Algorithm 883: SparsePOP---A Sparse Semidefinite Programming Relaxation of Polynomial Optimization Problems

Authors:
Hayato Waki;Sunyoung Kim;Masakazu Kojima;Masakazu Muramatsu;Hiroshi Sugimoto
Affiliations:
Tokyo Institute of Technology;Ewha W. University;Tokyo Institute of Technology;The University of Electro-Communications;Tokyo Institute of Technology
Venue:
ACM Transactions on Mathematical Software (TOMS)
Year:
2008

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Abstract

SparsePOP is a Matlab implementation of the sparse semidefinite programming (SDP) relaxation method for approximating a global optimal solution of a polynomial optimization problem (POP) proposed by Waki et al. [2006]. The sparse SDP relaxation exploits a sparse structure of polynomials in POPs when applying “a hierarchy of LMI relaxations of increasing dimensions” Lasserre [2006]. The efficiency of SparsePOP to approximate optimal solutions of POPs is thus increased, and larger-scale POPs can be handled.