A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems
Optimization and Control
2012-03-12 v1
Abstract
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.
Cite
@article{arxiv.1203.2070,
title = {A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems},
author = {Radu Ioan Bot and Christopher Hendrich},
journal= {arXiv preprint arXiv:1203.2070},
year = {2012}
}