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A Branch-and-Cut Algorithm for Solving Mixed-integer Semidefinite Optimization Problems

Ken Kobayashi (ken-kobayashi***at***jp.fujitsu.com)
Yuichi Takano (ytakano***at***sk.tsukuba.ac.jp)

Abstract: This paper is concerned with a cutting-plane algorithm for solving mixed-integer semidefinite optimization (MISDO) problems. In this algorithm, the positive semidefinite constraint is relaxed, and the resultant mixed-integer linear optimization problem is repeatedly solved with valid inequalities for the relaxed constraint. We prove convergence properties of the algorithm. Moreover, to speed up the computation, we devise a branch-and-cut algorithm, in which valid inequalities are dynamically added in the process of a branch-and-bound procedure. We test the computational performance of our cutting-plane and branch-and-cut algorithms for three types of MISDO problems: random instances, computing restricted isometry constants, and robust truss topology design. Experimental results demonstrate that for many problem instances, our branch-and-cut algorithm delivered superior performance compared with a general-purpose MISDO solver in terms of computational efficiency and stability.

Keywords: mixed-integer optimization, semidefinite optimization, cutting-plane algorithm, branch-and-cut algorithm

Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming )

Category 2: Linear, Cone and Semidefinite Programming (Semi-definite Programming )

Category 3: Global Optimization

Citation:

Download: [PDF]

Entry Submitted: 09/08/2018
Entry Accepted: 09/08/2018
Entry Last Modified: 06/24/2019

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