Barrier subgradient method
AbstractIn 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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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2008060.
Date of creation: 01 Oct 2008
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convex optimization; subgradient methods; non-smooth optimization; minimax problems; saddle points; variational inequalities; stochastic optimization; black-box methods; lower complexity bounds.;
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