-k, t, a+k+(k-1)*lda, 1 );
//
//  Row elimination with column indexing.
//
    for ( j = k+1; j <= n; j++ )
    {
      t = a[l-1+(j-1)*lda];
      if ( l != k )
      {
        a[l-1+(j-1)*lda] = a[k-1+(j-1)*lda];
        a[k-1+(j-1)*lda] = t;
      }
      saxpy ( n-k, t, a+k+(k-1)*lda, 1, a+k+(j-1)*lda, 1 );
    }

  }

  ipvt[n-1] = n;

  if ( a[n-1+(n-1)*lda] == 0.0 )
  {
    info = n;
  }

  return info;
}
//****************************************************************************80

void sgesl ( float a[], int lda, int n, int ipvt[], float b[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SGESL solves a real general linear system A * X = B.
//
//  Discussion:
//
//    SGESL can solve either of the systems A * X = B or A' * X = B.
//
//    The system matrix must have been factored by SGECO or SGEFA.
//
//    A division by zero will occur if the input factor contains a
//    zero on the diagonal.  Technically this indicates singularity
//    but it is often caused by improper arguments or improper
//    setting of LDA.  It will not occur if the subroutines are
//    called correctly and if SGECO has set 0.0 < RCOND
//    or SGEFA has set INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float A[LDA*N], the output from SGECO or SGEFA.
//
//    Input, int LDA, the leading dimension of A.
//
//    Input, int N, the order of the matrix A.
//
//    Input, int IPVT[N], the pivot vector from SGECO or SGEFA.
//
//    Input/output, float B[N].
//    On input, the right hand side vector.
//    On output, the solution vector.
//
//    Input, int JOB.
//    0, solve A * X = B;
//    nonzero, solve A' * X = B.
//
{
  int k;
  int l;
  float t;
//
//  Solve A * X = B.
//
  if ( job == 0 )
  {
    for ( k = 1; k <= n-1; k++ )
    {
      l = ipvt[k-1];
      t = b[l-1];

      if ( l != k )
      {
        b[l-1] = b[k-1];
        b[k-1] = t;
      }

      saxpy ( n-k, t, a+k+(k-1)*lda, 1, b+k, 1 );

    }

    for ( k = n; 1 <= k; k-- )
    {
      b[k-1] = b[k-1] / a[k-1+(k-1)*lda];
      t = -b[k-1];
      saxpy ( k-1, t, a+0+(k-1)*lda, 1, b, 1 );
    }
  }
//
//  Solve A' * X = B.
//
  else
  {
    for ( k = 1; k <= n; k++ )
    {
      t = sdot ( k-1, a+0+(k-1)*lda, 1, b, 1 );
      b[k-1] = ( b[k-1] - t ) / a[k-1+(k-1)*lda];
    }

    for ( k = n-1; 1 <= k; k-- )
    {
      b[k-1] = b[k-1] + sdot ( n-k, a+k+(k-1)*lda, 1, b+k, 1 );
      l = ipvt[k-1];

      if ( l != k )
      {
        t = b[l-1];
        b[l-1] = b[k-1];
        b[k-1] = t;
      }
    }
  }
  return;
}
//****************************************************************************80

int sgtsl ( int n, float c[], float d[], float e[], float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SGTSL solves a general tridiagonal linear system.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, int N, the order of the tridiagonal matrix.
//
//    Input/output, float C[N], contains the subdiagonal of the
//    tridiagonal matrix in entries C(2:N).  On output, C is destroyed.
//
//    Input/output, float D[N].  On input, the diagonal of the
//    matrix.  On output, D is destroyed.
//
//    Input/output, float E[N], contains the superdiagonal of the
//    tridiagonal matrix in entries E(1:N-1).  On output E is destroyed.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
//    Output, int SGTSL, error flag.
//    0, normal value.
//    K, the K-th element of the diagonal becomes exactly zero.  The
//       subroutine returns if this error condition is detected.
//
{
  int info;
  int k;
  float t;

  info = 0;
  c[0] = d[0];

  if ( 2 <= n )
  {
    d[0] = e[0];
    e[0] = 0.0;
    e[n-1] = 0.0;

    for ( k = 1; k <= n - 1; k++ )
    {
//
//  Find the larger of the two rows.
//
      if ( r4_abs ( c[k-1] ) <= r4_abs ( c[k] ) )
      {
//
//  Interchange rows.
//
        t = c[k];
        c[k] = c[k-1];
        c[k-1] = t;

        t = d[k];
        d[k] = d[k-1];
        d[k-1] = t;

        t = e[k];
        e[k] = e[k-1];
        e[k-1] = t;

        t = b[k];
        b[k] = b[k-1];
        b[k-1] = t;
      }
//
//  Zero elements.
//
      if ( c[k-1] == 0.0 )
      {
        info = k;
        return info;
      }

      t = -c[k] / c[k-1];
      c[k] = d[k] + t * d[k-1];
      d[k] = e[k] + t * e[k-1];
      e[k] = 0.0;
      b[k] = b[k] + t * b[k-1];
    }
  }

  if ( c[n-1] == 0.0 )
  {
    info = n;
    return info;
  }
//
//  Back solve.
//
  b[n-1] = b[n-1] / c[n-1];

  if ( 1 < n )
  {
    b[n-2] = ( b[n-2] - d[n-2] * b[n-1] ) / c[n-2];

    for ( k = n-2; 1 <= k; k-- )
    {
      b[k-1] = ( b[k-1] - d[k-1] * b[k] - e[k-1] * b[k+1] ) / c[k-1];
    }

  }

  return info;
}
//****************************************************************************80

float spbco ( float abd[], int lda, int n, int m, float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPBCO factors a real symmetric positive definite banded matrix.
//
//  Discussion:
//
//    SPBCO also estimates the condition of the matrix.
//
//    If RCOND is not needed, SPBFA is slightly faster.
//
//    To solve A*X = B, follow SPBCO by SPBSL.
//
//    To compute inverse(A)*C, follow SPBCO by SPBSL.
//
//    To compute determinant(A), follow SPBCO by SPBDI.
//
//  Band storage:
//
//    If A is a symmetric positive definite band matrix, the following
//    program segment will set up the input.
//
//      m = (band width above diagonal)
//      do j = 1, n
//        i1 = max (1, j-m)
//        do i = i1, j
//          k = i-j+m+1
//          abd(k,j) = a(i,j)
//        }
//      }
//
//    This uses M + 1 rows of A, except for the M by M upper left triangle,
//    which is ignored.
//
//    For example, if the original matrix is
//
//      11 12 13  0  0  0
//      12 22 23 24  0  0
//      13 23 33 34 35  0
//       0 24 34 44 45 46
//       0  0 35 45 55 56
//       0  0  0 46 56 66
//
//    then N = 6, M = 2  and ABD should contain
//
//       *  * 13 24 35 46
//       * 12 23 34 45 56
//      11 22 33 44 55 66
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float ABD[LDA*N].  On input, the matrix to be
//    factored.  The columns of the upper triangle are stored in the columns
//    of ABD and the diagonals of the upper triangle are stored in the rows
//    of ABD.  On output, an upper triangular matrix R, stored in band form,
//    so that A = R'*R.  If INFO /= 0, the factorization is not complete.
//
//    Input, int LDA, the leading dimension of the array ABD.
//    M+1 <= LDA is required.
//
//    Input, int N, the order of the matrix.
//
//    Input, int M, the number of diagonals above the main diagonal.
//
//    Output, float Z[N], a work vector whose contents are usually
//    unimportant.  If A is singular to working precision, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//    If INFO /= 0, Z is unchanged.
//
//    Output, float SPBCO, an estimate of the reciprocal condition number
//    RCOND.  For the system A*X = B, relative perturbations in A and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float anorm;
  float ek;
  int i;
  int info;
  int j;
  int j2;
  int k;
  int l;
  int la;
  int lb;
  int lm;
  int mu;
  float rcond;
  float s;
  float sm;
  float t;
  float wk;
  float wkm;
  float ynorm;
//
//  Find the norm of A.
//
  for ( j = 1; j <= n; j++ )
  {
    l = i4_min ( j, m+1 );
    mu = i4_max ( m+2-j, 1 );
    z[j-1] = sasum ( l, abd+mu-1+(j-1)*lda, 1 );
    k = j - l;
    for ( i = mu; i <= m; i++ )
    {
      k = k + 1;
      z[k-1] = z[k-1] + r4_abs ( abd[i-1+(j-1)*lda] );
    }
  }
  anorm = 0.0;
  for ( i = 1; i <= n; i++ )
  {
    anorm = r4_max ( anorm, z[i-1] );
  }
//
//  Factor.
//
  info = spbfa ( abd, lda, n, m );

  if ( info != 0 )
  {
    rcond = 0.0;
    return rcond;
  }
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where A*Z = Y and A*Y = E.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of W where R'*W = E.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve R' * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  for ( k = 1; k <= n; k++ )
  {
    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( -z[k-1] );
    }

    if ( abd[m+(k-1)*lda] < r4_abs ( ek - z[k-1] ) )
    {
      s = abd[m+(k-1)*lda] / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }
    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );
    wk = wk / abd[m+(k-1)*lda];
    wkm = wkm / abd[m+(k-1)*lda];
    j2 = i4_min ( k+m, n );
    i = m + 1;

    if ( k+1 <= j2 )
    {
      for ( j = k+1; j <= j2; j++ )
      {
        i = i - 1;
        sm = sm + r4_abs ( z[j-1] + wkm * abd[i-1+(j-1)*lda] );
        z[j-1] = z[j-1] + wk * abd[i-1+(j-1)*lda];
        s = s + r4_abs ( z[j-1] );
      }

      if ( s < sm )
      {
        t = wkm - wk;
        wk = wkm;
        i = m + 1;

        for ( j = k+1; j <= j2; j++ )
        {
          i = i - 1;
          z[j-1] = z[j-1] + t * abd[i-1+(j-1)*lda];
        }
      }
    }
    z[k-1] = wk;
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve R * Y = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( abd[m+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = abd[m+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
    }
    z[k-1] = z[k-1] / abd[m+(k-1)*lda];
    lm = i4_min ( k-1, m );
    la = m + 1 - lm;
    lb = k - lm;
    t = -z[k-1];
    saxpy ( lm, t, abd+la-1+(k-1)*lda, 1, z+lb-1, 1 );
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = 1.0;
//
//  Solve R' * V = Y.
//
  for ( k = 1; k <= n; k++ )
  {
    lm = i4_min ( k-1, m );
    la = m + 1 - lm;
    lb = k - lm;

    z[k-1] = z[k-1] - sdot ( lm, abd+la-1+(k-1)*lda, 1, z+lb-1, 1 );

    if ( abd[m+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = abd[m+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
    z[k-1] = z[k-1] / abd[m+(k-1)*lda];
  }
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve R * Z = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( abd[m+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = abd[m+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
    z[k-1] = z[k-1] / abd[m+(k-1)*lda];
    lm = i4_min ( k-1, m );
    la = m + 1 - lm;
    lb = k - lm;
    t = -z[k-1];
    saxpy ( lm, t, abd+la-1+(k-1)*lda, 1, z+lb-1, 1 );
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void spbdi ( float abd[], int lda, int n, int m, float det[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPBDI computes the determinant of a matrix factored by SPBCO or SPBFA.
//
//  Discussion:
//
//    If the inverse is needed, use SPBSL N times.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float ABD[LDA*N], the output from SPBCO or SPBFA.
//
//    Input, int LDA, the leading dimension of the array ABD.
//
//    Input, int N, the order of the matrix.
//
//    Input, int M, the number of diagonals above the main diagonal.
//
//    Output, float DET[2], the determinant of the original
//    matrix in the form
//      determinant = DET[0] * 10.0**DET[1]
//    with 1.0D+00 <= DET[0] < 10.0D+00 or DET[0] == 0.0D+00.
//
{
  int i;
  float s;
//
//  Compute the determinant.
//
  det[0] = 1.0;
  det[1] = 0.0;
  s = 10.0;

  for ( i = 1; i <= n; i++ )
  {
    det[0] = det[0] * abd[m+(i-1)*lda] * abd[m+(i-1)*lda];

    if ( det[0] == 0.0 )
    {
      return;
    }

    while ( det[0] < 1.0 )
    {
      det[0] = det[0] * s;
      det[1] = det[1] - 1.0;
    }

    while ( s <= det[0] )
    {
      det[0] = det[0] / s;
      det[1] = det[1] + 1.0;
    }

  }

  return;
}
//****************************************************************************80

int spbfa ( float abd[], int lda, int n, int m )

//****************************************************************************80
//
//  Purpose:
//
//    SPBFA factors a symmetric positive definite matrix stored in band form.
//
//  Discussion:
//
//    SPBFA is usually called by SPBCO, but it can be called
//    directly with a saving in time if RCOND is not needed.
//
//    If A is a symmetric positive definite band matrix,
//    the following program segment will set up the input.
//
//      m = (band width above diagonal)
//      do j = 1, n
//        i1 = max ( 1, j-m )
//        do i = i1, j
//          k = i-j+m+1
//          abd(k,j) = a(i,j)
//        end do
//      end do
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    23 May 2005
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float ABD[LDA*N].  On input, the matrix to be
//    factored.  The columns of the upper triangle are stored in the columns
//    of ABD and the diagonals of the upper triangle are stored in the
//    rows of ABD.  On output, an upper triangular matrix R, stored in band
//    form, so that A = R' * R.
//
//    Input, int LDA, the leading dimension of the array ABD.
//    M+1 <= LDA is required.
//
//    Input, int N, the order of the matrix.
//
//    Input, int M, the number of diagonals above the main diagonal.
//
//    Output, int SPBFA, error indicator.
//    0, for normal return.
//    K, if the leading minor of order K is not positive definite.
//
{
  int ik;
  int info;
  int j;
  int jk;
  int k;
  int mu;
  float s;
  float t;

  for ( j = 1; j <= n; j++ )
  {
    s = 0.0;
    ik = m + 1;
    jk = i4_max ( j - m, 1 );
    mu = i4_max ( m + 2 - j, 1 );

    for ( k = mu; k <= m; k++ )
    {
      t = abd[k-1+(j-1)*lda] 
        - sdot ( k-mu, abd+ik-1+(jk-1)*lda, 1, abd+mu-1+(j-1)*lda, 1 );
      t = t / abd[m+(jk-1)*lda];
      abd[k-1+(j-1)*lda] = t;
      s = s + t * t;
      ik = ik - 1;
      jk = jk + 1;
    }
    s = abd[m+(j-1)*lda] - s;

    if ( s <= 0.0 )
    {
      info = j;
      return info;
    }
    abd[m+(j-1)*lda] = sqrt ( s );
  }
  info = 0;

  return info;
}
//****************************************************************************80

void spbsl ( float abd[], int lda, int n, int m, float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPBSL solves a real SPD band system factored by SPBCO or SPBFA.
//
//  Discussion:
//
//    The matrix is assumed to be a symmetric positive definite (SPD)
//    band matrix.
//
//    To compute inverse(A) * C  where C is a matrix with P columns:
//
//      call spbco ( abd, lda, n, rcond, z, info )
//
//      if ( rcond is too small .or. info /= 0) go to ...
//
//      do j = 1, p
//        call spbsl ( abd, lda, n, c(1,j) )
//      end do
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal.  Technically this indicates
//    singularity but it is usually caused by improper subroutine
//    arguments.  It will not occur if the subroutines are called
//    correctly and INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float ABD[LDA*N], the output from SPBCO or SPBFA.
//
//    Input, int LDA, the leading dimension of the array ABD.
//
//    Input, int N, the order of the matrix.
//
//    Input, int M, the number of diagonals above the main diagonal.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  int k;
  int la;
  int lb;
  int lm;
  float t;
//
//  Solve R'*Y = B.
//
  for ( k = 1; k <= n; k++ )
  {
    lm = i4_min ( k-1, m );
    la = m + 1 - lm;
    lb = k - lm;
    t = sdot ( lm, abd+la-1+(k-1)*lda, 1, b+lb-1, 1 );
    b[k-1] = ( b[k-1] - t ) / abd[m+(k-1)*lda];
  }
//
//  Solve R*X = Y.
//
  for ( k = n; 1 <= k; k-- )
  {
    lm = i4_min ( k-1, m );
    la = m + 1 - lm;
    lb = k - lm;
    b[k-1] = b[k-1] / abd[m+(k-1)*lda];
    t = -b[k-1];
    saxpy ( lm, t, abd+la-1+(k-1)*lda, 1, b+lb-1, 1 );
  }

  return;
}
//****************************************************************************80

float spoco ( float a[], int lda, int n, float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPOCO factors a real symmetric positive definite matrix and estimates its condition.
//
//  Discussion:
//
//    If RCOND is not needed, SPOFA is slightly faster.
//
//    To solve A*X = B, follow SPOCO by SPOSL.
//
//    To compute inverse(A)*C, follow SPOCO by SPOSL.
//
//    To compute determinant(A), follow SPOCO by SPODI.
//
//    To compute inverse(A), follow SPOCO by SPODI.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the symmetric
//    matrix to be factored.  Only the diagonal and upper triangle are used.
//    On output, an upper triangular matrix R so that A = R'*R where R'
//    is the transpose.  The strict lower triangle is unaltered.
//    If INFO /= 0, the factorization is not complete.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Output, float Z[N], a work vector whose contents are usually
//    unimportant.  If A is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//    If INFO /= 0, Z is unchanged.
//
//    Output, float SPOCO, an estimate of the reciprocal
//    condition of A.  For the system A*X = B, relative perturbations in
//    A and B of size EPSILON may cause relative perturbations in X of
//    size EPSILON/RCOND.  If RCOND is so small that the logical expression
//      1.0D+00 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float anorm;
  float ek;
  int i;
  int info;
  int j;
  int k;
  float rcond;
  float s;
  float sm;
  float t;
  float wk;
  float wkm;
  float ynorm;
//
//  Find norm of A using only upper half.
//
  for ( j = 1; j <= n; j++ )
  {
    z[j-1] = sasum ( j, a+0+(j-1)*lda, 1 );
    for ( i = 1; i <= j-1; i++ )
    {
      z[i-1] = z[i-1] + r4_abs ( a[i-1+(j-1)*lda] );
    }
  }

  anorm = 0.0;
  for ( i = 1; i <= n; i++ )
  {
    anorm = r4_max ( anorm, z[i-1] );
  }
//
//  Factor.
//
  info = spofa ( a, lda, n );

  if ( info != 0 )
  {
    rcond = 0.0;
    return rcond;
  }
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where A*Z = Y and A*Y = E.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of W where R'*W = E.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve R' * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  for ( k = 1; k <= n; k++ )
  {
    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( -z[k-1] );
    }

    if ( a[k-1+(k-1)*lda] < r4_abs ( ek - z[k-1] ) )
    {
      s = a[k-1+(k-1)*lda] / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }

