A double smoothing technique for solving unconstrained nondifferentiable convex optimization problems

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.

Cited by in corpus (1)

• On the acceleration of the double smoothing technique for unconstrained convex optimization problems