Show simple item record

dc.contributor.authorBurer, Samuel
Monteiro, Renato D.C.
Zhang, Yin
dc.date.accessioned 2018-06-18T17:48:43Z
dc.date.available 2018-06-18T17:48:43Z
dc.date.issued 2001-06
dc.identifier.citation Burer, 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) https://hdl.handle.net/1911/101973.
dc.identifier.urihttps://hdl.handle.net/1911/101973
dc.description.abstract The 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.extent 25 pp
dc.title A Computational Study of a Gradient-Based Log-Barrier Algorithm for a Class of Large-Scale SDPs
dc.type Technical report
dc.date.note June 2001
dc.identifier.digital TR01-11
dc.type.dcmi Text


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record