    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );
    wk = wk / a[k-1+(k-1)*lda];
    wkm = wkm / a[k-1+(k-1)*lda];

    if ( k + 1 <= n )
    {
      for ( j = k+1; j <= n; j++ )
      {
        sm = sm + r4_abs ( z[j-1] + wkm * a[k-1+(j-1)*lda] );
        z[j-1] = z[j-1] + wk * a[k-1+(j-1)*lda];
        s = s + r4_abs ( z[j-1] );
      }

      if ( s < sm )
      {
        t = wkm - wk;
        wk = wkm;
        for ( j = k+1; j <= n; j++ )
        {
          z[j-1] = z[j-1] + t * a[k-1+(j-1)*lda];
        }
      }
    }
    z[k-1] = wk;
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve R * Y = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( a[k-1+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = a[k-1+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
    }
    z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
    t = -z[k-1];
    saxpy ( k-1, t, a+0+(k-1)*lda, 1, z, 1 );
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = 1.0;
//
//  Solve R' * V = Y.
//
  for ( k = 1; k <= n; k++ )
  {
    z[k-1] = z[k-1] - sdot ( k-1, a+0+(k-1)*lda, 1, z, 1 );

    if ( a[k-1+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = a[k-1+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
    z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
  }
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve R * Z = V.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( a[k-1+(k-1)*lda] < r4_abs ( z[k-1] ) )
    {
      s = a[k-1+(k-1)*lda] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
    z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
    t = -z[k-1];
    saxpy ( k-1, t, a+0+(k-1)*lda, 1, z, 1 );
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void spodi ( float a[], int lda, int n, float det[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SPODI computes the determinant and inverse of a certain matrix.
//
//  Discussion:
//
//    The matrix is real symmetric positive definite.
//    SPODI uses the factors computed by SPOCO, SPOFA or SQRDC.
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal and the inverse is requested.
//    It will not occur if the subroutines are called correctly
//    and if SPOCO or SPOFA has set INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the output A from
//    SPOCO or SPOFA, or the output X from SQRDC.  On output, if SPOCO or
//    SPOFA was used to factor A then SPODI produces the upper half of
//    inverse(A).  If SQRDC was used to decompose X then SPODI produces
//    the upper half of inverse(X'*X) where X' is the transpose.
//    Elements of A below the diagonal are unchanged.  If the units digit
//    of JOB is zero, A is unchanged.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix A.
//
//    Input, int JOB, specifies the task.
//    11, both determinant and inverse.
//    01, inverse only.
//    10, determinant only.
//
//    Output, float DET[2], the determinant of A or of X'*X
//    if requested.
//      determinant = DET[0] * 10.0**DET[1]
//    with 1.0D+00 <= DET[0] < 10.0D+00 or DET[0] == 0.0D+00.
//
{
  int i;
  int j;
  int k;
  float s;
  float t;
//
//  Compute the determinant.
//
  if ( job / 10 != 0 )
  {
    det[0] = 1.0;
    det[1] = 0.0;
    s = 10.0;

    for ( i = 1; i <= n; i++ )
    {
      det[0] = det[0] * a[i-1+(i-1)*lda] * a[i-1+(i-1)*lda];

      if ( det[0] == 0.0 )
      {
        break;
      }

      while ( det[0] < 1.0 )
      {
        det[0] = det[0] * s;
        det[1] = det[1] - 1.0;
      }

      while ( s <= det[0] )
      {
        det[0] = det[0] / s;
        det[1] = det[1] + 1.0;
      }
    }
  }
//
//  Compute inverse(R).
//
  if ( ( job % 10 ) != 0 )
  {
    for ( k = 1; k <= n; k++ )
    {
      a[k-1+(k-1)*lda] = 1.0 / a[k-1+(k-1)*lda];
      t = -a[k-1+(k-1)*lda];
      sscal ( k-1, t, a+0+(k-1)*lda, 1 );

      for ( j = k+1; j <= n; j++ )
      {
        t = a[k-1+(j-1)*lda];
        a[k-1+(j-1)*lda] = 0.0;
        saxpy ( k, t, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
      }
    }
//
//  Form inverse(R) * (inverse(R))'.
//
    for ( j = 1; j <= n; j++ )
    {
      for ( k = 1; k <= j-1; k++ )
      {
        t = a[k-1+(j-1)*lda];
        saxpy ( k, t, a+0+(j-1)*lda, 1, a+0+(k-1)*lda, 1 );
      }
      t = a[j-1+(j-1)*lda];
      sscal ( j, t, a+0+(j-1)*lda, 1 );
    }
  }

  return;
}
//****************************************************************************80

int spofa ( float a[], int lda, int n )

//****************************************************************************80
//
//  Purpose:
//
//    SPOFA factors a real symmetric positive definite matrix.
//
//  Discussion:
//
//    SPOFA is usually called by SPOCO, but it can be called
//    directly with a saving in time if RCOND is not needed.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the symmetric matrix
//    to be  factored.  Only the diagonal and upper triangle are used.
//    On output, an upper triangular matrix R so that A = R'*R
//    where R' is the transpose.  The strict lower triangle is unaltered.
//    If INFO /= 0, the factorization is not complete.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Output, int SPOFA, error flag.
//    0, for normal return.
//    K, signals an error condition.  The leading minor of order K is not
//    positive definite.
//
{
  int info;
  int j;
  int k;
  float s;
  float t;

  for ( j = 1; j <= n; j++ )
  {
    s = 0.0;

    for ( k = 1; k <= j-1; k++ )
    {
      t = a[k-1+(j-1)*lda] - sdot ( k-1, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
      t = t / a[k-1+(k-1)*lda];
      a[k-1+(j-1)*lda] = t;
      s = s + t * t;
    }

    s = a[j-1+(j-1)*lda] - s;

    if ( s <= 0.0 )
    {
      info = j;
      return info;
    }

    a[j-1+(j-1)*lda] = sqrt ( s );
  }

  info = 0;

  return info;
}
//****************************************************************************80

void sposl ( float a[], int lda, int n, float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPOSL solves a linear system factored by SPOCO or SPOFA.
//
//  Discussion:
//
//    To compute inverse(A) * C where C is a matrix with P columns:
//
//      call spoco ( a, lda, n, rcond, z, info )
//
//      if ( rcond is not too small .and. info == 0 ) then
//        do j = 1, p
//          call sposl ( a, lda, n, c(1,j) )
//        end do
//      end if
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal.  Technically this indicates
//    singularity but it is usually caused by improper subroutine
//    arguments.  It will not occur if the subroutines are called
//    correctly and INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float A[LDA*N], the output from SPOCO or SPOFA.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  int k;
  float t;
//
//  Solve R' * Y = B.
//
  for ( k = 1; k <= n; k++ )
  {
    t = sdot ( k-1, a+0+(k-1)*lda, 1, b, 1 );
    b[k-1] = ( b[k-1] - t ) / a[k-1+(k-1)*lda];
  }
//
//  Solve R * X = Y.
//
  for ( k = n; 1 <= k; k-- )
  {
    b[k-1] = b[k-1] / a[k-1+(k-1)*lda];
    t = -b[k-1];
    saxpy ( k-1, t, a+0+(k-1)*lda, 1, b, 1 );
  }

  return;
}
//****************************************************************************80

float sppco ( float ap[], int n, float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPPCO factors a real symmetric positive definite matrix in packed form.
//
//  Discussion:
//
//    SPPCO also estimates the condition of the matrix.
//
//    If RCOND is not needed, SPPFA is slightly faster.
//
//    To solve A*X = B, follow SPPCO by SPPSL.
//
//    To compute inverse(A)*C, follow SPPCO by SPPSL.
//
//    To compute determinant(A), follow SPPCO by SPPDI.
//
//    To compute inverse(A), follow SPPCO by SPPDI.
//
//  Packed storage:
//
//    The following program segment will pack the upper triangle of
//    a symmetric matrix.
//
//      k = 0
//      do j = 1, n
//        do i = 1, j
//          k = k + 1
//          ap[k-1] = a(i,j)
//        }
//      }
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[N*(N+1)/2].  On input, the packed
//    form of a symmetric matrix A.  The columns of the upper triangle are
//    stored sequentially in a one-dimensional array.  On output, an upper
//    triangular matrix R, stored in packed form, so that A = R'*R.
//    If INFO /= 0, the factorization is not complete.
//
//    Input, int N, the order of the matrix.
//
//    Output, float Z[N], a work vector whose contents are usually
//    unimportant.  If A is singular to working precision, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//    If INFO /= 0, Z is unchanged.
//
//    Output, float SPPCO, an estimate of the reciprocal condition number RCOND
//    of A.  For the system A*X = B, relative perturbations in A and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float anorm;
  float ek;
  int i;
  int ij;
  int info;
  int j;
  int j1;
  int k;
  int kj;
  int kk;
  float rcond;
  float s;
  float sm;
  float t;
  float wk;
  float wkm;
  float ynorm;
//
//  Find the norm of A.
//
  j1 = 1;
  for ( j = 1; j <= n; j++ )
  {
    z[j-1] = sasum ( j, ap+j1-1, 1 );
    ij = j1;
    j1 = j1 + j;
    for ( i = 1; i <= j-1; i++ )
    {
      z[i-1] = z[i-1] + r4_abs ( ap[ij-1] );
      ij = ij + 1;
    }
  }
  anorm = 0.0;
  for ( i = 1; i <= n; i++ )
  {
    anorm = r4_max ( anorm, z[i-1] );
  }
//
//  Factor.
//
  info = sppfa ( ap, n );

  if ( info != 0 )
  {
    rcond = 0.0;
    return rcond;
  }
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where A * Z = Y and A * Y = E.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of W where R'*W = E.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve R' * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  kk = 0;

  for ( k = 1; k <= n; k++ )
  {
    kk = kk + k;

    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( -z[k-1] );
    }

    if ( ap[kk-1] < r4_abs ( ek - z[k-1] ) )
    {
      s = ap[kk-1] / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }
    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );
    wk = wk / ap[kk-1];
    wkm = wkm / ap[kk-1];
    kj = kk + k;

    if ( k + 1 <= n )
    {
      for ( j = k + 1; j <= n; j++ )
      {
        sm = sm + r4_abs ( z[j-1] + wkm * ap[kj-1] );
        z[j-1] = z[j-1] + wk * ap[kj-1];
        s = s + r4_abs ( z[j-1] );
        kj = kj + j;
      }

      if ( s < sm )
      {
        t = wkm - wk;
        wk = wkm;
        kj = kk + k;

        for ( j = k+1; j <= n; j++ )
        {
          z[j-1] = z[j-1] + t * ap[kj-1];
          kj = kj + j;
        }
      }
    }
    z[k-1] = wk;
  }

  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve R * Y = W.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( ap[kk-1] < r4_abs ( z[k-1] ) )
    {
      s = ap[kk-1] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
    }
    z[k-1] = z[k-1] / ap[kk-1];
    kk = kk - k;
    t = -z[k-1];
    saxpy ( k-1, t, ap+kk, 1, z, 1 );
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = 1.0;
//
//  Solve R' * V = Y.
//
  for ( k = 1; k <= n; k++ )
  {
    z[k-1] = z[k-1] - sdot ( k-1, ap+kk, 1, z, 1 );
    kk = kk + k;

    if ( ap[kk-1] < r4_abs ( z[k-1] ) )
    {
      s = ap[kk-1] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }

    z[k-1] = z[k-1] / ap[kk-1];
  }
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve R * Z = V.
//
  for ( k = n; 1 <= k; k-- )
  {
    if ( ap[kk-1] < r4_abs ( z[k-1] ) )
    {
      s = ap[kk-1] / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }
    z[k-1] = z[k-1] / ap[kk-1];
    kk = kk - k;
    t = -z[k-1];
    saxpy ( k-1, t, ap+kk, 1, z, 1 );
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void sppdi ( float ap[], int n, float det[2], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SPPDI: determinant and inverse of a matrix factored by SPPCO or SPPFA.
//
//  Discussion:
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal and the inverse is requested.
//    It will not occur if the subroutines are called correctly
//    and if SPOCO or SPOFA has set INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[N*(N+1)/2].  On input, the output from
//    SPPCO or SPPFA.  On output, the upper triangular half of the
//    inverse, if requested.
//
//    Input, int N, the order of the matrix.
//
//    Output, float DET[2], the determinant of the original matrix
//    if requested.
//      determinant = DET[0] * 10.0**DET[1]
//    with  1.0D+00 <= DET[0] < 10.0D+00 or DET[0] == 0.0D+00.
//
//    Input, int JOB, job request.
//    11, both determinant and inverse.
//    01, inverse only.
//    10, determinant only.
//
{
  int i;
  int ii;
  int j;
  int j1;
  int jj;
  int k;
  int k1;
  int kj;
  int kk;
  float s;
  float t;
//
//  Compute the determinant.
//
  if ( job / 10 != 0 )
  {
    det[0] = 1.0;
    det[1] = 0.0;
    s = 10.0;
    ii = 0;

    for ( i = 1; i <= n; i++ )
    {
      ii = ii + i;

      det[0] = det[0] * ap[ii-1] * ap[ii-1];

      if ( det[0] == 0.0 )
      {
        break;
      }

      while ( det[0] < 1.0 )
      {
        det[0] = det[0] * s;
        det[1] = det[1] - 1.0;
      }

      while ( s <= det[0] )
      {
        det[0] = det[0] / s;
        det[1] = det[1] + 1.0;
      }
    }
  }
//
//  Compute inverse(R).
//
  if ( ( job % 10 ) != 0 )
  {
    kk = 0;

    for ( k = 1; k <= n; k++ )
    {
      k1 = kk + 1;
      kk = kk + k;
      ap[kk-1] = 1.0 / ap[kk-1];
      t = -ap[kk-1];
      sscal ( k-1, t, ap+k1-1, 1 );
      j1 = kk + 1;
      kj = kk + k;

      for ( j = k + 1; j <= n; j++ )
      {
        t = ap[kj-1];
        ap[kj-1] = 0.0;
        saxpy ( k, t, ap+k1-1, 1, ap+j1-1, 1 );
        j1 = j1 + j;
        kj = kj + j;
      }
    }
//
//  Form inverse(R) * (inverse(R))'.
//
    jj = 0;

    for ( j = 1; j <= n; j++ )
    {
      j1 = jj + 1;
      jj = jj + j;
      k1 = 1;
      kj = j1;

      for ( k = 1; k <= j-1; k++ )
      {
        t = ap[kj-1];
        saxpy ( k, t, ap+j1-1, 1, ap+k1-1, 1 );
        k1 = k1 + k;
        kj = kj + 1;
      }
      t = ap[jj-1];
      sscal ( j, t, ap+j1-1, 1 );
    }
  }
  return;
}
//****************************************************************************80

int sppfa ( float ap[], int n )

