It can be used with the interactive Pythoninterpreter, on the command line by executing Python scripts, orintegrated in other software via Python extension modules. Python 3.8 compatibility.Several minor additions and improvements.Merged the source for the Python 2.7 and Python 3 versions.Translated the user guide to Sphinx. Python Software for Convex Optimization .
There are two approaches for indexing dense and sparse matrices: single-argument indexing and double-argument indexing. CVXOPT is a free software package for convex optimization based on the Python programming language. Solving a quadratic program¶.
Some BLAS and LAPACK routines. The nonlinear convex optimization solver in thesolvers module.
It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. CVXOPT .
A modeling toolfor convex piecewise-linear optimization problems.Upgrades of the GLPK and MOSEK interfaces.Upgrade of the MOSEK interface to MOSEK version 6. Indexing of matrices¶. Improved Windows compatibility (Python 3.5+).Several bug fixes (int/int_t issues). A few bug fixes inthe matrix class.Several bug fixes. matrix(), spmatrix(), and the other functions in cvxopt.base can now be directly imported from cvxopt (“ from cvxopt import matrix ” replaces “ from cvxopt.base import matrix ”, although the older code still works).
A Numpy array is created from a matrix using Numpy… This example illustrates different ways to create dense and sparse matrices. Upgrade to SuiteSparseversion 4.1.0. Interfaces to the MOSEK andGLPK integer LP solvers (these features are documented in the sourcedocstrings).Upgrade to SuiteSparse version 4.4.5. In double-argument indexing a matrix is indexed using two index-sets I and J. A new cone program solver, with support for second-order coneconstraints.Addition of two-dimensional discrete transforms.
As an example, we can solve the QP
Several bug fixes.Several bug fixes.
Performanceimprovements in the optimization routines. The LAPACKsolvers for banded and tridiagonal equations. Improved SunOS/Solariscompatibility (“complex double” instead of “complex”).Performance improvements in the sparse matrix arithmetic.
Several bug fixes.Python 3.8 and PyPy compatibility.Sparse linear equation solvers from UMFPACK and LDL. Its main purposeis to make the development of software for convex optimization applicationsstraightforward by building on Python’s extensive standard libraryand on the strengths of Python as a high-level programming language.interfaces to the linear programming solver in GLPK, the semidefiniteprogramming solver in DSDP5, and the linear, quadratic and second-ordercone programming solvers in MOSEKinterfaces to the sparse LU and Cholesky solvers from UMFPACK and CHOLMODan interface to the fast Fourier transform routines from FFTWCVXOPT was originally developed for use in our own work, and is being madeavailable in the hope that it may be useful to others.We welcome feedback, bug reports, and suggestions for improvements, butcan only offer very limited support.Python Software for Convex Optimizationroutines for nonlinear convex optimization
Performance improvementsfor certain spmatrix slicing operations. A dense matrix is created using the matrix() function; it can be created from a list (or iterator): Improved Numpy compatibility via buffer protocol(works in both Python 2.x and 3.x). Numpy and CVXOPT¶ In Python 2.7, Numpy arrays and CVXOPT matrices are compatible and exchange information using the Array Interface.
Several bug fixes.Interfaces to the LP solvers in MOSEK and GLPK.Removed the SuiteSparse source code from the distribution.The CHOLMOD interface.