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

We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs , which contain no off-diagonal terms x j x k for j ≠ k , and we provide a sufficient condition on the problem data guaranteeing that the basic Shor semidefinite relaxation is exact. Our condition complem...

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Vydané v:Mathematical programming Ročník 181; číslo 1; s. 1 - 17
Hlavní autori: Burer, Samuel, Ye, Yinyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020
Springer Nature B.V
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ISSN:0025-5610, 1436-4646
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Abstract We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs , which contain no off-diagonal terms x j x k 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 general QCQP into diagonal form, we establish new, polynomial-time-checkable 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.
AbstractList We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs, which contain no off-diagonal terms xjxk 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 general QCQP into diagonal form, we establish new, polynomial-time-checkable 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.
We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs , which contain no off-diagonal terms x j x k 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 general QCQP into diagonal form, we establish new, polynomial-time-checkable 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.
Author Ye, Yinyu
Burer, Samuel
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  orcidid: 0000-0001-5886-458X
  surname: Burer
  fullname: Burer, Samuel
  email: samuel-burer@uiowa.edu
  organization: Department of Management Sciences, University of Iowa
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  givenname: Yinyu
  surname: Ye
  fullname: Ye, Yinyu
  organization: Department of Management Science and Engineering, Institute of Computational and Mathematical Engineering, Stanford University
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ContentType Journal Article
Copyright Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2019
Mathematical Programming is a copyright of Springer, (2019). All Rights Reserved.
Springer-Verlag GmbH Germany, part of Springer Nature and Mathematical Optimization Society 2019.
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Snippet We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs , which contain no off-diagonal terms x j x k for j ≠ k , and...
We study a class of quadratically constrained quadratic programs (QCQPs), called diagonal QCQPs, which contain no off-diagonal terms xjxk for j≠k, and we...
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SubjectTerms Approximation
Calculus of Variations and Optimal Control; Optimization
Combinatorics
Constraints
Formulations
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Mathematical and Computational Physics
Mathematical Methods in Physics
Mathematical programming
Mathematics
Mathematics and Statistics
Mathematics of Computing
Numerical Analysis
Optimization
Polynomials
Theoretical
Variables
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Title Exact semidefinite formulations for a class of (random and non-random) nonconvex quadratic programs
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