//****************************************************************************80
//
//  Purpose:
//
//    SPPFA factors a real symmetric positive definite matrix in packed form.
//
//  Discussion:
//
//    SPPFA is usually called by SPPCO, but it can be called
//    directly with a saving in time if RCOND is not needed.
//
//  Packed storage:
//
//    The following program segment will pack the upper
//    triangle of a symmetric matrix.
//
//      k = 0
//      do j = 1, n
//        do i = 1, j
//          k = k + 1
//          ap(k) = a(i,j)
//        end do
//      end do
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[N*(N+1)/2].  On input, the packed
//    form of a symmetric matrix A.  The columns of the upper triangle are
//    stored sequentially in a one-dimensional array.  On output, an upper
//    triangular matrix R, stored in packed form, so that A = R'*R.
//
//    Input, int N, the order of the matrix.
//
//    Output, int SPPFA, error flag.
//    0, for normal return.
//    K, if the leading minor of order K is not positive definite.
//
{
  int info;
  int j;
  int jj;
  int k;
  int kj;
  int kk;
  float s;
  float t;

  info = 0;
  jj = 0;

  for ( j = 1; j <= n; j++ )
  {
    s = 0.0;
    kj = jj;
    kk = 0;

    for ( k = 1; k <= j-1; k++ )
    {
      kj = kj + 1;
      t = ap[kj-1] - sdot ( k-1, ap+kk, 1, ap+jj, 1 );
      kk = kk + k;
      t = t / ap[kk-1];
      ap[kj-1] = t;
      s = s + t * t;
    }

    jj = jj + j;
    s = ap[jj-1] - s;

    if ( s <= 0.0 )
    {
      info = j;
      return info;
    }

    ap[jj-1] = sqrt ( s );

  }
  return info;
}
//****************************************************************************80

void sppsl ( float ap[], int n, float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPPSL solves a real symmetric positive definite system factored by SPPCO or SPPFA.
//
//  Discussion:
//
//    To compute inverse(A) * C where C is a matrix with P columns
//
//      call sppco ( ap, n, rcond, z, info )
//
//      if ( rcond is too small .or. info /= 0 ) then
//        exit
//      end if
//
//      do j = 1, p
//        call sppsl ( ap, n, c(1,j) )
//      end do
//
//    A division by zero will occur if the input factor contains
//    a zero on the diagonal.  Technically this indicates
//    singularity but it is usually caused by improper subroutine
//    arguments.  It will not occur if the subroutines are called
//    correctly and INFO == 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float AP[N*(N+1)/2], the output from SPPCO or SPPFA.
//
//    Input, int N, the order of the matrix.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  int k;
  int kk;
  float t;

  kk = 0;

  for ( k = 1; k <= n; k++ )
  {
    t = sdot ( k-1, ap+kk, 1, b, 1 );
    kk = kk + k;
    b[k-1] = ( b[k-1] - t ) / ap[kk-1];
  }

  for ( k = n; 1 <= k; k-- )
  {
    b[k-1] = b[k-1] / ap[kk-1];
    kk = kk - k;
    t = -b[k-1];
    saxpy ( k-1, t, ap+kk, 1, b, 1 );
  }

  return;
}
//****************************************************************************80

void sptsl ( int n, float d[], float e[], float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SPTSL solves a positive definite tridiagonal linear system.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, int N, the order of the matrix.
//
//    Input/output, float D[N], on input the diagonal of the
//    tridiagonal matrix.  On output, D is destroyed.
//
//    Input, float E[N], the offdiagonal of the tridiagonal matrix in
//    entries E(1:N-1).
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  int k;
  int kbm1;
  int ke;
  int kf;
  int kp1;
  int nm1d2;
  float t1;
  float t2;
//
//  Check for 1 x 1 case.
//
  if ( n == 1 )
  {
    b[0] = b[0] / d[0];
    return;
  }

  nm1d2 = ( n - 1 ) / 2;

  if ( 2 < n )
  {
    kbm1 = n - 1;
//
//  Zero top half of subdiagonal and bottom half of superdiagonal.
//
    for ( k = 1; k <= nm1d2; k++ )
    {
      t1 = e[k-1] / d[k-1];
      d[k] = d[k] - t1 * e[k-1];
      b[k] = b[k] - t1 * b[k-1];
      t2 = e[kbm1-1] / d[kbm1];
      d[kbm1-1] = d[kbm1-1] - t2 * e[kbm1-1];
      b[kbm1-1] = b[kbm1-1] - t2 * b[kbm1];
      kbm1 = kbm1 - 1;
    }
  }

  kp1 = nm1d2 + 1;
//
//  Clean up for possible 2 x 2 block at center.
//
  if ( ( n % 2 ) == 0 )
  {
    t1 = e[kp1-1] / d[kp1-1];
    d[kp1] = d[kp1] - t1 * e[kp1-1];
    b[kp1] = b[kp1] - t1 * b[kp1-1];
    kp1 = kp1 + 1;
  }
//
//  Back solve starting at the center, going towards the top and bottom.
//
  b[kp1-1] = b[kp1-1] / d[kp1-1];

  if ( 2 < n )
  {
    k = kp1 - 1;
    ke = kp1 + nm1d2 - 1;

    for ( kf = kp1; kf <= ke; kf++ )
    {
      b[k-1] = ( b[k-1] - e[k-1] * b[k] ) / d[k-1];
      b[kf] = ( b[kf] - e[kf-1] * b[kf-1] ) / d[kf];
      k = k - 1;
    }
  }

  if ( ( n % 2 ) == 0 )
  {
    b[0] = ( b[0] - e[0] * b[1] ) / d[0];
  }

  return;
}
//****************************************************************************80

void sqrdc ( float a[], int lda, int n, int p, float qraux[], int jpvt[], 
  float work[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SQRDC computes the QR factorization of a real rectangular matrix.
//
//  Discussion:
//
//    SQRDC uses Householder transformations.
//
//    Column pivoting based on the 2-norms of the reduced columns may be
//    performed at the user's option.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A(LDA,P).  On input, the N by P matrix
//    whose decomposition is to be computed.  On output, A contains in
//    its upper triangle the upper triangular matrix R of the QR
//    factorization.  Below its diagonal A contains information from
//    which the orthogonal part of the decomposition can be recovered.
//    Note that if pivoting has been requested, the decomposition is not that
//    of the original matrix A but that of A with its columns permuted
//    as described by JPVT.
//
//    Input, int LDA, the leading dimension of the array A.  LDA must
//    be at least N.
//
//    Input, int N, the number of rows of the matrix A.
//
//    Input, int P, the number of columns of the matrix A.
//
//    Output, float QRAUX[P], contains further information required
//    to recover the orthogonal part of the decomposition.
//
//    Input/output, integer JPVT[P].  On input, JPVT contains integers that
//    control the selection of the pivot columns.  The K-th column A(*,K) of A
//    is placed in one of three classes according to the value of JPVT(K).
//      > 0, then A(K) is an initial column.
//      = 0, then A(K) is a free column.
//      < 0, then A(K) is a final column.
//    Before the decomposition is computed, initial columns are moved to
//    the beginning of the array A and final columns to the end.  Both
//    initial and final columns are frozen in place during the computation
//    and only free columns are moved.  At the K-th stage of the
//    reduction, if A(*,K) is occupied by a free column it is interchanged
//    with the free column of largest reduced norm.  JPVT is not referenced
//    if JOB == 0.  On output, JPVT(K) contains the index of the column of the
//    original matrix that has been interchanged into the K-th column, if
//    pivoting was requested.
//
//    Workspace, float WORK[P].  WORK is not referenced if JOB == 0.
//
//    Input, int JOB, initiates column pivoting.
//    0, no pivoting is done.
//    nonzero, pivoting is done.
//
{
  int j;
  int jp;
  int l;
  int lup;
  int maxj;
  float maxnrm;
  float nrmxl;
  int pl;
  int pu;
  bool swapj;
  float t;
  float tt;

  pl = 1;
  pu = 0;
//
//  If pivoting is requested, rearrange the columns.
//
  if ( job != 0 )
  {
    for ( j = 1; j <= p; j++ )
    {
      swapj = ( 0 < jpvt[j-1] );

      if ( jpvt[j-1] < 0 )
      {
        jpvt[j-1] = -j;
      }
      else
      {
        jpvt[j-1] = j;
      }

      if ( swapj )
      {
        if ( j != pl )
        {
          sswap ( n, a+0+(pl-1)*lda, 1, a+0+(j-1), 1 );
        }
        jpvt[j-1] = jpvt[pl-1];
        jpvt[pl-1] = j;
        pl = pl + 1;
      }
    }
    pu = p;

    for ( j = p; 1 <= j; j-- )
    {
      if ( jpvt[j-1] < 0 )
      {
        jpvt[j-1] = -jpvt[j-1];

        if ( j != pu )
        {
          sswap ( n, a+0+(pu-1)*lda, 1, a+0+(j-1)*lda, 1 );
          jp = jpvt[pu-1];
          jpvt[pu-1] = jpvt[j-1];
          jpvt[j-1] = jp;
        }
        pu = pu - 1;
      }
    }
  }
//
//  Compute the norms of the free columns.
//
  for ( j = pl; j <= pu; j++ )
  {
    qraux[j-1] = snrm2 ( n, a+0+(j-1)*lda, 1 );
  }

  for ( j = pl; j <= pu; j++ )
  {
    work[j-1] = qraux[j-1];
  }
//
//  Perform the Householder reduction of A.
//
  lup = i4_min ( n, p );

  for ( l = 1; l <= lup; l++ )
  {
//
//  Bring the column of largest norm into the pivot position.
//
    if ( pl <= l && l < pu )
    {
      maxnrm = 0.0;
      maxj = l;
      for ( j = l; j <= pu; j++ )
      {
        if ( maxnrm < qraux[j-1] )
        {
          maxnrm = qraux[j-1];
          maxj = j;
        }
      }

      if ( maxj != l )
      {
        sswap ( n, a+0+(l-1)*lda, 1, a+0+(maxj-1)*lda, 1 );
        qraux[maxj-1] = qraux[l-1];
        work[maxj-1] = work[l-1];
        jp = jpvt[maxj-1];
        jpvt[maxj-1] = jpvt[l-1];
        jpvt[l-1] = jp;
      }
    }
//
//  Compute the Householder transformation for column L.
//
    qraux[l-1] = 0.0;

    if ( l != n )
    {
      nrmxl = snrm2 ( n-l+1, a+l-1+(l-1)*lda, 1 );

      if ( nrmxl != 0.0 )
      {
        if ( a[l-1+(l-1)*lda] != 0.0 )
        {
          nrmxl = nrmxl * r4_sign ( a[l-1+(l-1)*lda] );
        }

        sscal ( n-l+1, 1.0 / nrmxl, a+l-1+(l-1)*lda, 1 );
        a[l-1+(l-1)*lda] = 1.0 + a[l-1+(l-1)*lda];
//
//  Apply the transformation to the remaining columns, updating the norms.
//
        for ( j = l + 1; j <= p; j++ )
        {
          t = -sdot ( n-l+1, a+l-1+(l-1)*lda, 1, a+l-1+(j-1)*lda, 1 ) 
            / a[l-1+(l-1)*lda];
          saxpy ( n-l+1, t, a+l-1+(l-1)*lda, 1, a+l-1+(j-1)*lda, 1 );

          if ( pl <= j && j <= pu )
          {
            if ( qraux[j-1] != 0.0 )
            {
              tt = 1.0 - pow ( r4_abs ( a[l-1+(j-1)*lda] ) / qraux[j-1], 2 );
              tt = r4_max ( tt, 0.0 );
              t = tt;
              tt = 1.0 + 0.05 * tt * pow ( qraux[j-1] / work[j-1], 2 );

              if ( tt != 1.0 )
              {
                qraux[j-1] = qraux[j-1] * sqrt ( t );
              }
              else
              {
                qraux[j-1] = snrm2 ( n-l, a+l+(j-1)*lda, 1 );
                work[j-1] = qraux[j-1];
              }
            }
          }
        }
//
//  Save the transformation.
//
        qraux[l-1] = a[l-1+(l-1)*lda];
        a[l-1+(l-1)*lda] = -nrmxl;
      }
    }
  }
  return;
}
//****************************************************************************80

int sqrsl ( float a[], int lda, int n, int k, float qraux[], float y[], 
  float qy[], float qty[], float b[], float rsd[], float ab[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SQRSL computes transformations, projections, and least squares solutions.
//
//  Discussion:
//
//    SQRSL requires the output of SQRDC.
//
//    For K <= min(N,P), let AK be the matrix
//
//      AK = ( A(JPVT[0]), A(JPVT(2)), ..., A(JPVT(K)) )
//
//    formed from columns JPVT[0], ..., JPVT(K) of the original
//    N by P matrix A that was input to SQRDC.  If no pivoting was
//    done, AK consists of the first K columns of A in their
//    original order.  SQRDC produces a factored orthogonal matrix Q
//    and an upper triangular matrix R such that
//
//      AK = Q * (R)
//               (0)
//
//    This information is contained in coded form in the arrays
//    A and QRAUX.
//
//    The parameters QY, QTY, B, RSD, and AB are not referenced
//    if their computation is not requested and in this case
//    can be replaced by dummy variables in the calling program.
//    To save storage, the user may in some cases use the same
//    array for different parameters in the calling sequence.  A
//    frequently occuring example is when one wishes to compute
//    any of B, RSD, or AB and does not need Y or QTY.  In this
//    case one may identify Y, QTY, and one of B, RSD, or AB, while
//    providing separate arrays for anything else that is to be
//    computed.
//
//    Thus the calling sequence
//
//      sqrsl ( a, lda, n, k, qraux, y, dum, y, b, y, dum, 110, info )
//
//    will result in the computation of B and RSD, with RSD
//    overwriting Y.  More generally, each item in the following
//    list contains groups of permissible identifications for
//    a single calling sequence.
//
//      1. (Y,QTY,B) (RSD) (AB) (QY)
//
//      2. (Y,QTY,RSD) (B) (AB) (QY)
//
//      3. (Y,QTY,AB) (B) (RSD) (QY)
//
//      4. (Y,QY) (QTY,B) (RSD) (AB)
//
//      5. (Y,QY) (QTY,RSD) (B) (AB)
//
//      6. (Y,QY) (QTY,AB) (B) (RSD)
//
//    In any group the value returned in the array allocated to
//    the group corresponds to the last member of the group.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float A[LDA*P], contains the output of SQRDC.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the number of rows of the matrix AK.  It must
//    have the same value as N in SQRDC.
//
//    Input, int K, the number of columns of the matrix AK.  K
//    must not be greater than min(N,P), where P is the same as in the
//    calling sequence to SQRDC.
//
//    Input, float QRAUX[P], the auxiliary output from SQRDC.
//
//    Input, float Y[N], a vector to be manipulated by SQRSL.
//
//    Output, float QY[N], contains Q * Y, if requested.
//
//    Output, float QTY[N], contains Q' * Y, if requested.
//
//    Output, float B[K], the solution of the least squares problem
//      minimize norm2 ( Y - AK * B),
//    if its computation has been requested.  Note that if pivoting was
//    requested in SQRDC, the J-th component of B will be associated with
//    column JPVT(J) of the original matrix A that was input into SQRDC.
//
//    Output, float RSD[N], the least squares residual Y - AK * B,
//    if its computation has been requested.  RSD is also the orthogonal
//    projection of Y onto the orthogonal complement of the column space
//    of AK.
//
//    Output, float AB[N], the least squares approximation Ak * B,
//    if its computation has been requested.  AB is also the orthogonal
//    projection of Y onto the column space of A.
//
//    Input, integer JOB, specifies what is to be computed.  JOB has
//    the decimal expansion ABCDE, with the following meaning:
//
//      if A != 0, compute QY.
//      if B != 0, compute QTY.
//      if C != 0, compute QTY and B.
//      if D != 0, compute QTY and RSD.
//      if E != 0, compute QTY and AB.
//
//    Note that a request to compute B, RSD, or AB automatically triggers
//    the computation of QTY, for which an array must be provided in the
//    calling sequence.
//
//    Output, int SQRSL, is zero unless the computation of B has
//    been requested and R is exactly singular.  In this case, INFO is the
//    index of the first zero diagonal element of R, and B is left unaltered.
//
{
  bool cab;
  bool cb;
  bool cqty;
  bool cqy;
  bool cr;
  int i;
  int info;
  int j;
  int jj;
  int ju;
  float t;
  float temp;
//
//  set info flag.
//
  info = 0;
//
//  Determine what is to be computed.
//
  cqy =  (   job / 10000          != 0 );
  cqty = ( ( job %  10000 )       != 0 );
  cb =   ( ( job %   1000 ) / 100 != 0 );
  cr =   ( ( job %    100 ) /  10 != 0 );
  cab =  ( ( job %     10 )       != 0 );

  ju = i4_min ( k, n-1 );
//
//  Special action when N = 1.
//
  if ( ju == 0 )
  {
    if ( cqy )
    {
      qy[0] = y[0];
    }

    if ( cqty )
    {
      qty[0] = y[0];
    }

    if ( cab )
    {
      ab[0] = y[0];
    }

    if ( cb )
    {
      if ( a[0+0*lda] == 0.0 )
      {
        info = 1;
      }
      else
      {
        b[0] = y[0] / a[0+0*lda];
      }
    }

    if ( cr )
    {
      rsd[0] = 0.0;
    }
    return info;
  }
//
//  Set up to compute QY or QTY.
//
  if ( cqy )
  {
    for ( i = 1; i <= n; i++ )
    {
      qy[i-1] = y[i-1];
    }
  }

  if ( cqty )
  {
    for ( i = 1; i <= n; i++ )
    {
      qty[i-1] = y[i-1];
    }
  }
//
//  Compute QY.
//
  if ( cqy )
  {
    for ( jj = 1; jj <= ju; jj++ )
    {
      j = ju - jj + 1;

