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arXiv · 1708.01690

Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization

Roman Pogodin·Mikhail Krechetov·Yury Maximov
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Abstract

We propose a method for low-rank semidefinite programming in application to the semidefinite relaxation of unconstrained binary quadratic problems. The method improves an existing solution of the semidefinite programming relaxation to achieve a lower rank solution. This procedure is computationally efficient as it does not require projecting on the cone of positive-semidefinite matrices. Its performance in terms of objective improvement and rank reduction is tested over multiple graphs of large-scale Gset graph collection and over binary optimization problems from the Biq Mac collection.

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Roman Pogodin, Mikhail Krechetov, Yury Maximov. 2017-08-05. Efficient Rank Minimization to Tighten Semidefinite Programming for Unconstrained Binary Quadratic Optimization. https://arxiv.org/abs/1708.01690

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