Python Extension for Sparse Matrix Cone Programs
SMCP is a software package for solving linear sparse matrix cone programs. The code is experimental and it is released to accompany the following paper:
M. S. Andersen, J. Dahl, and L. Vandenberghe, Implementation of nonsymmetric interior-point methods for linear optimization over sparse matrix cones. Mathematical Programming Computation, Aug. 2010.
The package provides an implementation of a nonsymmetric interior-point method which is based on chordal matrix techniques. Only one type of cone is used, but this cone includes the three canonical cones — the nonnegative orthant, the second-order cone, and the positive semidefinite cone — as special cases. The efficiency of the solver depends not only on the dimension of the cone, but also on its structure. Nonchordal sparsity patterns are handled using chordal embedding techniques.
Version 0.3a (May 10, 2010) includes:
We plan to turn SMCP into a C library with Python and Matlab interfaces. Future releases may include additional functionality as listed below:
A platform-independent source package as well as Windows binaries are available from the Download and installation section. The source package includes source code, documentation, and installation guidelines.
SMCP is developed by Martin S. Andersen (firstname.lastname@example.org), as PhD work under Lieven Vandenberghe at University of California, Los Angeles. The independent packages CVXOPT and CHOMPACK, which are included in the SMCP distribution, are developed by Joachim Dahl and Lieven Vandenberghe.
We welcome feedback, and bug reports are much appreciated. Please email bug reports to email@example.com along with the following information: