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Semidefinite programming relaxation methods for global optimization problems with sparse polynomials and unbounded semialgebraic feasible sets. (English) Zbl 1369.90136
Summary: We propose a hierarchy of semidefinite programming (SDP) relaxations for polynomial optimization with sparse patterns over unbounded feasible sets. The convergence of the proposed SDP hierarchy is established for a class of polynomial optimization problems. This is done by employing known sums-of-squares sparsity techniques of M. Kojima and M. Muramatsu [Comput. Optim. Appl. 42, No. 1, 31–41 (2009; Zbl 1153.90545)] and J. B. Lasserre [SIAM J. Optim. 17, No. 3, 822–843 (2006; Zbl 1119.90036)] together with a representation theorem for polynomials over unbounded sets obtained recently in [V. Jeyakumar et al., J. Optim. Theory Appl. 163, No. 3, 707–718 (2014; Zbl 1302.90208)]. We demonstrate that the proposed sparse SDP hierarchy can solve some classes of large scale polynomial optimization problems with unbounded feasible sets using the polynomial optimization solver SparsePOP developed by H. Waki et al. [“Algorithm 883: SparsePOP: a sparse semidefinite programming relaxation of polynomial optimization problems”, ACM Trans. Math. Softw. 35, 15 (2008)].

90C26 Nonconvex programming, global optimization
90C22 Semidefinite programming
SeDuMi; SFSDP; SparsePOP
Full Text: DOI
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