259. Douglas-Rachford DC methods for generalized DC programming
Invited abstract in session FD-6: Difference and decomposition methods, stream Methods for non-/monotone inclusions and their applications.
Friday, 14:10 - 15:50Room: M:H
Authors (first author is the speaker)
| 1. | AVINASH DIXIT
|
| Mathematics, Kirori Mal College, University of Delhi |
Abstract
Nonconvex functions are a trending framework among researchers in optimization. In this paper, we consider the difference of convex functions (DC) programming problems which is the backbone of nonconvex programming and global optimization. The classical problem contains the difference between two proper convex and lower semicontinuous functions. This paper deals with the generalized DC programming problem which deals with the minimization of three convex functions. We propose two novel methods, an inertial Douglas Rachford DC algorithm and a parametrized Douglas Rachford DC algorithm to solve the problem. We study their convergence behavior in the Hilbert space framework. Lastly, we conducted numerical experiments to study the application of proposed algorithms to solve real-world problems. The results show that the proposed algorithms outperform other already proposed algorithms.
Keywords
- SS - Advances in Nonlinear Optimization and Applications
- Analysis and engineering of optimization algorithms
- Convex and non-smooth optimization
Status: accepted
Back to the list of papers