      if ( qraux[j-1] != 0.0 )
      {
        temp = a[j-1+(j-1)*lda];
        a[j-1+(j-1)*lda] = qraux[j-1];
        t = -sdot ( n-j+1, a+j-1+(j-1)*lda, 1, qy+j-1, 1 ) / a[j-1+(j-1)*lda];
        saxpy ( n-j+1, t, a+j-1+(j-1)*lda, 1, qy+j-1, 1 );
        a[j-1+(j-1)*lda] = temp;
      }
    }
  }
//
//  Compute Q'*Y.
//
  if ( cqty )
  {
    for ( j = 1; j <= ju; j++ )
    {
      if ( qraux[j-1] != 0.0 )
      {
        temp = a[j-1+(j-1)*lda];
        a[j-1+(j-1)*lda] = qraux[j-1];
        t = -sdot ( n-j+1, a+j-1+(j-1)*lda, 1, qty+j-1, 1 ) / a[j-1+(j-1)*lda];
        saxpy ( n-j+1, t, a+j-1+(j-1)*lda, 1, qty+j-1, 1 );
        a[j-1+(j-1)*lda] = temp;
      }
    }
  }
//
//  Set up to compute B, RSD, or AB.
//
  if ( cb )
  {
    for ( i = 1; i <= k; i++ )
    {
      b[i-1] = qty[i-1];
    }
  }

  if ( cab )
  {
    for ( i = 1; i <= k; i++ )
    {
      ab[i-1] = qty[i-1];
    }
  }

  if ( cr && k < n )
  {
    for ( i = k+1; i <= n; i++ )
    {
      rsd[i-1] = qty[i-1];
    }
  }

  if ( cab && k+1 <= n )
  {
    for ( i = k+1; i <= n; i++ )
    {
      ab[i-1] = 0.0;
    }
  }

  if ( cr )
  {
    for ( i = 1; i <= k; i++ )
    {
      rsd[i-1] = 0.0;
    }
  }
//
//  Compute B.
//
  if ( cb )
  {
    for ( jj = 1; jj <= k; jj++ )
    {
      j = k - jj + 1;

      if ( a[j-1+(j-1)*lda] == 0.0 )
      {
        info = j;
        break;
      }

      b[j-1] = b[j-1] / a[j-1+(j-1)*lda];

      if ( j != 1 )
      {
        t = -b[j-1];
        saxpy ( j-1, t, a+0+(j-1)*lda, 1, b, 1 );
      }
    }
  }
//
//  Compute RSD or AB as required.
//
  if ( cr || cab )
  {
    for ( jj = 1; jj <= ju; jj++ )
    {
      j = ju - jj + 1;

      if ( qraux[j-1] != 0.0 )
      {
        temp = a[j-1+(j-1)*lda];
        a[j-1+(j-1)*lda] = qraux[j-1];

        if ( cr )
        {
          t = -sdot ( n-j+1, a+j-1+(j-1)*lda, 1, rsd+j-1, 1 ) 
            / a[j-1+(j-1)*lda];
          saxpy ( n-j+1, t, a+j-1+(j-1)*lda, 1, rsd+j-1, 1 );
        }

        if ( cab )
        {
          t = -sdot ( n-j+1, a+j-1+(j-1)*lda, 1, ab+j-1, 1 ) 
            / a[j-1+(j-1)*lda];
          saxpy ( n-j+1, t, a+j-1+(j-1)*lda, 1, ab+j-1, 1 );
        }
        a[j-1+(j-1)*lda] = temp;
      }
    }
  }

  return info;
}
//****************************************************************************80

float ssico ( float a[], int lda, int n, int kpvt[], float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSICO factors a real symmetric matrix and estimates its condition.
//
//  Discussion:
//
//    If RCOND is not needed, SSIFA is slightly faster.
//
//    To solve A * X = B, follow SSICO by SSISL.
//
//    To compute inverse(A)*C, follow SSICO by SSISL.
//
//    To compute inverse(A), follow SSICO by SSIDI.
//
//    To compute determinant(A), follow SSICO by SSIDI.
//
//    To compute inertia(A), follow SSICO by SSIDI.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the symmetric
//    matrix to be factored.  Only the diagonal and upper triangle are used.
//    On output, a block diagonal matrix and the multipliers which
//    were used to obtain it.  The factorization can be written A = U*D*U'
//    where U is a product of permutation and unit upper triangular
//    matrices, U' is the transpose of U, and D is block diagonal
//    with 1 by 1 and 2 by 2 blocks.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Output, int KPVT[N], pivot indices.
//
//    Output, float Z[N], a work vector whose contents are usually
//    unimportant.  If A is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//
//    Output, float SSICO, an estimate of the reciprocal condition number RCOND
//    of A.  For the system A*X = B, relative perturbations in A and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float ak;
  float akm1;
  float anorm;
  float bk;
  float bkm1;
  float denom;
  float ek;
  int i;
  int info;
  int j;
  int k;
  int kp;
  int kps;
  int ks;
  float rcond;
  float s;
  float t;
  float ynorm;
//
//  Find the norm of A, using only entries in the upper half of the matrix.
//
  for ( j = 1; j <= n; j++ )
  {
    z[j-1] = sasum ( j, a+0+(j-1)*lda, 1 );
    for ( i = 1; i <= j-1; i++ )
    {
      z[i-1] = z[i-1] + r4_abs ( a[i-1+(j-1)*lda] );
    }
  }

  anorm = 0.0;
  for ( i = 1; i <= n; i++ )
  {
    anorm = r4_max ( anorm, z[i-1] );
  }
//
//  Factor.
//
  info = ssifa ( a, lda, n, kpvt );
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where A*Z = Y and A*Y = E.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of W where U*D*W = E.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve U * D * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }
  k = n;

  while ( k != 0 )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }
    kp = abs ( kpvt[k-1] );
    kps = k + 1 - ks;

    if ( kp != kps )
    {
      t = z[kps-1];
      z[kps-1] = z[kp-1];
      z[kp-1] = t;
    }

    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( z[k-1] );
    }

    z[k-1] = z[k-1] + ek;
    saxpy ( k-ks, z[k-2], a+0+(k-1)*lda, 1, z, 1 );

    if ( ks != 1 )
    {
      if ( z[k-2] != 0.0 )
      {
        ek = ek * r4_sign ( z[k-2] );
      }
      z[k-2] = z[k-2] + ek;
      saxpy ( k-ks, z[k-2], a+0+(k-2)*lda, 1, z, 1 );
    }

    if ( ks != 2 )
    {
      if ( r4_abs ( a[k-1+(k-1)*lda] ) < r4_abs ( z[k-1] ) )
      {
        s = r4_abs ( a[k-1+(k-1)*lda] ) / r4_abs ( z[k-1] );
        for ( i = 1; i <= n; i++ )
        {
          z[i-1] = s * z[i-1];
        }
        ek = s * ek;
      }

      if ( a[k-1+(k-1)*lda] != 0.0 )
      {
        z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
      }
      else
      {
        z[k-1] = 1.0;
      }
    }
    else
    {
      ak = a[k-1+(k-1)*lda] / a[k-2+(k-1)*lda];
      akm1 = a[k-2+(k-2)*lda] / a[k-2+(k-1)*lda];
      bk = z[k-1] / a[k-2+(k-1)*lda];
      bkm1 = z[k-2] / a[k-2+(k-1)*lda];
      denom = ak * akm1 - 1.0;
      z[k-1] = ( akm1 * bk - bkm1 ) / denom;
      z[k-2] = ( ak * bkm1 - bk ) / denom;
    }
    k = k - ks;
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve U' * Y = W.
//
  k = 1;

  while ( k <= n )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != 1 )
    {
      z[k-1] = z[k-1] + sdot ( k-1, a+0+(k-1)*lda, 1, z, 1 );

      if ( ks == 2 )
      {
        z[k] = z[k] + sdot ( k-1, a+0+k*lda, 1, z, 1 );
      }

      kp = abs ( kpvt[k-1] );

      if ( kp != k )
      {
        t = z[k-1];
        z[k-1] = z[kp-1];
        z[kp-1] = t;
      }
    }
    k = k + ks;
  }
  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
  ynorm = 1.0;
//
//  Solve U * D * V = Y.
//
  k = n;

  while ( k != 0 )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != ks )
    {
      kp = abs ( kpvt[k-1] );
      kps = k + 1 - ks;

      if ( kp != kps )
      {
        t = z[kps-1];
        z[kps-1] = z[kp-1];
        z[kp-1] = t;
      }

      saxpy ( k-ks, z[k-1], a+0+(k-1)*lda, 1, z, 1 );

      if ( ks == 2 )
      {
        saxpy ( k-ks, z[k-2], a+0+(k-2)*lda, 1, z, 1 );
      }
    }

    if ( ks != 2 )
    {
      if ( r4_abs ( a[k-1+(k-1)*lda] ) < r4_abs ( z[k-1] ) )
      {
        s = r4_abs ( a[k-1+(k-1)*lda] ) / r4_abs ( z[k-1] );
        for ( i = 1; i <= n; i++ )
        {
          z[i-1] = s * z[i-1];
        }
        ynorm = s * ynorm;
      }

      if ( a[k-1+(k-1)*lda] != 0.0 )
      {
        z[k-1] = z[k-1] / a[k-1+(k-1)*lda];
      }
      else
      {
        z[k-1] = 1.0;
      }
    }
    else
    {
      ak = a[k-1+(k-1)*lda] / a[k-2+(k-1)*lda];
      akm1 = a[k-2+(k-2)*lda] / a[k-2+(k-1)*lda];
      bk = z[k-1] / a[k-2+(k-1)*lda];
      bkm1 = z[k-2] / a[k-2+(k-1)*lda];
      denom = ak * akm1 - 1.0;
      z[k-1] = ( akm1 * bk - bkm1 ) / denom;
      z[k-2] = ( ak * bkm1 - bk ) / denom;
    }
    k = k - ks;
  }

  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve U' * Z = V.
//
  k = 1;

  while ( k <= n )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != 1 )
    {
      z[k-1] = z[k-1] + sdot ( k-1, a+0+(k-1)*lda, 1, z, 1 );
      if ( ks == 2 )
      {
        z[k] = z[k] + sdot ( k-1, a+0+k*lda, 1, z, 1 );
      }
      kp = abs ( kpvt[k-1] );

      if ( kp != k )
      {
        t = z[k-1];
        z[k-1] = z[kp-1];
        z[kp-1] = t;
      }
    }
    k = k + ks;
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void ssidi ( float a[], int lda, int n, int kpvt[], float det[2], 
  int inert[3], float work[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SSIDI computes the determinant, inertia and inverse of a real symmetric matrix.
//
//  Discussion:
//
//    SSIDI uses the factors from SSIFA.
//
//    A division by zero may occur if the inverse is requested
//    and SSICO has set RCOND == 0.0D+00 or SSIFA has set INFO /= 0.
//
//    Variables not requested by JOB are not used.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A(LDA,N).  On input, the output from SSIFA.
//    On output, the upper triangle of the inverse of the original matrix,
//    if requested.  The strict lower triangle is never referenced.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Input, int KPVT[N], the pivot vector from SSIFA.
//
//    Output, float DET[2], the determinant of the original matrix,
//    if requested.
//      determinant = DET[0] * 10.0**DET[1]
//    with 1.0D+00 <= abs ( DET[0] ) < 10.0D+00 or DET[0] = 0.0.
//
//    Output, int INERT(3), the inertia of the original matrix,
//    if requested.
//    INERT(1) = number of positive eigenvalues.
//    INERT(2) = number of negative eigenvalues.
//    INERT(3) = number of zero eigenvalues.
//
//    Workspace, float WORK[N].
//
//    Input, int JOB, specifies the tasks.
//    JOB has the decimal expansion ABC where
//    If C /= 0, the inverse is computed,
//    If B /= 0, the determinant is computed,
//    If A /= 0, the inertia is computed.
//    For example, JOB = 111 gives all three.
//
{
  float ak;
  float akkp1;
  float akp1;
  float d;
  bool dodet;
  bool doert;
  bool doinv;
  int j;
  int jb;
  int k;
  int ks;
  int kstep;
  float t;
  float temp;

  doinv = ( job %   10 )       != 0;
  dodet = ( job %  100 ) /  10 != 0;
  doert = ( job % 1000 ) / 100 != 0;

  if ( dodet || doert )
  {
    if ( doert )
    {
      inert[0] = 0;
      inert[1] = 0;
      inert[2] = 0;
    }

    if ( dodet )
    {
      det[0] = 1.0;
      det[1] = 0.0;
    }

    t = 0.0;

    for ( k = 1; k <= n; k++ )
    {
      d = a[k-1+(k-1)*lda];
//
//  2 by 2 block.
//
//  use det (d  s)  =  (d/t * c - t) * t,  t = abs ( s )
//          (s  c)
//  to avoid underflow/overflow troubles.
//
//  Take two passes through scaling.  Use T for flag.
//
      if ( kpvt[k-1] <= 0 )
      {
        if ( t == 0.0 )
        {
          t = r4_abs ( a[k-1+k*lda] );
          d = ( d / t ) * a[k+k*lda] - t;
        }
        else
        {
          d = t;
          t = 0.0;
        }
      }

      if ( doert )
      {
        if ( 0.0 < d )
        {
          inert[0] = inert[0] + 1;
        }
        else if ( d < 0.0 )
        {
          inert[1] = inert[1] + 1;
        }
        else if ( d == 0.0 )
        {
          inert[2] = inert[2] + 1;
        }
      }

      if ( dodet )
      {
        det[0] = det[0] * d;

        if ( det[0] != 0.0 )
        {
          while ( r4_abs ( det[0] ) < 1.0 )
          {
            det[0] = det[0] * 10.0;
            det[1] = det[1] - 1.0;
          }

          while ( 10.0 <= r4_abs ( det[0] ) )
          {
            det[0] = det[0] / 10.0;
            det[1] = det[1] + 1.0;
          }
        }
      }
    }
  }
//
//  Compute inverse(A).
//
  if ( doinv )
  {
    k = 1;

    while ( k <= n )
    {
      if ( 0 <= kpvt[k-1] )
      {
//
//  1 by 1.
//
        a[k-1+(k-1)*lda] = 1.0 / a[k-1+(k-1)*lda];

        if ( 2 <= k )
        {
          scopy ( k-1, a+0+(k-1)*lda, 1, work, 1 );

          for ( j = 1; j <= k-1; j++ )
          {
            a[j-1+(k-1)*lda] = sdot ( j, a+0+(j-1)*lda, 1, work, 1 );
            saxpy ( j-1, work[j-1], a+0+(j-1)*lda, 1, a+0+(k-1)*lda, 1 );
          }
          a[k-1+(k-1)*lda] = a[k-1+(k-1)*lda] 
            + sdot ( k-1, work, 1, a+0+(k-1)*lda, 1 );
        }
        kstep = 1;
      }
//
//  2 by 2.
//
      else
      {
        t = r4_abs ( a[k-1+k*lda] );
        ak = a[k-1+(k-1)*lda] / t;
        akp1 = a[k+k*lda] / t;
        akkp1 = a[k-1+k*lda] / t;
        d = t * ( ak * akp1 - 1.0 );
        a[k-1+(k-1)*lda] = akp1 / d;
        a[k+k*lda] = ak / d;
        a[k-1+k*lda] = -akkp1 / d;

        if ( 2 <= k )
        {
          scopy ( k-1, a+0+k*lda, 1, work, 1 );

          for ( j = 1; j <= k-1; j++ )
          {
            a[j-1+k*lda] = sdot ( j, a+0+(j-1)*lda, 1, work, 1 );
            saxpy ( j-1, work[j-1], a+0+(j-1)*lda, 1, a+0+k*lda, 1 );
          }
          a[k+k*lda] = a[k+k*lda] + sdot ( k-1, work, 1, a+0+k*lda, 1 );
          a[k-1+k*lda] = a[k-1+k*lda] 
            + sdot ( k-1, a+0+(k-1)*lda, 1, a+0+k*lda, 1 );
          scopy ( k-1, a+0+(k-1)*lda, 1, work, 1 );

          for ( j = 1; j <= k-1; j++ )
          {
            a[j-1+(k-1)*lda] = sdot ( j, a+0+(j-1)*lda, 1, work, 1 );
            saxpy ( j-1, work[j-1], a+0+(j-1)*lda, 1, a+0+(k-1)*lda, 1 );
          }
          a[k-1+(k-1)*lda] = a[k-1+(k-1)*lda] 
            + sdot ( k-1, work, 1, a+0+(k-1)*lda, 1 );
        }
        kstep = 2;
      }
//
//  Swap.
//
      ks = abs ( kpvt[k-1] );

      if ( ks != k )
      {
        sswap ( ks, a+0+(ks-1)*lda, 1, a+0+(k-1)*lda, 1 );

        for ( jb = ks; jb <= k; jb++ )
        {
          j = k + ks - jb;
          temp = a[j-1+(k-1)*lda];
          a[j-1+(k-1)*lda] = a[ks-1+(j-1)*lda];
          a[ks-1+(j-1)*lda] = temp;
        }

