Decomposition-based Method for Sparse Semidefinite Relaxations of Polynomial Optimization Problems


DOI: 10.1007/s10957-009-9624-2

Cite this article as:
Kleniati, P.M., Parpas, P. & Rustem, B. J Optim Theory Appl (2010) 145: 289. doi:10.1007/s10957-009-9624-2


We consider polynomial optimization problems pervaded by a sparsity pattern. It has been shown in Lasserre (SIAM J. Optim. 17(3):822–843, 2006) and Waki et al. (SIAM J. Optim. 17(1):218–248, 2006) that the optimal solution of a polynomial programming problem with structured sparsity can be computed by solving a series of semidefinite relaxations that possess the same kind of sparsity. We aim at solving the former relaxations with a decomposition-based method, which partitions the relaxations according to their sparsity pattern. The decomposition-based method that we propose is an extension to semidefinite programming of the Benders decomposition for linear programs (Benders, Comput. Manag. Sci. 2(1):3–19, 2005).


Polynomial optimization Semidefinite programming Sparse SDP relaxations Benders decomposition 

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of ComputingImperial College LondonLondonUK

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