Algorithm 883: SparsePOP - A sparse semidefinite programming relaxation of polynomial optimization problems

Hayato Waki, Sunyoung Kim, Masakazu Kojima, Masakazu Muramatsu, Hiroshi Sugimoto

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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.

Original languageEnglish
JournalACM Transactions on Mathematical Software
Issue number2
Publication statusPublished - Jul 1 2008
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Software
  • Applied Mathematics
  • Safety, Risk, Reliability and Quality

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