A variable smoothing algorithm for solving convex optimization problems

In this article we propose a method for solving unconstrained optimization problems with convex and Lipschitz continuous objective functions. By making use of the Moreau envelopes of the functions occurring in the objective, we smooth the latter to a convex and differentiable function with Lipschitz continuous gradient by using both variable and constant smoothing parameters. The resulting problem is solved via an accelerated first-order method and this allows us to recover approximately the optimal solutions to the initial optimization problem with a rate of convergence of order $Ø(\tfrac{\ln k}{k})$ for variable smoothing and of order $Ø(\tfrac{1}{k})$ for constant smoothing. Some numerical experiments employing the variable smoothing method in image processing and in supervised learning classification are also presented.

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