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

Title
Algorithm 883: SparsePOP - A sparse semidefinite programming relaxation of polynomial optimization problems
Authors
Waki H.Kim S.Kojima M.Muramatsu M.Sugimoto H.
Ewha Authors
김선영
SCOPUS Author ID
김선영scopus
Issue Date
2008
Journal Title
ACM Transactions on Mathematical Software
ISSN
0098-3500JCR Link
Citation
ACM Transactions on Mathematical Software vol. 35, no. 2
Indexed
SCI; SCIE; SCOPUS WOS scopus
Document Type
Article
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. © 2008 ACM.
DOI
10.1145/1377612.1377619
Appears in Collections:
자연과학대학 > 수학전공 > Journal papers
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