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[ascl:2008.017] CVXOPT: Convex Optimization

CVXOPT makes the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language. It offers efficient Python classes for dense and sparse matrices (real and complex) with Python indexing and slicing and overloaded operations for matrix arithmetic, an interface to the fast Fourier transform routines from FFTW, and an interface to most of the double-precision real and complex BLAS. It contains routines for linear, second-order cone, and semidefinite programming problems, and for nonlinear convex optimization. CVXOPT also provides an interface to LAPACK routines for solving linear equations and least-squares problems, matrix factorizations (LU, Cholesky, LDLT and QR), symmetric eigenvalue and singular value decomposition, and Schur factorization, and a modeling tool for specifying convex piecewise-linear optimization problems.

Code site:
https://cvxopt.org/ https://github.com/cvxopt/cvxopt
Used in:
https://ui.adsabs.harvard.edu/abs/2018JCAP...12..018I https://ui.adsabs.harvard.edu/abs/2019PhRvD..99j3019K
Bibcode:
2020ascl.soft08017A

Views: 117

ascl:2008.017
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