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

Simple item page
dc.contributor.authorBurer, Samuel
dc.contributor.authorMonteiro, Renato D.C.
dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-18T17:48:43Z
dc.date.available2018-06-18T17:48:43Z
dc.date.issued2001-06
dc.date.noteJune 2001
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.
dc.format.extent25 pp
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>.
dc.identifier.digitalTR01-11
dc.identifier.urihttps://hdl.handle.net/1911/101973
dc.language.isoeng
dc.titleA Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPs
dc.typeTechnical report
dc.type.dcmiText
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TR01-11.pdf
Size:
308.35 KB
Format:
Adobe Portable Document Format
Download
Collections
CAAM Technical Reports