Barrier subgradient method

In this paper we develop a new primal-dual subgradient method for nonsmooth convex optimization problems. This scheme is based on a self-concordant barrier for the basic feasible set. It is suitable for finding approximate solutions with certain relative accuracy. We discuss some applications of this technique including fractional covering problem, maximal concurrent flow problem, semidefinite relaxations and nonlinear online optimization.

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Document type	Document de travail (Working Paper)
Access type	Accès libre
Publication date	2008
Language	Anglais
Number of pages	21 pages
Collection	CORE Discussion Papers - 2008/60
Affiliation	UCL - EUEN/CORE - Center for operations research and econometrics
Keywords	convex optimization ; subgradient methods ; non-smooth optimization, ; minimax problems ; saddle points ; variational inequalities ; stochastic optimization ; black-box methods ; lower complexity bounds
Links	http://www.uclouvain.be/cps/ucl/doc/core/documents/coredp2008_60.pdf http://hdl.handle.net/2078.1/19673[Handle]

See also
	[boreal:94930]	Barrier subgradient method