Spectral relaxations and branching strategies for global optimization of mixed-integer quadratic programs
| Autor: | Arvind U. Raghunathan, Nikolaos V. Sahinidis, Carlos J. Nohra |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021103 operations research
Mathematics::Optimization and Control 0211 other engineering and technologies Regular polygon MathematicsofComputing_NUMERICALANALYSIS 90C11 90C20 90C26 010103 numerical & computational mathematics 02 engineering and technology Quadratic function 01 natural sciences Theoretical Computer Science Branching (linguistics) Statistics::Machine Learning Quadratic equation Optimization and Control (math.OC) FOS: Mathematics Applied mathematics 0101 mathematics Mathematics - Optimization and Control Global optimization Software Integer (computer science) Mathematics |
| DOI: | 10.48550/arxiv.2010.04822 |
| Popis: | We consider the global optimization of nonconvex quadratic programs and mixed-integer quadratic programs. We present a family of convex quadratic relaxations which are derived by convexifying nonconvex quadratic functions through perturbations of the quadratic matrix. We investigate the theoretical properties of these quadratic relaxations and show that they are equivalent to some particular semidefinite programs. We also introduce novel branching variable selection strategies which can be used in conjunction with the quadratic relaxations investigated in this paper. We integrate the proposed relaxation and branching techniques into the global optimization solver BARON, and test our implementation by conducting numerical experiments on a large collection of problems. Results demonstrate that the proposed implementation leads to very significant reductions in BARON's computational times to solve the test problems. Comment: 33 pages, 26 figures |
| Databáze: | OpenAIRE |
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