        if ( kstep != 1 )
        {
          temp = a[ks-1+k*lda];
          a[ks-1+k*lda] = a[k-1+k*lda];
          a[k-1+k*lda] = temp;
        }
      }
      k = k + kstep;
    }
  }
  return;
}
//****************************************************************************80

int ssifa ( float a[], int lda, int n, int kpvt[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSIFA factors a real symmetric matrix.
//
//  Discussion:
//
//    To solve A*X = B, follow SSIFA by SSISL.
//
//    To compute inverse(A)*C, follow SSIFA by SSISL.
//
//    To compute determinant(A), follow SSIFA by SSIDI.
//
//    To compute inertia(A), follow SSIFA by SSIDI.
//
//    To compute inverse(A), follow SSIFA by SSIDI.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the symmetric matrix
//    to be factored.  Only the diagonal and upper triangle are used.
//    On output, a block diagonal matrix and the multipliers which
//    were used to obtain it.  The factorization can be written A = U*D*U'
//    where U is a product of permutation and unit upper triangular
//    matrices, U' is the transpose of U, and D is block diagonal
//    with 1 by 1 and 2 by 2 blocks.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Output, int KPVT[N], the pivot indices.
//
//    Output, integer SSIFA, error flag.
//    0, normal value.
//    K, if the K-th pivot block is singular.  This is not an error
//    condition for this subroutine, but it does indicate that SSISL
//    or SSIDI may divide by zero if called.
//
{
  float absakk;
  float ak;
  float akm1;
  float alpha;
  float bk;
  float bkm1;
  float colmax;
  float denom;
  int imax;
  int imaxp1;
  int info;
  int j;
  int jj;
  int jmax;
  int k;
  int km1;
  int kstep;
  float mulk;
  float mulkm1;
  float rowmax;
  bool swap;
  float t;
//
//  ALPHA is used in choosing pivot block size.
//
  alpha = ( 1.0 + sqrt ( 17.0 ) ) / 8.0;

  info = 0;
//
//  Main loop on K, which goes from N to 1.
//
  k = n;

  while ( 0 < k )
  {
    if ( k == 1 )
    {
      kpvt[0] = 1;
      if ( a[0+0*lda] == 0.0 )
      {
        info = 1;
      }
      return info;
    }
//
//  This section of code determines the kind of
//  elimination to be performed.  When it is completed,
//  KSTEP will be set to the size of the pivot block, and
//  SWAP will be set to .true. if an interchange is required.
//
    km1 = k - 1;
    absakk = r4_abs ( a[k-1+(k-1)*lda] );
//
//  Determine the largest off-diagonal element in column K.
//
    imax = isamax ( k-1, a+0+(k-1)*lda, 1 );
    colmax = r4_abs ( a[imax-1+(k-1)*lda] );

    if ( alpha * colmax <= absakk )
    {
      kstep = 1;
      swap = false;
    }
//
//  Determine the largest off-diagonal element in row IMAX.
//
    else
    {
      rowmax = 0.0;
      imaxp1 = imax + 1;
      for ( j = imaxp1; j <= k; j++ )
      {
        rowmax = r4_max ( rowmax, r4_abs ( a[imax-1+(j-1)*lda] ) );
      }

      if ( imax != 1 )
      {
        jmax = isamax ( imax-1, a+0+(imax-1)*lda, 1 );
        rowmax = r4_max ( rowmax, r4_abs ( a[jmax-1+(imax-1)*lda] ) );
      }

      if ( alpha * rowmax <= r4_abs ( a[imax-1+(imax-1)*lda] ) )
      {
        kstep = 1;
        swap = true;
      }
      else if ( alpha * colmax * ( colmax / rowmax ) <= absakk ) 
      {
        kstep = 1;
        swap = false;
      }
      else
      {
        kstep = 2;
        swap = ( imax != k-1 );
      }
    }
//
//  Column K is zero.
//  Set INFO and iterate the loop.
//
    if ( r4_max ( absakk, colmax ) == 0.0 )
    {
      kpvt[k-1] = k;
      info = k;
    }
//
//  1 x 1 pivot block.
//
//  Perform an interchange.
//
    else if ( kstep != 2 )
    {
      if ( swap )
      {
        sswap ( imax, a+0+(imax-1)*lda, 1, a+0+(k-1)*lda, 1 );

        for ( jj = imax; jj <= k; jj++ )
        {
          j = k + imax - jj;
          t = a[j-1+(k-1)*lda];
          a[j-1+(k-1)*lda] = a[imax-1+(j-1)*lda];
          a[imax-1+(j-1)*lda] = t;
        }
      }
//
//  Perform the elimination.
//
      for ( jj = 1; jj <= k-1; jj++ )
      {
        j = k - jj;
        mulk = -a[j-1+(k-1)*lda] / a[k-1+(k-1)*lda];
        t = mulk;
        saxpy ( j, t, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
        a[j-1+(k-1)*lda] = mulk;
      }
//
//  Set the pivot array.
//
      if ( swap )
      {
        kpvt[k-1] = imax;
      }
      else
      {
        kpvt[k-1] = k;
      }
    }
//
//  2 x 2 pivot block.
//
//  Perform an interchange.
//
    else
    {
      if ( swap )
      {
        sswap ( imax, a+0+(imax-1)*lda, 1, a+0+(k-2)*lda, 1 );

        for ( jj = imax; jj <= k-1; jj++ )
        {
          j = k-1 + imax - jj;
          t = a[j-1+(k-1)*lda];
          a[j-1+(k-1)*lda] = a[imax-1+(j-1)*lda];
          a[imax-1+(j-1)*lda] = t;
        }

        t = a[k-2+(k-1)*lda];
        a[k-2+(k-1)*lda] = a[imax-1+(k-1)*lda];
        a[imax-1+(k-1)*lda] = t;
      }
//
//  Perform the elimination.
//
      if ( k-2 != 0 )
      {
        ak = a[k-1+(k-1)*lda] / a[k-2+(k-1)*lda];
        akm1 = a[k-2+(k-2)*lda] / a[k-2+(k-1)*lda];
        denom = 1.0 - ak * akm1;

        for ( jj = 1; jj <= k-2; jj++ )
        {
          j = k-1 - jj;
          bk = a[j-1+(k-1)*lda] / a[k-2+(k-1)*lda];
          bkm1 = a[j-1+(k-2)*lda] / a[k-2+(k-1)*lda];
          mulk = ( akm1 * bk - bkm1 ) / denom;
          mulkm1 = ( ak * bkm1 - bk ) / denom;
          t = mulk;
          saxpy ( j, t, a+0+(k-1)*lda, 1, a+0+(j-1)*lda, 1 );
          t = mulkm1;
          saxpy ( j, t, a+0+(k-2)*lda, 1, a+0+(j-1)*lda, 1 );
          a[j-1+(k-1)*lda] = mulk;
          a[j-1+(k-2)*lda] = mulkm1;
        }
      }
//
//  Set the pivot array.
//
      if ( swap )
      {
        kpvt[k-1] = -imax;
      }
      else
      {
        kpvt[k-1] = 1 - k;
      }
      kpvt[k-2] = kpvt[k-1];
    }
    k = k - kstep;
  }
  return info;
}
//****************************************************************************80

void ssisl ( float a[], int lda, int n, int kpvt[], float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSISL solves a real symmetric system factored by SSIFA.
//
//  Discussion:
//
//    To compute inverse(A) * C where C is a matrix with P columns
//
//      call ssifa ( a, lda, n, kpvt, info )
//
//      if ( info == 0 ) then
//        do j = 1, p
//          call ssisl ( a, lda, n, kpvt, c(1,j) )
//        end do
//      end if
//
//    A division by zero may occur if the inverse is requested
//    and SSICO has set RCOND == 0.0D+00 or SSIFA has set INFO /= 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float A[LDA*N], the output from SSIFA.
//
//    Input, int LDA, the leading dimension of the array A.
//
//    Input, int N, the order of the matrix.
//
//    Input, int KPVT[N], the pivot vector from SSIFA.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  float ak;
  float akm1;
  float bk;
  float bkm1;
  float denom;
  int k;
  int kp;
  float temp;
//
//  Loop backward applying the transformations and D inverse to B.
//
  k = n;

  while ( 0 < k )
  {
    if ( 0 <= kpvt[k-1] )
    {
//
//  1 x 1 pivot block.
//
      if ( k != 1 )
      {
        kp = kpvt[k-1];
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
//
//  Apply the transformation.
//
        saxpy ( k-1, b[k-1], a+0+(k-1)*lda, 1, b, 1 );
      }
//
//  Apply D inverse.
//
      b[k-1] = b[k-1] / a[k-1+(k-1)*lda];
      k = k - 1;
    }
    else
    {
//
//  2 x 2 pivot block.
//
      if ( k != 2 )
      {
        kp = abs ( kpvt[k-1] );
//
//  Interchange.
//
        if ( kp != k-1 )
        {
          temp = b[k-2];
          b[k-2] = b[kp-1];
          b[kp-1] = temp;
        }
//
//  Apply the transformation.
//
        saxpy ( k-2, b[k-1], a+0+(k-1)*lda, 1, b, 1 );
        saxpy ( k-2, b[k-2], a+0+(k-2)*lda, 1, b, 1 );
      }
//
//  Apply D inverse.
//
      ak = a[k-1+(k-1)*lda] / a[k-2+(k-1)*lda];
      akm1 = a[k-2+(k-2)*lda] / a[k-2+(k-1)*lda];
      bk = b[k-1] / a[k-2+(k-1)*lda];
      bkm1 = b[k-2] / a[k-2+(k-1)*lda];
      denom = ak * akm1 - 1.0;
      b[k-1] = ( akm1 * bk - bkm1 ) / denom;
      b[k-2] = ( ak * bkm1 - bk ) / denom;
      k = k - 2;
    }
  }
//
//  Loop forward applying the transformations.
//
  k = 1;

  while ( k <= n )
  {
    if ( 0 <= kpvt[k-1] )
    {
//
//  1 x 1 pivot block.
//
      if ( k != 1 )
      {
//
//  Apply the transformation.
//
        b[k-1] = b[k-1] + sdot ( k-1, a+0+(k-1)*lda, 1, b, 1 );
        kp = kpvt[k-1];
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
      }
      k = k + 1;
    }
    else
    {
//
//  2 x 2 pivot block.
//
      if ( k != 1 )
      {
//
//  Apply the transformation.
//
        b[k-1] = b[k-1] + sdot ( k-1, a+0+(k-1)*lda, 1, b, 1 );
        b[k] = b[k] + sdot ( k-1, a+0+k*lda, 1, b, 1 );
        kp = abs ( kpvt[k-1] );
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
      }
      k = k + 2;
    }
  }
  return;
}
//****************************************************************************80

float sspco ( float ap[], int n, int kpvt[], float z[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSPCO factors a real symmetric matrix stored in packed form.
//
//  Discussion:
//
//    SSPCO uses elimination with symmetric pivoting and estimates
//    the condition of the matrix.
//
//    If RCOND is not needed, SSPFA is slightly faster.
//
//    To solve A*X = B, follow SSPCO by SSPSL.
//
//    To compute inverse(A)*C, follow SSPCO by SSPSL.
//
//    To compute inverse(A), follow SSPCO by SSPDI.
//
//    To compute determinant(A), follow SSPCO by SSPDI.
//
//    To compute inertia(A), follow SSPCO by SSPDI.
//
//  Packed storage:
//
//    The following program segment will pack the upper triangle of a
//    symmetric matrix.
//
//      k = 0
//      do j = 1, n
//        do i = 1, j
//          k = k + 1
//          ap[k-1] = a(i,j)
//        }
//      }
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[N*(N+1)/2].  On input, the packed form
//    of a symmetric matrix A.  The columns of the upper triangle are stored
//    sequentially in a one-dimensional array.  On output, a block diagonal
//    matrix and the multipliers which were used to obtain it, stored in
//    packed form.  The factorization can be written A = U*D*U'
//    where U is a product of permutation and unit upper triangular
//    matrices, U' is the transpose of U, and D is block diagonal
//    with 1 by 1 and 2 by 2 blocks.
//
//    Input, int N, the order of the matrix.
//
//    Output, int KPVT[N], the pivot indices.
//
//    Output, float Z[N] a work vector whose contents are usually
//    unimportant.  If A is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//
//    Output, float SSPCO, an estimate of the reciprocal condition number RCOND
//    of A.  For the system A*X = B, relative perturbations in A and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0 + RCOND == 1.0D+00
//    is true, then A may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float ak;
  float akm1;
  float anorm;
  float bk;
  float bkm1;
  float denom;
  float ek;
  int i;
  int ij;
  int ik;
  int ikm1;
  int ikp1;
  int info;
  int j;
  int j1;
  int k;
  int kk;
  int km1k;
  int km1km1;
  int kp;
  int kps;
  int ks;
  float rcond;
  float s;
  float t;
  float ynorm;
//
//  Find norm of A using only upper half.
//
  j1 = 1;
  for ( j = 1; j <= n; j++ )
  {
    z[j-1] = sasum ( j, ap+j1-1, 1 );
    ij = j1;
    j1 = j1 + j;
    for ( i = 1; i <= j-1; i++ )
    {
      z[i-1] = z[i-1] + r4_abs ( ap[ij-1] );
      ij = ij + 1;
    }
  }
  anorm = 0.0;
  for ( i = 1; i <= n; i++ )
  {
    anorm = r4_max ( anorm, z[i-1] );
  }
//
//  Factor.
//
  info = sspfa ( ap, n, kpvt );
//
//  RCOND = 1/(norm(A)*(estimate of norm(inverse(A)))).
//
//  Estimate = norm(Z)/norm(Y) where A*Z = Y and A*Y = E.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of W where U*D*W = E.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve U * D * W = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  k = n;
  ik = ( n * ( n - 1 ) ) / 2;

  while ( k != 0 )
  {
    kk = ik + k;
    ikm1 = ik - ( k - 1 );

    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    kp = abs ( kpvt[k-1] );
    kps = k + 1 - ks;

    if ( kp != kps )
    {
      t = z[kps-1];
      z[kps-1] = z[kp-1];
      z[kp-1] = t;
    }

    if ( z[k-1] != 0.0 )
    {
      ek = ek * r4_sign ( z[k-1] );
    }

    z[k-1] = z[k-1] + ek;
    saxpy ( k-ks, z[k-1], ap+ik, 1, z, 1 );

    if ( ks != 1 )
    {
      if ( z[k-2] != 0.0 )
      {
        ek = ek * r4_sign ( z[k-2] );
      }
      z[k-2] = z[k-2] + ek;
      saxpy ( k-ks, z[k-2], ap+ikm1, 1, z, 1 );
    }

    if ( ks != 2 )
    {
      if ( r4_abs ( ap[kk-1] ) < r4_abs ( z[k-1] ) )
      {
        s = r4_abs ( ap[kk-1] ) / r4_abs ( z[k-1] );
        for ( i = 1; i <= n; i++ )
        {
          z[i-1] = s * z[i-1];
        }
        ek = s * ek;
      }

      if ( ap[kk-1] != 0.0 )
      {
        z[k-1] = z[k-1] / ap[kk-1];
      }
      else
      {
        z[k-1] = 1.0;
      }
    }
    else
    {
      km1k = ik + k - 1;
      km1km1 = ikm1 + k - 1;
      ak = ap[kk-1] / ap[km1k-1];
      akm1 = ap[km1km1-1] / ap[km1k-1];
      bk = z[k-1] / ap[km1k-1];
      bkm1 = z[k-2] / ap[km1k-1];
      denom = ak * akm1 - 1.0;
      z[k-1] = ( akm1 * bk - bkm1 ) / denom;
      z[k-2] = ( ak * bkm1 - bk ) / denom;
    }
    k = k - ks;
    ik = ik - k;
    if ( ks == 2 )
    {
      ik = ik - ( k + 1 );
    }
  }

  s = sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / s;
  }
//
//  Solve U' * Y = W.
//
  k = 1;
  ik = 0;

  while ( k <= n )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != 1 )
    {
      z[k-1] = z[k-1] + sdot ( k-1, ap+ik, 1, z, 1 );
      ikp1 = ik + k;

      if ( ks == 2 )
      {
        z[k] = z[k] + sdot ( k-1, ap+ikp1, 1, z, 1 );
      }

      kp = abs ( kpvt[k-1] );

      if ( kp != k )
      {
        t = z[k-1];
        z[k-1] = z[kp-1];
        z[kp-1] = t;
      }
    }

    ik = ik + k;
    if ( ks == 2 )
    {
      ik = ik + ( k + 1 );
    }
    k = k + ks;
  }
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = 1.0;
//
//  Solve U * D * V = Y.
//
  k = n;

  ik = ( n * ( n - 1 ) ) / 2;

  while ( 0 < k )
  {
    kk = ik + k;
    ikm1 = ik - ( k - 1 );

