Mathematical Programming

, Volume 156, Issue 1–2, pp 21–57 | Cite as

A dynamic inequality generation scheme for polynomial programming

Full Length Paper Series A

Abstract

Hierarchies of semidefinite programs have been used to approximate or even solve polynomial programs. This approach rapidly becomes computationally expensive and is often tractable only for problems of small size. In this paper, we propose a dynamic inequality generation scheme to generate valid polynomial inequalities for general polynomial programs. When used iteratively, this scheme improves the bounds without incurring an exponential growth in the size of the relaxation. As a result, the proposed scheme is in principle scalable to large general polynomial programming problems. When all the variables of the problem are non-negative or when all the variables are binary, the general algorithm is specialized to a more efficient algorithm. In the case of binary polynomial programs, we show special cases for which the proposed scheme converges to the global optimal solution. We also present several examples illustrating the computational behavior of the scheme and provide comparisons with Lasserre’s approach and, for the binary linear case, with the lift-and-project method of Balas, Ceria, and Cornuéjols.

Keywords

Polynomial programming Binary polynomial programming  Semidefinite programming Inequality generation 

Mathematics Subject Classification

90C22 90C26 90C27 90C30 

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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society 2015

Authors and Affiliations

  • Bissan Ghaddar
    • 1
  • Juan C. Vera
    • 2
  • Miguel F. Anjos
    • 3
  1. 1.IBM Research, Dublin Technology CampusMulhuddartIreland
  2. 2.Tilburg School of Economics and ManagementTilburg UniversityTilburgThe Netherlands
  3. 3.Canada Research Chair in Discrete Nonlinear Optimization in EngineeringGERAD and École Polytechnique de MontréalMontréalCanada

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