A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization

In this paper, we present a nonlinear programming algorithm for solving semidefinite programs (SDPs) in standard form. The algorithm's distinguishing feature is a change of variables that replaces the symmetric, positive semidefinite variable X of the SDP with a rectangular variable R according...

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Veröffentlicht in:	Mathematical programming Jg. 95; H. 2; S. 329 - 357
Hauptverfasser:	Burer, Samuel, Monteiro, Renato D.C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Heidelberg Springer 01.02.2003 Springer Nature B.V
Schlagworte:	Algorithms Applied sciences Digital Object Identifier Exact sciences and technology Mathematical programming Methods Operational research and scientific management Operational research. Management science Optimization Optimization techniques Semidefinite programming Sparsity Variables semidefinite programming Lagrangian nonlinear programming Semi definite programming limited memory BFGS Matrix factorization Non linear programming augmented Lagrangian low-rank factorization Mathematical programming Large scale system
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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