TITLE:
Sparse Solutions of Mixed Complementarity Problems
AUTHORS:
Peng Zhang, Zhensheng Yu
KEYWORDS:
Mixed Complementarity Problem, Sparse Solution, l1 Regularized Projection Minimization Model, Extragradient Thresholding Algorithm
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.8 No.1,
December
27,
2019
ABSTRACT: In this paper, we consider an extragradient thresholding algorithm for finding the sparse solution of mixed complementarity problems (MCPs). We establish a relaxation l1 regularized projection minimization model for the original problem and design an extragradient thresholding algorithm (ETA) to solve the regularized model. Furthermore, we prove that any cluster point of the sequence generated by ETA is a solution of MCP. Finally, numerical experiments show that the ETA algorithm can effectively solve the l1 regularized projection minimization model and obtain the sparse solution of the mixed complementarity problem.