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Accelerated nonnegative proximal gradient algorithm for sparse linear complementarity problem
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DOI：10.1007/s10589-026-00783-3.png)
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Abstract
En 中文
This paper aims to find a sparse solution for the linear complementarity problem (LCP) by solving a composite optimization problem with nonnegative constraint. We propose nonnegative proximal gradient algorithm with extrapolation technique, after illustrating the relationship between the nonnegative proximal operator and the standard one. We present the convergence of our proposed algorithm to a stationary point of the composite optimization problem. Furthermore, we establish the approximate global convergence of proposed algorithm for the & ell;0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _0$$\end{document} regularization problem as a special case. Numerical results demonstrate the effectiveness of proposed algorithm in approaching a sparse solution for the LCP. Finally, the application to Markowitz portfolio selection is discussed.
Keywords:
Sparse LCP
Composite optimization
Nonnegative proximal gradient algorithm
Extrapolation technique
Markowitz portfolio
Journal
C
IF:
2
Papers:
68
Citations:
3.5K