A boosted DC algorithm for non-differentiable DC components with non-monotone line search

• DOI: 10.1007/s10589-024-00578-4
• Publication Date: 2024-05-11T16:01:36Z
• Authors: O. P. Ferreira; E. M. Santos; J. C. O. Souza
• Keywords: Optimization and Control (math.OC); 0211 other engineering and technologies; FOS: Mathematics; 90C26, 65K05, 65K10, 47N10; 02 engineering and technology; Mathematics - Optimization and Control

27 pages<br/>We introduce a new approach to apply the boosted difference of convex functions algorithm (BDCA) for solving non-convex and non-differentiable problems involving difference of two convex functions (DC functions). Supposing the first DC component differentiable and the second one possibly non-differentiable, the main idea of BDCA is to use the point computed by the DC algorithm (DCA) to define a descent direction and perform a monotone line search to improve the decreasing the objetive function accelerating the convergence of the DCA. However, if the first DC component is non-differentiable, then the direction computed by BDCA can be an ascent direction and a monotone line search cannot be performed. Our approach uses a non-monotone line search in the BDCA (nmBDCA) to enable a possible growth in the objective function values controlled by a parameter. Under suitable assumptions, we show that any cluster point of the sequence generated by the nmBDCA is a critical point of the problem under consideration and provide some iteration-complexity bounds. Furthermore, if the first DC component is differentiable, we present different iteration-complexity bounds and prove the full convergence of the sequence under the Kurdyka-Łojasiewicz property of the objective function. Some numerical experiments show that the nmBDCA outperforms the DCA such as its monotone version.<br/>