[](https://github.com/cvxgrp/scs/actions/workflows/build.yml) [](https://www.cvxgrp.org/scs/) [](https://pepy.tech/projects/scs) [](https://anaconda.org/conda-forge/scs) SCS (`splitting conic solver`) is a numerical optimization package for solving large-scale convex cone problems. The current version is `3.3.1`. The full documentation is available [here](https://www.cvxgrp.org/scs/). If you wish to cite SCS please cite the papers listed [here](https://www.cvxgrp.org/scs/citing). ## Overview SCS solves convex cone programs of the form: ```text minimize (1/2) x'Px + c'x subject to Ax + s = b, s in K ``` where `K` is a product of convex cones. It uses an operator-splitting method (ADMM) with Anderson acceleration to achieve fast convergence. **Supported cones:** zero/linear (LP), box, second-order (SOCP), semidefinite (SDP), complex semidefinite, exponential, power, and spectral cones (log-determinant, nuclear norm, ell1-norm, sum-of-largest eigenvalues). ## Features - Multiple linear solver backends: sparse direct (QDLDL), dense direct (LAPACK), iterative (CG), Intel MKL Pardiso, Apple Accelerate (macOS), NVIDIA cuDSS, GPU - Anderson acceleration for faster convergence, including solve diagnostics - Problem data normalization/equilibration for numerical stability - Adaptive dual scaling, with per-row diagonal rescaling driven by residual profiles (`adaptive_diag_scale`, on by default) - Warm-starting and incremental `b`/`c` updates via `scs_update` - Ctrl-C signal handling for graceful interruption - Configurable precision (`float`/`double`) and index type (`int`/`long long`) - MIT licensed ## Building from Source ### Make (default) ```bash make # Build direct + indirect solvers with BLAS/LAPACK make test # Build test binaries ./out/run_tests_direct # Run direct solver tests ./out/run_tests_indirect ``` Note that the spectral-cone tests are skipped unless SCS is built with `USE_SPECTRAL_CONES=1` (see the flag table below); a default `make test` run reports them as `skipped`. ### CMake ```bash mkdir build && cd build cmake -DBUILD_TESTING=ON -DUSE_LAPACK=ON .. cmake --build . ctest ``` ### Common Build Flags | Flag | Default | Description | |------|---------|-------------| | `USE_LAPACK` | `1` | Enable BLAS/LAPACK (required for SDP support) | | `DLONG` | `0` | Use 64-bit integer indexing | | `SFLOAT` | `0` | Use single-precision floats | | `USE_SPECTRAL_CONES` | `0` | Enable spectral cone support (requires LAPACK) | | `USE_OPENMP` | `0` | Enable OpenMP parallelization | Example: `make DLONG=1 USE_LAPACK=1` ## Language Interfaces SCS has interfaces for several languages: - **Python:** [scs-python](https://github.com/bodono/scs-python) (`pip install scs`, [PyPI](https://pypi.org/project/scs/)) - **Julia:** [SCS.jl](https://github.com/jump-dev/SCS.jl) - **R:** [scs](https://cran.r-project.org/package=scs) - **MATLAB:** [scs-matlab](https://github.com/bodono/scs-matlab) ([install guide](https://www.cvxgrp.org/scs/install/matlab.html)) - **Ruby:** [scs-ruby](https://github.com/ankane/scs-ruby) In [CVXPY](https://www.cvxpy.org/), SCS is the default solver for semidefinite, exponential-cone and power-cone problems (Clarabel handles LPs and SOCPs, OSQP handles QPs). ## Project Structure ```text include/ Public API and internal headers src/ Core solver implementation linsys/ Linear solver backends (pluggable architecture) cpu/direct/ Sparse LDLt (AMD ordering + QDLDL, default) cpu/indirect/ Conjugate gradient cpu/dense/ Dense Cholesky on the Gram reduction (LAPACK dpotrf) mkl/direct/ Intel MKL Pardiso accelerate/direct/ Apple Accelerate sparse LDLt (macOS) cudss/direct/ NVIDIA cuDSS gpu/indirect/ GPU iterative solver external/ Vendored dependencies (AMD, QDLDL) test/ Test suite (minunit framework) docs/src/ Sphinx documentation source ``` ## License MIT License. See [LICENSE.txt](LICENSE.txt).