SparsePOP

(Sparse SDP Relaxation of Polynomial Optimization Problems)

Hayato Waki, Sunyoung Kim, Masakazu Kojima, Masakazu Muramatsu, Hiroshi Sugimoto and Makoto Yamashita

February 15, 2012


SparesPOP is a MATLAB implementation of a sparse semidefinite programming (SDP) relaxation method proposed for polynomial optimization problems (POPs) in the paper

H. Waki, S. Kim, M. Kojima and M. Muramatsu, ''Sums of squares and semidefinite programming relaxation for polynomial optimization problems with structured sparsity'', SIAM J. Optim. Vol 17, (1) 218-242 (2006). http://dx.doi.org/10.1137/050623802

H. Waki, S. Kim, M. Kojima, M. Muramatsu and H. Sugimoto, "SparsePOP : a Sparse Semidefinite Programming Relaxation of Polynomial Optimization Problems", ACM Transactions on Mathematical Software Vol 35, (2) 15 (2008). http://dx.doi.org/10.1145/1377612.1377619

The sparse SDP relaxation exploits a sparse structure of polynomials in POPs when applying "a hierarchy of LMI relaxations of increasing dimensions'' by Lasserre. The efficiency of SparsePOP to approximate optimal values of POPs is thus increased, and larger scale POPs can be handled.



If you have any questions, please send a message to kojima-spop "insert at mark" is.titech.ac.jp.