Fast implementation for semidefinite programs with positive matrix completion

Makoto Yamashita; Kazuhide Nakata

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arXiv · 1310.6919

Fast implementation for semidefinite programs with positive matrix completion

Makoto Yamashita·Kazuhide Nakata

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Abstract

Solving semidefinite programs (SDP) in a short time is the key to managing various mathematical optimization problems. The matrix-completion primal-dual interior-point method (MC-PDIPM) extracts a sparse structure of input SDP by factorizing the variable matrices. In this paper, we propose a new factorization based on the inverse of the variable matrix to enhance the performance of MC-PDIPM. We also use multithreaded parallel computing to deal with the major bottlenecks in MC-PDIPM. Numerical results show that the new factorization and multithreaded computing reduce the computation time for SDPs that have structural sparsity.

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BibTeX RIS

Makoto Yamashita, Kazuhide Nakata. 2014-05-26. Fast implementation for semidefinite programs with positive matrix completion. https://arxiv.org/abs/1310.6919

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