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Shang, M. and Nie, C. (2014) A Shrinkage-Thresholding Projection Method for Sparsest Solutions of LCPs. Journal of Inequalities and Applications, 2014, Article No. 51.
https://doi.org/10.1186/1029-242X-2014-51

has been cited by the following article:

• 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.