    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != ks )
    {
      kp = abs ( kpvt[k-1] );
      kps = k + 1 - ks;

      if ( kp != kps )
      {
        t = z[kps-1];
        z[kps-1] = z[kp-1];
        z[kp-1] = t;
      }

      saxpy ( k-ks, z[k-1], ap+ik, 1, z, 1 );

      if ( ks == 2 )
      {
        saxpy ( k-ks, z[k-2], ap+ikm1, 1, z, 1 );
      }
    }

    if ( ks != 2 )
    {
      if ( r4_abs ( ap[kk-1] ) < r4_abs ( z[k-1] ) )
      {
        s = r4_abs ( ap[kk-1] ) / r4_abs ( z[k-1] );
        for ( i = 1; i <= n; i++ )
        {
          z[i-1] = s * z[i-1];
        }
        ynorm = s * ynorm;
      }

      if ( ap[kk-1] != 0.0 )
      {
        z[k-1] = z[k-1] / ap[kk-1];
      }
      else
      {
        z[k-1] = 1.0;
      }
    }
    else
    {
      km1k = ik + k - 1;
      km1km1 = ikm1 + k - 1;
      ak = ap[kk-1] / ap[km1k-1];
      akm1 = ap[km1km1-1] / ap[km1k-1];
      bk = z[k-1] / ap[km1k-1];
      bkm1 = z[k-2] / ap[km1k-1];
      denom = ak * akm1 - 1.0;
      z[k-1] = ( akm1 * bk - bkm1 ) / denom;
      z[k-2] = ( ak * bkm1 - bk ) / denom;
    }
    k = k - ks;
    ik = ik - k;
    if ( ks == 2 )
    {
      ik = ik - ( k + 1 );
    }
  }
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;
//
//  Solve U' * Z = V.
//
  k = 1;
  ik = 0;

  while ( k <= n )
  {
    if ( kpvt[k-1] < 0 )
    {
      ks = 2;
    }
    else
    {
      ks = 1;
    }

    if ( k != 1 )
    {
      z[k-1] = z[k-1] + sdot ( k-1, ap+ik, 1, z, 1 );
      ikp1 = ik + k;

      if ( ks == 2 )
      {
        z[k] = z[k] + sdot ( k-1, ap+ikp1, 1, z, 1 );
      }

      kp = abs ( kpvt[k-1] );

      if ( kp != k )
      {
        t = z[k-1];
        z[k-1] = z[kp-1];
        z[kp-1] = t;
      }
    }

    ik = ik + k;
    if ( ks == 2 )
    {
      ik = ik + ( k + 1 );
    }
    k = k + ks;
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( anorm != 0.0 )
  {
    rcond = ynorm / anorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

void sspdi ( float ap[], int n, int kpvt[], float det[2], int inert[3], 
  float work[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SSPDI computes the determinant, inertia and inverse of a real symmetric matrix.
//
//  Discussion:
//
//    SSPDI uses the factors from SSPFA, where the matrix is stored in
//    packed form.
//
//    A division by zero will occur if the inverse is requested
//    and SSPCO has set RCOND == 0.0D+00 or SSPFA has set INFO /= 0.
//
//    Variables not requested by JOB are not used.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[(N*(N+1))/2].  On input, the output from
//    SSPFA.  On output, the upper triangle of the inverse of the original
//    matrix, stored in packed form, if requested.  The columns of the upper 
//    triangle are stored sequentially in a one-dimensional array.
//
//    Input, int N, the order of the matrix.
//
//    Input, int KPVT[N], the pivot vector from SSPFA.
//
//    Output, float DET[2], the determinant of the original matrix,
//    if requested.
//      determinant = DET[0] * 10.0**DET[1]
//    with 1.0D+00 <= abs ( DET[0] ) < 10.0D+00 or DET[0] = 0.0.
//
//    Output, int INERT[3], the inertia of the original matrix, if requested.
//    INERT(1) = number of positive eigenvalues.
//    INERT(2) = number of negative eigenvalues.
//    INERT(3) = number of zero eigenvalues.
//
//    Workspace, float WORK[N].
//
//    Input, int JOB, has the decimal expansion ABC where:
//      if A /= 0, the inertia is computed,
//      if B /= 0, the determinant is computed,
//      if C /= 0, the inverse is computed.
//    For example, JOB = 111  gives all three.
//
{
  float ak;
  float akkp1;
  float akp1;
  float d;
  bool dodet;
  bool doert;
  bool doinv;
  int ij;
  int ik;
  int ikp1;
  int iks;
  int j;
  int jb;
  int jk;
  int jkp1;
  int k;
  int kk;
  int kkp1;
  int km1;
  int ks;
  int ksj;
  int kskp1;
  int kstep;
  float t;
  float temp;

  doinv = ( job %   10 )       != 0;
  dodet = ( job %  100 ) /  10 != 0;
  doert = ( job % 1000 ) / 100 != 0;

  if ( dodet || doert )
  {
    if ( doert )
    {
      inert[0] = 0;
      inert[1] = 0;
      inert[2] = 0;
    }

    if ( dodet )
    {
      det[0] = 1.0;
      det[1] = 0.0;
    }

    t = 0.0;
    ik = 0;

    for ( k = 1; k <= n; k++ )
    {
      kk = ik + k;
      d = ap[kk-1];
//
//  2 by 2 block
//  use det (d  s)  =  (d/t * c - t) * t,  t = abs ( s )
//          (s  c)
//  to avoid underflow/overflow troubles.
//
//  Take two passes through scaling.  Use T for flag.
//
      if ( kpvt[k-1] <= 0 )
      {
        if ( t == 0.0 )
        {
          ikp1 = ik + k;
          kkp1 = ikp1 + k;
          t = r4_abs ( ap[kkp1-1] );
          d = ( d / t ) * ap[kkp1] - t;
        }
        else
        {
          d = t;
          t = 0.0;
        }
      }

      if ( doert )
      {
        if ( 0.0 < d )
        {
          inert[0] = inert[0] + 1;
        }
        else if ( d < 0.0 )
        {
          inert[1] = inert[1] + 1;
        }
        else if ( d == 0.0 )
        {
          inert[2] = inert[2] + 1;
        }
      }

      if ( dodet )
      {
        det[0] = det[0] * d;

        if ( det[0] != 0.0 )
        {
          while ( r4_abs ( det[0] ) < 1.0 )
          {
            det[0] = det[0] * 10.0;
            det[1] = det[1] - 1.0;
          }

          while ( 10.0 <= r4_abs ( det[0] ) )
          {
            det[0] = det[0] / 10.0;
            det[1] = det[1] + 1.0;
          }
        }
      }
      ik = ik + k;
    }
  }
//
//  Compute inverse(A).
//
  if ( doinv )
  {
    k = 1;
    ik = 0;

    while ( k <= n )
    {
      km1 = k - 1;
      kk = ik + k;
      ikp1 = ik + k;
      kkp1 = ikp1 + k;

      if ( 0 <= kpvt[k-1] )
      {
//
//  1 by 1.
//
        ap[kk-1] = 1.0 / ap[kk-1];

        if ( 2 <= k )
        {
          scopy ( k-1, ap+ik, 1, work, 1 );
          ij = 0;

          for ( j = 1; j <= k-1; j++ )
          {
            jk = ik + j;
            ap[jk-1] = sdot ( j, ap+ij, 1, work, 1 );
            saxpy ( j-1, work[j-1], ap+ij, 1, ap+ik, 1 );
            ij = ij + j;
          }
          ap[kk-1] = ap[kk-1] + sdot ( k-1, work, 1, ap+ik, 1 );
        }
        kstep = 1;
      }
      else
      {
//
//  2 by 2.
//
        t = r4_abs ( ap[kkp1-1] );
        ak = ap[kk-1] / t;
        akp1 = ap[kkp1] / t;
        akkp1 = ap[kkp1-1] / t;
        d = t * ( ak * akp1 - 1.0 );
        ap[kk-1] = akp1 / d;
        ap[kkp1] = ak / d;
        ap[kkp1-1] = -akkp1 / d;

        if ( 1 <= km1 )
        {
          scopy ( km1, ap+ikp1, 1, work, 1 );
          ij = 0;

          for ( j = 1; j <= km1; j++ )
          {
            jkp1 = ikp1 + j;
            ap[jkp1-1] = sdot ( j, ap+ij, 1, work, 1 );
            saxpy ( j-1, work[j-1], ap+ij, 1, ap+ikp1, 1 );
            ij = ij + j;
          }

          ap[kkp1] = ap[kkp1] + sdot ( km1, work, 1, ap+ikp1, 1 );
          ap[kkp1-1] = ap[kkp1-1] + sdot ( km1, ap+ik, 1, ap+ikp1, 1 );
          scopy ( km1, ap+ik, 1, work, 1 );
          ij = 0;

          for ( j = 1; j <= km1; j++ )
          {
            jk = ik + j;
            ap[jk-1] = sdot ( j, ap+ij, 1, work, 1 );
            saxpy ( j-1, work[j-1], ap+ij, 1, ap+ik, 1 );
            ij = ij + j;
          }
          ap[kk-1] = ap[kk-1] + sdot ( km1, work, 1, ap+ik, 1 );
        }
        kstep = 2;
      }
//
//  Swap.
//
      ks = abs ( kpvt[k-1] );

      if ( ks != k )
      {
        iks = ( ks * ( ks - 1 ) ) / 2;
        sswap ( ks, ap+iks, 1, ap+ik, 1 );
        ksj = ik + ks;

        for ( jb = ks; jb <= k; jb++ )
        {
          j = k + ks - jb;
          jk = ik + j;
          temp = ap[jk-1];
          ap[jk-1] = ap[ksj-1];
          ap[ksj-1] = temp;
          ksj = ksj - ( j - 1 );
        }

        if ( kstep != 1 )
        {
          kskp1 = ikp1 + ks;
          temp = ap[kskp1-1];
          ap[kskp1-1] = ap[kkp1-1];
          ap[kkp1-1] = temp;
        }
      }
      ik = ik + k;
      if ( kstep == 2 )
      {
        ik = ik + k + 1;
      }
      k = k + kstep;
    }
  }

  return;
}
//****************************************************************************80

int sspfa ( float ap[], int n, int kpvt[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSPFA factors a real symmetric matrix stored in packed form.
//
//  Discussion:
//
//    To solve A*X = B, follow SSPFA by SSPSL.
//
//    To compute inverse(A)*C, follow SSPFA by SSPSL.
//
//    To compute determinant(A), follow SSPFA by SSPDI.
//
//    To compute inertia(A), follow SSPFA by SSPDI.
//
//    To compute inverse(A), follow SSPFA by SSPDI.
//
//  Packed storage:
//
//    The following program segment will pack the upper triangle of a
//    symmetric matrix.
//
//      k = 0
//      do j = 1, n
//        do i = 1, j
//          k = k + 1
//          ap(k) = a(i,j)
//        end do
//      end do
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float AP[(N*(N+1))/2].  On input, the packed form of a
//    symmetric matrix A.  The columns of the upper triangle are stored
//    sequentially in a one-dimensional array.  On output, a block diagonal
//    matrix and the multipliers which were used to obtain it stored in
//    packed form.  The factorization can be written A = U*D*U' where U
//    is a product of permutation and unit upper triangular matrices, U'
//    is the transpose of U, and D is block diagonal with 1 by 1 and 2
//    by 2 blocks.
//
//    Input, int N, the order of the matrix.
//
//    Output, int KPVT[N], the pivot indices.
//
//    Output, int SSPFA, error flag.
//    0, normal value.
//    K, if the K-th pivot block is singular.  This is not an error
//    condition for this subroutine, but it does indicate that SSPSL or
//    SSPDI may divide by zero if called.
//
{
  float absakk;
  float ak;
  float akm1;
  float alpha;
  float bk;
  float bkm1;
  float colmax;
  float denom;
  int ij;
  int ijj;
  int ik;
  int ikm1;
  int im;
  int imax;
  int imaxp1;
  int imim;
  int imj;
  int imk;
  int info;
  int j;
  int jj;
  int jk;
  int jkm1;
  int jmax;
  int jmim;
  int k;
  int kk;
  int km1;
  int km1k;
  int km1km1;
  int kstep;
  float mulk;
  float mulkm1;
  float rowmax;
  bool swap;
  float t;
//
//  ALPHA is used in choosing pivot block size.
//
  alpha = ( 1.0 + sqrt ( 17.0 ) ) / 8.0;

  info = 0;
//
//  Main loop on K, which goes from N to 1.
//
  k = n;
  ik = ( n * ( n - 1 ) ) / 2;

  for ( ; ; )
  {
//
//  Leave the loop if K = 0 or K = 1.
//
    if ( k == 0 )
    {
      break;
    }

    if ( k == 1 )
    {
      kpvt[0] = 1;
      if ( ap[0] == 0.0 )
      {
        info = 1;
      }
      break;
    }
//
//  This section of code determines the kind of elimination to be performed.
//  When it is completed, KSTEP will be set to the size of the pivot block,
//  and SWAP will be set to .true. if an interchange is required.
//
    km1 = k - 1;
    kk = ik + k;
    absakk = r4_abs ( ap[kk-1] );
//
//  Determine the largest off-diagonal element in column K.
//
    imax = isamax ( k-1, ap+ik, 1 );
    imk = ik + imax;
    colmax = r4_abs ( ap[imk-1] );

    if ( alpha * colmax <= absakk )
    {
      kstep = 1;
      swap = false;
    }
//
//  Determine the largest off-diagonal element in row IMAX.
//
    else
    {
      rowmax = 0.0;
      imaxp1 = imax + 1;
      im = ( imax * ( imax - 1 ) ) / 2;
      imj = im + 2 * imax;

      for ( j = imaxp1; j <= k; j++ )
      {
        rowmax = r4_max ( rowmax, r4_abs ( ap[imj-1] ) );
        imj = imj + j;
      }

      if ( imax != 1 )
      {
        jmax = isamax ( imax-1, ap+im, 1 );
        jmim = jmax + im;
        rowmax = r4_max ( rowmax, r4_abs ( ap[jmim-1] ) );
      }

      imim = imax + im;

      if ( alpha * rowmax <= r4_abs ( ap[imim-1] ) )
      {
        kstep = 1;
        swap = true;
      }
      else if ( alpha * colmax * ( colmax / rowmax ) <= absakk )
      {
        kstep = 1;
        swap = false;
      }
      else
      {
        kstep = 2;
        swap = imax != km1;
      }
    }
//
//  Column K is zero.  Set INFO and iterate the loop.
//
    if ( r4_max ( absakk, colmax ) == 0.0 )
    {
      kpvt[k-1] = k;
      info = k;
    }
    else
    {
      if ( kstep != 2 )
      {
//
//  1 x 1 pivot block.
//
        if ( swap )
        {
//
//  Perform an interchange.
//
          sswap ( imax, ap+im, 1, ap+ik, 1 );
          imj = ik + imax;

          for ( jj = imax; jj <= k; jj++ )
          {
            j = k + imax - jj;
            jk = ik + j;
            t = ap[jk-1];
            ap[jk-1] = ap[imj-1];
            ap[imj-1] = t;
            imj = imj - ( j - 1 );
          }
        }
//
//  Perform the elimination.
//
        ij = ik - ( k - 1 );

        for ( jj = 1; jj <= km1; jj++ )
        {
          j = k - jj;
          jk = ik + j;
          mulk = -ap[jk-1] / ap[kk-1];
          t = mulk;
          saxpy ( j, t, ap+ik, 1, ap+ij, 1 );
          ijj = ij + j;
          ap[jk-1] = mulk;
          ij = ij - ( j - 1 );
        }
//
//  Set the pivot array.
//
        if ( swap )
        {
          kpvt[k-1] = imax;
        }
        else
        {
          kpvt[k-1] = k;
        }
      }
      else
      {
//
//  2 x 2 pivot block.
//
        km1k = ik + k - 1;
        ikm1 = ik - ( k - 1 );
//
//  Perform an interchange.
//
        if ( swap )
        {
          sswap ( imax, ap+im, 1, ap+ikm1, 1 );
          imj = ikm1 + imax;

          for ( jj = imax; jj <= km1; jj++ )
          {
            j = km1 + imax - jj;
            jkm1 = ikm1 + j;
            t = ap[jkm1-1];
            ap[jkm1-1] = ap[imj-1];
            ap[imj-1] = t;
            imj = imj - ( j - 1 );
          }
          t = ap[km1k-1];
          ap[km1k-1] = ap[imk-1];
          ap[imk-1] = t;
        }
//
//  Perform the elimination.
//
        if ( k-2 != 0 )
        {
          ak = ap[kk-1] / ap[km1k-1];
          km1km1 = ikm1 + k - 1;
          akm1 = ap[km1km1-1] / ap[km1k-1];
          denom = 1.0 - ak * akm1;
          ij = ik - ( k - 1 ) - ( k - 2 );

          for ( jj = 1; jj <= k-2; jj++ )
          {
            j = km1 - jj;
            jk = ik + j;
            bk = ap[jk-1] / ap[km1k-1];
            jkm1 = ikm1 + j;
            bkm1 = ap[jkm1-1] / ap[km1k-1];
            mulk = ( akm1 * bk - bkm1 ) / denom;
            mulkm1 = ( ak * bkm1 - bk ) / denom;
            t = mulk;
            saxpy ( j, t, ap+ik, 1, ap+ij, 1 );
            t = mulkm1;
            saxpy ( j, t, ap+ikm1, 1, ap+ij, 1 );
            ap[jk-1] = mulk;
            ap[jkm1-1] = mulkm1;
            ijj = ij + j;
            ij = ij - ( j - 1 );
          }
        }
//
//  Set the pivot array.
//
        if ( swap )
        {
          kpvt[k-1] = -imax;
        }
        else
        {
          kpvt[k-1] = 1 - k;
        }
        kpvt[k-2] = kpvt[k-1];
      }
    }

    ik = ik - ( k - 1 );
    if ( kstep == 2 )
    {
      ik = ik - ( k - 2 );
    }

    k = k - kstep;

