Automatically assigned DDC number: 51972

Manually assigned DDC number: 51972

Number of references: 35

Title: Semidefinite Optimization

Author:

Subject: M. J. Todd Semidefinite Optimization

Description: Optimization problems in which the variable is not a vector but a symmetric matrix which is required to be positive semidefinite have been intensely studied in the last ten years. Part of the reason for the interest stems from the applicability of such problems to such diverse areas as designing the strongest column, checking the stability of a di#erential inclusion, and obtaining tight bounds for hard combinatorial optimization problems. Part also derives from great advances in our ability to solve such problems e#ciently in theory and in practice (perhaps "or" would be more appropriate: the most e#ective computational methods are not always provably e#cient in theory, and vice versa). Here we describe this class of optimization problems, give a number of examples demonstrating its significance, outline its duality theory, and discuss algorithms for solving such problems. # Copyright (C) by Cambridge University Press, Acta Numerica 10 (2001) 515--560. + School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853, USA (miketodd@cs.cornell.edu). Research supported in part by NSF through grant DMS-9805602 and ONR through grant N00014-96-1-0050. 1 1

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2001-08-22

Pubyear: 2001

Format: ps

Identifier: http://citeseer.ist.psu.edu/450264.html

Source: http://www.orie.cornell.edu/~miketodd/soa4.ps.Z

Language: en

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