期刊
ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE
卷 35, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1377612.1377619
关键词
algorithms; global optimization; Matlab software package; polynomial optimization problem; semidefinite programming relaxation; sparsity; sums-of-squares optimization
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.
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