  }

  return info;
}
//****************************************************************************80

void sspsl ( float ap[], int n, int kpvt[], float b[] )

//****************************************************************************80
//
//  Purpose:
//
//    SSPSL solves the real symmetric system factored by SSPFA.
//
//  Discussion:
//
//    To compute inverse(A) * C where C is a matrix with P columns:
//
//      call sspfa ( ap, n, kpvt, info )
//
//      if ( info /= 0 ) go to ...
//
//      do j = 1, p
//        call sspsl ( ap, n, kpvt, c(1,j) )
//      end do
//
//    A division by zero may occur if SSPCO has set RCOND == 0.0D+00
//    or SSPFA has set INFO /= 0.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float AP[(N*(N+1))/2], the output from SSPFA.
//
//    Input, int N, the order of the matrix.
//
//    Input, int KPVT[N], the pivot vector from SSPFA.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
{
  float ak;
  float akm1;
  float bk;
  float bkm1;
  float denom;
  int ik;
  int ikm1;
  int ikp1;
  int k;
  int kk;
  int km1k;
  int km1km1;
  int kp;
  float temp;
//
//  Loop backward applying the transformations and D inverse to B.
//
  k = n;
  ik = ( n * ( n - 1 ) ) / 2;

  while ( 0 < k )
  {
    kk = ik + k;

    if ( 0 <= kpvt[k-1] )
    {
//
//  1 x 1 pivot block.
//
      if ( k != 1 )
      {
        kp = kpvt[k-1];
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
//
//  Apply the transformation.
//
        saxpy ( k-1, b[k-1], ap+ik, 1, b, 1 );
      }
//
//  Apply D inverse.
//
      b[k-1] = b[k-1] / ap[kk-1];
      k = k - 1;
      ik = ik - k;
    }
    else
    {
//
//  2 x 2 pivot block.
//
      ikm1 = ik - ( k - 1 );

      if ( k != 2 )
      {
        kp = abs ( kpvt[k-1] );
//
//  Interchange.
//
        if ( kp != k-1 )
        {
          temp = b[k-2];
          b[k-2] = b[kp-1];
          b[kp-1] = temp;
        }
//
//  Apply the transformation.
//
        saxpy ( k-2, b[k-1], ap+ik, 1, b, 1 );
        saxpy ( k-2, b[k-2], ap+ikm1, 1, b, 1 );
      }
//
//  Apply D inverse.
//
      km1k = ik + k - 1;
      kk = ik + k;
      ak = ap[kk-1] / ap[km1k-1];
      km1km1 = ikm1 + k - 1;
      akm1 = ap[km1km1-1] / ap[km1k-1];
      bk = b[k-1] / ap[km1k-1];
      bkm1 = b[k-2] / ap[km1k-1];
      denom = ak * akm1 - 1.0;
      b[k-1] = ( akm1 * bk - bkm1 ) / denom;
      b[k-2] = ( ak * bkm1 - bk ) / denom;
      k = k - 2;
      ik = ik - ( k + 1 ) - k;
    }
  }
//
//  Loop forward applying the transformations.
//
  k = 1;
  ik = 0;

  while ( k <= n )
  {
    if ( 0 <= kpvt[k-1] )
    {
//
//  1 x 1 pivot block.
//
      if ( k != 1 )
      {
//
//  Apply the transformation.
//
        b[k-1] = b[k-1] + sdot ( k-1, ap+ik, 1, b, 1 );
        kp = kpvt[k-1];
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
      }
      ik = ik + k;
      k = k + 1;
    }
    else
    {
//
//  2 x 2 pivot block.
//
      if ( k != 1 )
      {
//
//  Apply the transformation.
//
        b[k-1] = b[k-1] + sdot ( k-1, ap+ik, 1, b, 1 );
        ikp1 = ik + k;
        b[k] = b[k] + sdot ( k-1, ap+ikp1, 1, b, 1 );
        kp = abs ( kpvt[k-1] );
//
//  Interchange.
//
        if ( kp != k )
        {
          temp = b[k-1];
          b[k-1] = b[kp-1];
          b[kp-1] = temp;
        }
      }
      ik = ik + k + k + 1;
      k = k + 2;
    }
  }

  return;
}
//****************************************************************************80

int ssvdc ( float a[], int lda, int m, int n, float s[], float e[], 
  float u[], int ldu, float v[], int ldv, float work[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    SSVDC computes the singular value decomposition of a real rectangular matrix.
//
//  Discussion:
//
//    This routine reduces an M by N matrix A to diagonal form by orthogonal
//    transformations U and V.  The diagonal elements S(I) are the singular
//    values of A.  The columns of U are the corresponding left singular
//    vectors, and the columns of V the right singular vectors.
//
//    The form of the singular value decomposition is then
//
//      A(MxN) = U(MxM) * S(MxN) * V(NxN)'
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    03 May 2007
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float A[LDA*N].  On input, the M by N matrix whose
//    singular value decomposition is to be computed.  On output, the matrix
//    has been destroyed.  Depending on the user's requests, the matrix may 
//    contain other useful information.
//
//    Input, int LDA, the leading dimension of the array A.
//    LDA must be at least M.
//
//    Input, int M, the number of rows of the matrix.
//
//    Input, int N, the number of columns of the matrix A.
//
//    Output, float S[MM], where MM = min(M+1,N).  The first
//    min(M,N) entries of S contain the singular values of A arranged in
//    descending order of magnitude.
//
//    Output, float E[N], where MM = min(M+1,N), ordinarily contains zeros.
//    However see the discussion of INFO for exceptions.
//
//    Output, float U[LDU*K].  If JOBA = 1 then K = M;
//    if 2 <= JOBA, then K = min(M,N).  U contains the M by M matrix of left singular
//    vectors.  U is not referenced if JOBA = 0.  If M <= N or if JOBA = 2, then
//    U may be identified with A in the subroutine call.
//
//    Input, int LDU, the leading dimension of the array U.
//    LDU must be at least M.
//
//    Output, float V[LDV*N], the N by N matrix of right singular vectors.
//    V is not referenced if JOB is 0.  If N <= M, then V may be identified
//    with A in the subroutine call.
//
//    Input, int LDV, the leading dimension of the array V.
//    LDV must be at least N.
//
//    Workspace, float WORK[M].
//
//    Input, int JOB, controls the computation of the singular
//    vectors.  It has the decimal expansion AB with the following meaning:
//      A =  0, do not compute the left singular vectors.
//      A =  1, return the M left singular vectors in U.
//      A >= 2, return the first min(M,N) singular vectors in U.
//      B =  0, do not compute the right singular vectors.
//      B =  1, return the right singular vectors in V.
//
//    Output, int SSVDC, status indicator INFO.
//    The singular values (and their corresponding singular vectors)
//    S(*INFO+1), S(*INFO+2),...,S(MN) are correct.  Here MN = min ( M, N ).
//    Thus if *INFO is 0, all the singular values and their vectors are
//    correct.  In any event, the matrix B = U' * A * V is the bidiagonal
//    matrix with the elements of S on its diagonal and the elements of E on
//    its superdiagonal.  Thus the singular values of A and B are the same.
//
{
  float b;
  float c;
  float cs;
  float el;
  float emm1;
  float f;
  float g;
  int i;
  int info;
  int iter;
  int j;
  int jobu;
  int k;
  int kase;
  int kk;
  int l;
  int ll;
  int lls;
  int ls;
  int lu;
  int maxit = 30;
  int mm;
  int mm1;
  int mn;
  int mp1;
  int nct;
  int nctp1;
  int ncu;
  int nrt;
  int nrtp1;
  float scale;
  float shift;
  float sl;
  float sm;
  float smm1;
  float sn;
  float t;
  float t1;
  float test;
  bool wantu;
  bool wantv;
  float ztest;
//
//  Determine what is to be computed.
//
  info = 0;
  wantu = false;
  wantv = false;
  jobu = ( job % 100 ) / 10;

  if ( 1 < jobu )
  {
    ncu = i4_min ( m, n );
  }
  else
  {
    ncu = m;
  }

  if ( jobu != 0 )
  {
    wantu = true;
  }

  if ( ( job % 10 ) != 0 )
  {
    wantv = true;
  }
//
//  Reduce A to bidiagonal form, storing the diagonal elements
//  in S and the super-diagonal elements in E.
//
  nct = i4_min ( m-1, n );
  nrt = i4_max ( 0, i4_min ( m, n-2 ) );
  lu = i4_max ( nct, nrt );

  for ( l = 1; l <= lu; l++ )
  {
//
//  Compute the transformation for the L-th column and
//  place the L-th diagonal in S(L).
//
    if ( l <= nct )
    {
      s[l-1] = snrm2 ( m-l+1, a+l-1+(l-1)*lda, 1 );

      if ( s[l-1] != 0.0 )
      {
        if ( a[l-1+(l-1)*lda] != 0.0 )
        {
          s[l-1] = r4_sign ( a[l-1+(l-1)*lda] ) * r4_abs ( s[l-1] );
        }
        sscal ( m-l+1, 1.0 / s[l-1], a+l-1+(l-1)*lda, 1 );
        a[l-1+(l-1)*lda] = 1.0 + a[l-1+(l-1)*lda];
      }

      s[l-1] = -s[l-1];

    }

    for ( j = l+1; j <= n; j++ )
    {
//
//  Apply the transformation.
//
      if ( l <= nct && s[l-1] != 0.0 )
      {
        t = - sdot ( m-l+1, a+l-1+(l-1)*lda, 1, a+l-1+(j-1)*lda, 1 ) 
          / a[l-1+(l-1)*lda];
        saxpy ( m-l+1, t, a+l-1+(l-1)*lda, 1, a+l-1+(j-1)*lda, 1 );
      }
//
//  Place the L-th row of A into E for the
//  subsequent calculation of the row transformation.
//
      e[j-1] = a[l-1+(j-1)*lda];
    }
//
//  Place the transformation in U for subsequent back multiplication.
//
    if ( wantu && l <= nct )
    {
      for ( i = l; i <= m; i++ )
      {
        u[i-1+(l-1)*ldu] = a[i-1+(l-1)*lda];
      }
    }

    if ( l <= nrt )
    {
//
//  Compute the L-th row transformation and place the
//  L-th superdiagonal in E(L).
//
      e[l-1] = snrm2 ( n-l, e+l, 1 );

      if ( e[l-1] != 0.0 )
      {
        if ( e[l] != 0.0 )
        {
          e[l-1] = r4_sign ( e[l] ) * r4_abs ( e[l-1] );
        }
        sscal ( n-l, 1.0 / e[l-1], e+l, 1 );
        e[l] = 1.0 + e[l];
      }

      e[l-1] = -e[l-1];
//
//  Apply the transformation.
//
      if ( l+1 <= m && e[l-1] != 0.0 )
      {
        for ( j = l+1; j <= m; j++ )
        {
          work[j-1] = 0.0;
        }

        for ( j = l+1; j <= n; j++ )
        {
          saxpy ( m-l, e[j-1], a+l+(j-1)*lda, 1, work+l, 1 );
        }

        for ( j = l+1; j <= n; j++ )
        {
          saxpy ( m-l, -e[j-1]/e[l], work+l, 1, a+l+(j-1)*lda, 1 );
        }
      }
//
//  Place the transformation in V for subsequent back multiplication.
//
      if ( wantv )
      {
        for ( j = l+1; j <= n; j++ )
        {
          v[j-1+(l-1)*ldv] = e[j-1];
        }
      }
    }
  }
//
//  Set up the final bidiagonal matrix of order MN.
//
  mn = i4_min ( m + 1, n );
  nctp1 = nct + 1;
  nrtp1 = nrt + 1;

  if ( nct < n )
  {
    s[nctp1-1] = a[nctp1-1+(nctp1-1)*lda];
  }

  if ( m < mn )
  {
    s[mn-1] = 0.0;
  }

  if ( nrtp1 < mn )
  {
    e[nrtp1-1] = a[nrtp1-1+(mn-1)*lda];
  }

  e[mn-1] = 0.0;
//
//  If required, generate U.
//
  if ( wantu )
  {
    for ( i = 1; i <= m; i++ )
    {
      for ( j = nctp1; j <= ncu; j++ )
      {
        u[(i-1)+(j-1)*ldu] = 0.0;
      }
    }

    for ( j = nctp1; j <= ncu; j++ )
    {
      u[j-1+(j-1)*ldu] = 1.0;
    }

    for ( ll = 1; ll <= nct; ll++ )
    {
      l = nct - ll + 1;

      if ( s[l-1] != 0.0 )
      {
        for ( j = l+1; j <= ncu; j++ )
        {
          t = - sdot ( m-l+1, u+(l-1)+(l-1)*ldu, 1, u+(l-1)+(j-1)*ldu, 1 ) 
            / u[l-1+(l-1)*ldu];
          saxpy ( m-l+1, t, u+(l-1)+(l-1)*ldu, 1, u+(l-1)+(j-1)*ldu, 1 );
        }

        sscal ( m-l+1, -1.0, u+(l-1)+(l-1)*ldu, 1 );
        u[l-1+(l-1)*ldu] = 1.0 + u[l-1+(l-1)*ldu];
        for ( i = 1; i <= l-1; i++ )
        {
          u[i-1+(l-1)*ldu] = 0.0;
        }
      }
      else
      {
        for ( i = 1; i <= m; i++ )
        {
          u[i-1+(l-1)*ldu] = 0.0;
        }
        u[l-1+(l-1)*ldu] = 1.0;
      }
    }
  }
//
//  If it is required, generate V.
//
  if ( wantv )
  {
    for ( ll = 1; ll <= n; ll++ )
    {
      l = n - ll + 1;

      if ( l <= nrt && e[l-1] != 0.0 )
      {
        for ( j = l+1; j <= n; j++ )
        {
          t = - sdot ( n-l, v+l+(l-1)*ldv, 1, v+l+(j-1)*ldv, 1 ) 
            / v[l+(l-1)*ldv];
          saxpy ( n-l, t, v+l+(l-1)*ldv, 1, v+l+(j-1)*ldv, 1 );
        }

      }
      for ( i = 1; i <= n; i++ )
      {
        v[i-1+(l-1)*ldv] = 0.0;
      }
      v[l-1+(l-1)*ldv] = 1.0;
    }
  }
//
//  Main iteration loop for the singular values.
//
  mm = mn;
  iter = 0;

  while ( 0 < mn )
  {
//
//  If too many iterations have been performed, set flag and return.
//
    if ( maxit <= iter )
    {
      info = mn;
      return info;
    }
//
//  This section of the program inspects for
//  negligible elements in the S and E arrays.
//
//  On completion the variables KASE and L are set as follows:
//
//  KASE = 1     if S(MN) and E(L-1) are negligible and L < MN
//  KASE = 2     if S(L) is negligible and L < MN
//  KASE = 3     if E(L-1) is negligible, L < MN, and
//               S(L), ..., S(MN) are not negligible (QR step).
//  KASE = 4     if E(MN-1) is negligible (convergence).
//
    for ( ll = 1; ll <= mn; ll++ )
    {
      l = mn - ll;

      if ( l == 0 )
      {
        break;
      }

      test = r4_abs ( s[l-1] ) + r4_abs ( s[l] );
      ztest = test + r4_abs ( e[l-1] );

      if ( ztest == test )
      {
        e[l-1] = 0.0;
        break;
      }
    }

    if ( l == mn - 1 )
    {
      kase = 4;
    }
    else
    {
      mp1 = mn + 1;

      for ( lls = l+1; lls <= mn+1; lls++ )
      {
        ls = mn - lls + l + 1;

        if ( ls == l )
        {
          break;
        }

        test = 0.0;
        if ( ls != mn )
        {
          test = test + r4_abs ( e[ls-1] );
        }

        if ( ls != l + 1 )
        {
          test = test + r4_abs ( e[ls-2] );
        }

        ztest = test + r4_abs ( s[ls-1] );

        if ( ztest == test )
        {
          s[ls-1] = 0.0;
          break;
        }

