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Article Dans Une Revue SIAM Journal on Optimization Année : 2009

Regularization Methods for Semidefinite Programming

Résumé

We introduce a new class of algorithms for solving linear semidefinite programming (SDP) problems. Our approach is based on classical tools from convex optimization such as quadratic regularization and augmented Lagrangian techniques. We study the theoretical properties and we show that practical implementations behave very well on some instances of SDP having a large number of constraints. We also show that the ''boundary point method'' is an instance of this class.
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Dates et versions

hal-00389552 , version 1 (28-05-2009)

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Jérôme Malick, Janez Povh, Franz Rendl, Angelika Wiegele. Regularization Methods for Semidefinite Programming. SIAM Journal on Optimization, 2009, 20 (1), pp.336-356. ⟨10.1137/070704575⟩. ⟨hal-00389552⟩
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