An Improved Boosted DC Algorithm for Nonsmooth Functions with Applications in Image Recovery

TL;DR

This paper introduces an improved algorithm, IBDCA, for non-smooth DC problems, demonstrating enhanced convergence and efficiency in image recovery applications compared to existing methods.

Contribution

The paper proposes IBDCA, a monotone variant of BDCA, tailored for non-smooth DC problems, with proven convergence and superior performance in image recovery tasks.

Findings

IBDCA converges to critical points with monotonic objective decrease.

Numerical experiments show IBDCA outperforms DCA and other methods.

Application in image recovery demonstrates practical effectiveness.

Abstract

We propose a new approach to perform the boosted difference of convex functions algorithm (BDCA) on non-smooth and non-convex problems involving the difference of convex (DC) functions. The recently proposed BDCA uses an extrapolation step from the point computed by the classical DC algorithm (DCA) via a line search procedure in a descent direction to get an additional decrease of the objective function and accelerate the convergence of DCA. However, when the first function in DC decomposition is non-smooth, the direction computed by BDCA can be ascent and a monotone line search cannot be performed. In this work, we proposed a monotone improved boosted difference of convex functions algorithm (IBDCA) for certain types of non-smooth DC programs, namely those that can be formulated as the difference of a possibly non-smooth function and a smooth one. We show that any cluster point of the sequence generated by IBDCA is a critical point of the problem under consideration and that the corresponding objective value is monotonically decreasing and convergent. We also present the global convergence and the convergent rate under the Kurdyka-Lojasiewicz property. The applications of IBDCA in image recovery show the effectiveness of our proposed method. The corresponding numerical experiments demonstrate that our IBDCA outperforms DCA and other state-of-the-art DC methods in both computational time and number of iterations.