      }

      if ( ls == l )
      {
        kase = 3;
      }
      else if ( ls == mn )
      {
        kase = 1;
      }
      else
      {
        kase = 2;
        l = ls;
      }
    }

    l = l + 1;
//
//  Deflate negligible S(MN).
//
    if ( kase == 1 )
    {
      mm1 = mn - 1;
      f = e[mn-2];
      e[mn-2] = 0.0;

      for ( kk = 1; kk <= mm1; kk++ )
      {
        k = mm1 - kk + l;
        t1 = s[k-1];
        srotg ( &t1, &f, &cs, &sn );
        s[k-1] = t1;

        if ( k != l )
        {
          f = -sn * e[k-2];
          e[k-2] = cs * e[k-2];
        }

        if ( wantv )
        {
          srot ( n, v+0+(k-1)*ldv, 1, v+0+(mn-1)*ldv, 1, cs, sn );
        }
      }
    }
//
//  Split at negligible S(L).
//
    else if ( kase == 2 )
    {
      f = e[l-2];
      e[l-2] = 0.0;

      for ( k = l; k <= mn; k++ )
      {
        t1 = s[k-1];
        srotg ( &t1, &f, &cs, &sn );
        s[k-1] = t1;
        f = - sn * e[k-1];
        e[k-1] = cs * e[k-1];
        if ( wantu )
        {
          srot ( m, u+0+(k-1)*ldu, 1, u+0+(l-2)*ldu, 1, cs, sn );
        }
      }
    }
//
//  Perform one QR step.
//
    else if ( kase == 3 )
    {
//
//  Calculate the shift.
//
      scale = r4_max ( r4_abs ( s[mn-1] ), 
              r4_max ( r4_abs ( s[mn-2] ), 
              r4_max ( r4_abs ( e[mn-2] ), 
              r4_max ( r4_abs ( s[l-1] ), r4_abs ( e[l-1] ) ) ) ) );

      sm = s[mn-1] / scale;
      smm1 = s[mn-2] / scale;
      emm1 = e[mn-2] / scale;
      sl = s[l-1] / scale;
      el = e[l-1] / scale;
      b = ( ( smm1 + sm ) * ( smm1 - sm ) + emm1 * emm1 ) / 2.0;
      c = ( sm * emm1 ) * ( sm * emm1 );
      shift = 0.0;

      if ( b != 0.0 || c != 0.0 )
      {
        shift = sqrt ( b * b + c );
        if ( b < 0.0 )
        {
          shift = -shift;
        }
        shift = c / ( b + shift );
      }

      f = ( sl + sm ) * ( sl - sm ) - shift;
      g = sl * el;
//
//  Chase zeros.
//
      mm1 = mn - 1;

      for ( k = l; k <= mm1; k++ )
      {
        srotg ( &f, &g, &cs, &sn );

        if ( k != l )
        {
          e[k-2] = f;
        }

        f = cs * s[k-1] + sn * e[k-1];
        e[k-1] = cs * e[k-1] - sn * s[k-1];
        g = sn * s[k];
        s[k] = cs * s[k];

        if ( wantv )
        {
          srot ( n, v+0+(k-1)*ldv, 1, v+0+k*ldv, 1, cs, sn );
        }

        srotg ( &f, &g, &cs, &sn );
        s[k-1] = f;
        f = cs * e[k-1] + sn * s[k];
        s[k] = -sn * e[k-1] + cs * s[k];
        g = sn * e[k];
        e[k] = cs * e[k];

        if ( wantu && k < m )
        {
          srot ( m, u+0+(k-1)*ldu, 1, u+0+k*ldu, 1, cs, sn );
        }
      }
      e[mn-2] = f;
      iter = iter + 1;
    }
//
//  Convergence.
//
    else if ( kase == 4 )
    {
//
//  Make the singular value nonnegative.
//
      if ( s[l-1] < 0.0 )
      {
        s[l-1] = -s[l-1];
        if ( wantv )
        {
          sscal ( n, -1.0, v+0+(l-1)*ldv, 1 );
        }
      }
//
//  Order the singular value.
//
      for ( ; ; )
      {
        if ( l == mm )
        {
          break;
        }

        if ( s[l] <= s[l-1] )
        {
          break;
        }

        t = s[l-1];
        s[l-1] = s[l];
        s[l] = t;

        if ( wantv && l < n )
        {
          sswap ( n, v+0+(l-1)*ldv, 1, v+0+l*ldv, 1 );
        }

        if ( wantu && l < m )
        {
          sswap ( m, u+0+(l-1)*ldu, 1, u+0+l*ldu, 1 );
        }

        l = l + 1;
      }
      iter = 0;
      mn = mn - 1;
    }
  }

  return info;
}
//****************************************************************************80

float strco ( float t[], int ldt, int n, float z[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    STRCO estimates the condition of a real triangular matrix.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float T[LDT*N], the triangular matrix.  The zero
//    elements of the matrix are not referenced, and the corresponding
//    elements of the array can be used to store other information.
//
//    Input, int LDT, the leading dimension of the array T.
//
//    Input, int N, the order of the matrix.
//
//    Output, float Z[N] a work vector whose contents are usually
//    unimportant.  If T is close to a singular matrix, then Z is an
//    approximate null vector in the sense that
//      norm(A*Z) = RCOND * norm(A) * norm(Z).
//
//    Input, int JOB, indicates the shape of T:
//    0, T is lower triangular.
//    nonzero, T is upper triangular.
//
//    Output, float STRCO, an estimate of the reciprocal condition RCOND
//    of T.  For the system T*X = B, relative perturbations in T and B of size
//    EPSILON may cause relative perturbations in X of size EPSILON/RCOND.
//    If RCOND is so small that the logical expression
//      1.0D+00 + RCOND == 1.0D+00
//    is true, then T may be singular to working precision.  In particular,
//    RCOND is zero if exact singularity is detected or the estimate underflows.
//
{
  float ek;
  int i;
  int i1;
  int j;
  int j1;
  int j2;
  int k;
  int kk;
  int l;
  bool lower;
  float rcond;
  float s;
  float sm;
  float temp;
  float tnorm;
  float w;
  float wk;
  float wkm;
  float ynorm;

  lower = ( job == 0 );
//
//  Compute the 1-norm of T.
//
  tnorm = 0.0;

  for ( j = 1; j <= n; j++ )
  {
    if ( lower )
    {
      l = n + 1 - j;
      i1 = j;
    }
    else
    {
      l = j;
      i1 = 1;
    }
    tnorm = r4_max ( tnorm, sasum ( l, t+i1-1+(j-1)*ldt, 1 ) );
  }
//
//  RCOND = 1/(norm(T)*(estimate of norm(inverse(T)))).
//
//  Estimate = norm(Z)/norm(Y) where T * Z = Y and T' * Y = E.
//
//  T' is the transpose of T.
//
//  The components of E are chosen to cause maximum local
//  growth in the elements of Y.
//
//  The vectors are frequently rescaled to avoid overflow.
//
//  Solve T' * Y = E.
//
  ek = 1.0;
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = 0.0;
  }

  for ( kk = 1; kk <= n; kk++ )
  {
    if ( lower )
    {
      k = n + 1 - kk;
    }
    else
    {
      k = kk;
    }

    if ( z[k-1] != 0.0 )
    {
      ek = r4_sign ( -z[k-1] ) * ek;
    }

    if ( r4_abs ( t[k-1+(k-1)*ldt] ) < r4_abs ( ek - z[k-1] ) )
    {
      s = r4_abs ( t[k-1+(k-1)*ldt] ) / r4_abs ( ek - z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ek = s * ek;
    }

    wk = ek - z[k-1];
    wkm = -ek - z[k-1];
    s = r4_abs ( wk );
    sm = r4_abs ( wkm );

    if ( t[k-1+(k-1)*ldt] != 0.0 )
    {
      wk = wk / t[k-1+(k-1)*ldt];
      wkm = wkm / t[k-1+(k-1)*ldt];
    }
    else
    {
      wk = 1.0;
      wkm = 1.0;
    }

    if ( kk != n )
    {
      if ( lower )
      {
        j1 = 1;
        j2 = k - 1;
      }
      else
      {
        j1 = k + 1;
        j2 = n;
      }

      for ( j = j1; j <= j2; j++ )
      {
        sm = sm + r4_abs ( z[j-1] + wkm * t[k-1+(j-1)*ldt] );
        z[j-1] = z[j-1] + wk * t[k-1+(j-1)*ldt];
        s = s + r4_abs ( z[j-1] );
      }

      if ( s < sm )
      {
        w = wkm - wk;
        wk = wkm;
        for ( j = j1; j <= j2; j++ )
        {
          z[j-1] = z[j-1] + w * t[k-1+(j-1)*ldt];
        }
      }
    }
    z[k-1] = wk;
  }

  temp = sasum ( n, z, 1 );

  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = z[i-1] / temp;
  }

  ynorm = 1.0;
//
//  Solve T * Z = Y.
//
  for ( kk = 1; kk <= n; kk++ )
  {
    if ( lower )
    {
      k = kk;
    }
    else
    {
      k = n + 1 - kk;
    }

    if ( r4_abs ( t[k-1+(k-1)*ldt] ) < r4_abs ( z[k-1] ) )
    {
      s = r4_abs ( t[k-1+(k-1)*ldt] ) / r4_abs ( z[k-1] );
      for ( i = 1; i <= n; i++ )
      {
        z[i-1] = s * z[i-1];
      }
      ynorm = s * ynorm;
    }

    if ( t[k-1+(k-1)*ldt] != 0.0 )
    {
      z[k-1] = z[k-1] / t[k-1+(k-1)*ldt];
    }
    else
    {
      z[k-1] = 1.0;
    }

    if ( lower )
    {
      i1 = k + 1;
    }
    else
    {
      i1 = 1;
    }

    if ( kk < n )
    {
      w = -z[k-1];
      saxpy ( n-kk, w, t+i1-1+(k-1)*ldt, 1, z+i1-1, 1 );
    }
  }
//
//  Make ZNORM = 1.0.
//
  s = 1.0 / sasum ( n, z, 1 );
  for ( i = 1; i <= n; i++ )
  {
    z[i-1] = s * z[i-1];
  }
  ynorm = s * ynorm;

  if ( tnorm != 0.0 )
  {
    rcond = ynorm / tnorm;
  }
  else
  {
    rcond = 0.0;
  }

  return rcond;
}
//****************************************************************************80

int strdi ( float t[], int ldt, int n, float det[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    STRDI computes the determinant and inverse of a real triangular matrix.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input/output, float T[LDT*N].
//    On input, T contains the triangular matrix.  The zero elements of the
//    matrix are not referenced, and the corresponding elements of the array
//    can be used to store other information.
//    On output, T contains the inverse matrix, if it was requested.
//
//    Input, int LDT, the leading dimension of T.
//
//    Input, int N, the order of the matrix.
//
//    Output, float DET[2], the determinant of the matrix, if
//    requested.  The determinant = DET[0] * 10.0**DET[1], with
//    1.0 <= abs ( DET[0] ) < 10.0, or DET[0] == 0.
//
//    Input, int JOB, specifies the shape of T, and the task.
//    010, inverse of lower triangular matrix.
//    011, inverse of upper triangular matrix.
//    100, determinant only.
//    110, determinant and inverse of lower triangular.
//    111, determinant and inverse of upper triangular.
//
//    Output, int STRDI.
//    If the inverse was requested, then
//    0, if the system was nonsingular;
//    nonzero, if the system was singular.
//
{
  int i;
  int info;
  int j;
  int k;
  float temp;
//
//  Determinant.
//
  info = 0;

  if ( job / 100 != 0 )
  {
    det[0] = 1.0;
    det[1] = 0.0;

    for ( i = 1; i <= n; i++ )
    {
      det[0] = det[0] * t[i-1+(i-1)*ldt];

      if ( det[0] == 0.0 )
      {
        break;
      }

      while ( r4_abs ( det[0] ) < 1.0 )
      {
        det[0] = det[0] * 10.0;
        det[1] = det[1] - 1.0;
      }

      while ( 10.0 <= r4_abs ( det[0] ) )
      {
        det[0] = det[0] / 10.0;
        det[1] = det[1] + 1.0;
      }
    }
  }

  if ( ( ( job / 10 ) % 10 ) == 0 )
  {
    return info;
  }
//
//  Inverse of an upper triangular matrix.
//
  if ( ( job % 10 ) != 0 )
  {
    info = 0;

    for ( k = 1; k <= n; k++ )
    {
      if ( t[k-1+(k-1)*ldt] == 0.0 )
      {
        info = k;
        break;
      }

      t[k-1+(k-1)*ldt] = 1.0 / t[k-1+(k-1)*ldt];
      temp = -t[k-1+(k-1)*ldt];
      sscal ( k-1, temp, t+0+(k-1)*ldt, 1 );

      for ( j = k + 1; j <= n; j++ )
      {
        temp = t[k-1+(j-1)*ldt];
        t[k-1+(j-1)*ldt] = 0.0;
        saxpy ( k, temp, t+0+(k-1)*ldt, 1, t+0+(j-1)*ldt, 1 );
      }
    }
  }
//
//  Inverse of a lower triangular matrix.
//
  else
  {
    info = 0;

    for ( k = n; 1 <= k; k-- )
    {
      if ( t[k-1+(k-1)*ldt] == 0.0 )
      {
        info = k;
        break;
      }

      t[k-1+(k-1)*ldt] = 1.0 / t[k-1+(k-1)*ldt];
      temp = -t[k-1+(k-1)*ldt];

      if ( k != n )
      {
        sscal ( n-k, temp, t+k+(k-1)*ldt, 1 );
      }

      for ( j = 1; j <= k-1; j++ )
      {
        temp = t[k-1+(j-1)*ldt];
        t[k-1+(j-1)*ldt] = 0.0;
        saxpy ( n-k+1, temp, t+k-1+(k-1)*ldt, 1, t+k-1+(j-1)*ldt, 1 );
      }
    }
  }

  return info;
}
//****************************************************************************80

int strsl ( float t[], int ldt, int n, float b[], int job )

//****************************************************************************80
//
//  Purpose:
//
//    STRSL solves triangular linear systems.
//
//  Discussion:
//
//    STRSL can solve T * X = B or T' * X = B where T is a triangular
//    matrix of order N.
//
//    Here T' denotes the transpose of the matrix T.
//
//  Licensing:
//
//    This code is distributed under the GNU LGPL license. 
//
//  Modified:
//
//    04 April 2006
//
//  Author:
//
//    C++ version by John Burkardt.
//
//  Reference:
//
//    Jack Dongarra, Cleve Moler, Jim Bunch and Pete Stewart,
//    LINPACK User's Guide,
//    SIAM, (Society for Industrial and Applied Mathematics),
//    3600 University City Science Center,
//    Philadelphia, PA, 19104-2688.
//    ISBN 0-89871-172-X
//
//  Parameters:
//
//    Input, float T[LDT*N], the matrix of the system.  The zero
//    elements of the matrix are not referenced, and the corresponding
//    elements of the array can be used to store other information.
//
//    Input, int LDT, the leading dimension of the array T.
//
//    Input, int N, the order of the matrix.
//
//    Input/output, float B[N].  On input, the right hand side.
//    On output, the solution.
//
//    Input, int JOB, specifies what kind of system is to be solved:
//    00, solve T * X = B, T lower triangular,
//    01, solve T * X = B, T upper triangular,
//    10, solve T'* X = B, T lower triangular,
//    11, solve T'* X = B, T upper triangular.
//
//    Output, int STRSL, singularity indicator.
//    0, the system is nonsingular.
//    nonzero, the index of the first zero diagonal element of T.
//
{
  int info;
  int j;
  int jj;
  int kase;
  float temp;
//
//  Check for zero diagonal elements.
//
  for ( j = 1; j <= n; j++ )
  {
    if ( t[j-1+(j-1)*ldt] == 0.0 )
    {
      info = j;
      return info;
    }
  }

  info = 0;
//
//  Determine the task and go to it.
//
  if ( ( job % 10 ) == 0 )
  {
    kase = 1;
  }
  else
  {
    kase = 2;
  }

  if ( ( job % 100 ) / 10 != 0 )
  {
    kase = kase + 2;
  }
//
//  Solve T * X = B for T lower triangular.
//
  if ( kase == 1 )
  {
    b[0] = b[0] / t[0+0*ldt];
    for ( j = 2; j <= n; j++ )
    {
      temp = -b[j-2];
      saxpy ( n-j+1, temp, t+(j-1)+(j-2)*ldt, 1, b+j-1, 1 );
      b[j-1] = b[j-1] / t[j-1+(j-1)*ldt];
    }
  }
//
//  Solve T * X = B for T upper triangular.
//
  else if ( kase == 2 )
  {
    b[n-1] = b[n-1] / t[n-1+(n-1)*ldt];
    for ( jj = 2; jj <= n; jj++ )
    {
      j = n - jj + 1;
      temp = -b[j];
      saxpy ( j, temp, t+0+j*ldt, 1, b, 1 );
      b[j-1] = b[j-1] / t[j-1+(j-1)*ldt];
    }
  }
//
//  Solve T' * X = B for T lower triangular.
//
  else if ( kase == 3 )
  {
    b[n-1] = b[n-1] / t[n-1+(n-1)*ldt];
    for ( jj = 2; jj <= n; jj++ )
    {
      j = n - jj + 1;
      b[j-1] = b[j-1] - sdot ( jj-1, t+j+(j-1)*ldt, 1, b+j, 1 );
      b[j-1] = b[j-1] / t[j-1+(j-1)*ldt];
    }
  }
//
//  Solve T' * X = B for T upper triangular.
//
  else if ( kase == 4 )
  {
    b[0] = b[0] / t[0+0*ldt];
    for ( j = 2; j <= n; j++ )
    {
      b[j-1] = b[j-1] - sdot ( j-1, t+0+(j-1)*ldt, 1, b, 1 );
      b[j-1] = b[j-1] / t[j-1+(j-1)*ldt];
    }
  }

  return info;
}