Fixed-Point Delayed Subgradient Methods for Nonsmooth Convex Optimization Problems

TL;DR

This paper introduces a novel fixed-point delayed subgradient method for nonsmooth convex optimization, demonstrating convergence properties and applying it to image inpainting with numerical analysis of delay effects.

Contribution

It proposes a new iterative method combining fixed-point and delayed subgradient techniques, with convergence proofs and practical application to image inpainting.

Findings

Convergence of subsequences to optimal solutions.

Whole sequence convergence under strict convexity.

Numerical results showing delay effects on objective functions.

Abstract

In this paper, we consider the nonsmooth convex optimization problems over the fixed point constraint sets of firmly nonexpansive operators. To find an optimal solution of the problem, we present an iterative method based on the hybrid steepest descent method and the idea of a delayed subgradient scheme in which allows the use of staled subgradients from the earlier iteration when updating the next iteration. We start the convergence part by deriving an upper bound for the difference of the best-achieved function values and the optimal value. After that, to ensure the convergence in iterations, we prove that there exists a subsequence of the generated sequence by the proposed method which converges to an optimal solution. Moreover, we subsequently show that the whole generated sequence converges to an optimal solution when the strict convexity of the objective function is imposed. We further extend the presented results to the centralized network system consisting of a finite number of workers and a central server. Finally, we apply the proposed method to image inpainting problems. The numerical results describe the effect of delay in many cases of objective functions.