Welcome to CVXPY CVXPY 0.4.11 documentation

For the best CVXPY support! Try the new, improved CVXPY 1.0, available. Please report any bugs you find! CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. For example, the following code solves a least-squares problem where the variable is constrained by lower and upper bounds. The optimal objective is returned by prob.solve.

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This website cvxpy.org was created on February 17, 2014. As of today, it is five hundred and eighty-five weeks, eighteen days, three hours, and twenty-four minutes young.
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LINKS TO BUSINESS

Home CVXOPT

Python Software for Convex Optimization. An interface to most of the double-precision real and complex BLAS. Routines for nonlinear convex optimization.

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TITLE

Welcome to CVXPY CVXPY 0.4.11 documentation

DESCRIPTION

For the best CVXPY support! Try the new, improved CVXPY 1.0, available. Please report any bugs you find! CVXPY is a Python-embedded modeling language for convex optimization problems. It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. For example, the following code solves a least-squares problem where the variable is constrained by lower and upper bounds. The optimal objective is returned by prob.solve.

CONTENT

This website cvxpy.org has the following in the web site, "For the best CVXPY support! Try the new, improved CVXPY 1." Our analyzers analyzed that the website also stated " Please report any bugs you find! CVXPY is a Python-embedded modeling language for convex optimization problems." The Website also stated " It allows you to express your problem in a natural way that follows the math, rather than in the restrictive standard form required by solvers. For example, the following code solves a least-squares problem where the variable is constrained by lower and upper bounds. The optimal objective is returned by prob."

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