A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems

Details A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems

2012-03-09

arxiv.org/abs/1203.2070v1

Mathematics

Optimization and Control

Scientific paper

The aim of this paper is to develop an efficient algorithm for solving a class of unconstrained nondifferentiable convex optimization problems in finite dimensional spaces. To this end we formulate first its Fenchel dual problem and regularize it in two steps into a differentiable strongly convex one with Lipschitz continuous gradient. The doubly regularized dual problem is then solved via a fast gradient method with the aim of accelerating the resulting convergence scheme. The theoretical results are finally applied to an l1 regularization problem arising in image processing.

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