Recognizing underlying sparsity in optimization

DOI10.1007/s10107-008-0210-4zbMath1163.90026OpenAlexW2074448783WikidataQ58185774 ScholiaQ58185774MaRDI QIDQ1013977

Sunyoung Kim, Kojima, Masakazu, Phillipe L. Toint

Publication date: 24 April 2009

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10107-008-0210-4

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Semidefinite programming (90C22) Nonlinear programming (90C30)

Related Items

Correlative sparsity structures and semidefinite relaxations for concave cost transportation problems with change of variables, Solving polynomial least squares problems via semidefinite programming relaxations, Strong practical stability and stabilization of uncertain discrete linear repetitive processes, An Iterative Scheme for Valid Polynomial Inequality Generation in Binary Polynomial Programming, Decomposition-based method for sparse semidefinite relaxations of polynomial optimization problems, Correlative sparsity in primal-dual interior-point methods for LP, SDP, and SOCP, A dynamic inequality generation scheme for polynomial programming

Uses Software

• LANCELOT
• GALAHAD
• SeDuMi
• CUTEr
• SparsePOP
• GLOBALLib
• ve08
• minpack

Cites Work

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