Regularization methods for semidefinite programming. (English) Zbl 1187.90219

Summary: 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” from J. Povh, F. Rendl and A. Wiegele [Computing 78, No. 3, 277–286 (2006; Zbl 1275.90055)] is an instance of this class.


90C22 Semidefinite programming
90C53 Methods of quasi-Newton type
90C06 Large-scale problems in mathematical programming


Zbl 1275.90055
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