A Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPs

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dc.contributor.authorBurer, Samuelen_US
dc.contributor.authorMonteiro, Renato D.C.en_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-18T17:48:43Zen_US
dc.date.available2018-06-18T17:48:43Zen_US
dc.date.issued2001-06en_US
dc.date.noteJune 2001en_US
dc.description.abstractThe authors of this paper recently introduced a transformation that converts a class of semidefinite programs (SDPs) into nonlinear optimization problems free of matrix-valued constraints and variables. This transformation enables the application of nonlinear optimization techniques to the solution of certain SDPs that are too large for conventional interior-point methods to handle efficiently. Based on the transformation, they proposed a globally convergent, first-order (i.e., gradient-based) log-barrier algorithm for solving a class of linear SDPs. In this paper, we discuss an efficient implementation of the proposed algorithm and report computational results on semidefinite relaxations of three types of combinatorial optimization problems. Our results demonstrate that the proposed algorithm is indeed capable of solving large-scale SDPs and is particularly effective for problems with a large number of constraints.en_US
dc.format.extent25 ppen_US
dc.identifier.citationBurer, Samuel, Monteiro, Renato D.C. and Zhang, Yin. "A Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPs." (2001) <a href="https://hdl.handle.net/1911/101973">https://hdl.handle.net/1911/101973</a>.en_US
dc.identifier.digitalTR01-11en_US
dc.identifier.urihttps://hdl.handle.net/1911/101973en_US
dc.language.isoengen_US
dc.titleA Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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