{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximizing the volume of a box\n", "\n", "*This example is adapted from Boyd, Kim, Vandenberghe, and Hassibi,* \"[A Tutorial on Geometric Programming](https://web.stanford.edu/~boyd/papers/pdf/gp_tutorial.pdf).\"\n", "\n", "In this example, we maximize the shape of a box with height $h$, width $w$, and depth $d$, with limits on the wall area $2(hw + hd)$\n", "and the floor area $wd$, subject to bounds on the aspect ratios $h/w$ and $w/d$. The optimization problem is\n", "\n", "$$\n", "\\begin{array}{ll}\n", "\\mbox{maximize} & hwd \\\\\n", "\\mbox{subject to} & 2(hw + hd) \\leq A_{\\text wall}, \\\\\n", "& wd \\leq A_{\\text flr}, \\\\\n", "& \\alpha \\leq h/w \\leq \\beta, \\\\\n", "& \\gamma \\leq d/w \\leq \\delta.\n", "\\end{array}\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "maximize h * w * d\n", "subject to 2.0 * (h * w + h * d) <= 100.0\n", " w * d <= 10.0\n", " 0.5 <= h / w\n", " h / w <= 2.0\n", " 0.5 <= d / w\n", " d / w <= 2.0\n" ] } ], "source": [ "import cvxpy as cp\n", "\n", "# Problem data.\n", "A_wall = 100\n", "A_flr = 10\n", "alpha = 0.5\n", "beta = 2\n", "gamma = 0.5\n", "delta = 2\n", "\n", "h = cp.Variable(pos=True, name=\"h\")\n", "w = cp.Variable(pos=True, name=\"w\")\n", "d = cp.Variable(pos=True, name=\"d\")\n", "\n", "volume = h * w * d\n", "wall_area = 2 * (h * w + h * d)\n", "flr_area = w * d\n", "hw_ratio = h/w\n", "dw_ratio = d/w\n", "constraints = [\n", " wall_area <= A_wall,\n", " flr_area <= A_flr,\n", " hw_ratio >= alpha,\n", " hw_ratio <= beta,\n", " dw_ratio >= gamma,\n", " dw_ratio <= delta\n", "]\n", "problem = cp.Problem(cp.Maximize(volume), constraints)\n", "print(problem)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "77.45966630736292" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "assert not problem.is_dcp()\n", "assert problem.is_dgp()\n", "problem.solve(gp=True)\n", "problem.value" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.7459666715289766" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h.value" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.872983364643079" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.value" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.581988871583608" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.value" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8333333206334043" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A 1% increase in allowed wall space should yield approximately\n", "# a 0.83% increase in maximum value.\n", "constraints[0].dual_value" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6666666801983365" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# A 1% increase in allowed wall space should yield approximately\n", "# a 0.66% increase in maximum value.\n", "constraints[1].dual_value" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }