Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs

• 成果类型: Article
• 署名作者: Burer, Samuel; Ye, Yinyu
• 署名单位: University of Iowa; Stanford University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-019-01367-2
• 发表日期: 2020
• 页码: 1-17
• 关键词: probabilistic analysis; simplex algorithm; AVERAGE NUMBER; rank solutions; optimization; relaxation; steps

摘要

We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs, which contain no off-diagonal terms x j xk for j = k, and we provide a sufficient condition on the problem data guaranteeing that the basic Shor semidefinite relaxation is exact. Our condition complements and refines those already present in the literature and can be checked in polynomial time. We then extend our analysis from diagonal QCQPs to general QCQPs, i.e., ones with no particular structure. By reformulating a generalQCQPinto diagonal form, we establish new, polynomial-timecheckable sufficient conditions for the semidefinite relaxations of general QCQPs to be exact. Finally, these ideas are extended to show that a class of random general QCQPs has exact semidefinite relaxations with high probability as long as the number of constraints grows no faster than a fixed polynomial in the number of variables. To the best of our knowledge, this is the first result establishing the exactness of the semidefinite relaxation for random general QCQPs.