A New Conjugate Gradient Projection Method for Convex Constrained Nonlinear Equations. (9th July 2020)

• Record Type: Journal Article
• Authors: Liu, Pengjie; Jian, Jinbao; Jiang, Xianzhen
• Other Names: Liu Chongyang Academic Editor.
• Is Part Of: Complexity. Volume 2020(2020)
• Publication Date: 2020-07-09
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2020/8323865
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : The conjugate gradient projection method is one of the most effective methods for solving large-scale monotone nonlinear equations with convex constraints. In this paper, a new conjugate parameter is designed to generate the search direction, and an adaptive line search strategy is improved to yield the step size, and then, a new conjugate gradient projection method is proposed for large-scale monotone nonlinear equations with convex constraints. Under mild conditions, the proposed method is proved to be globally convergent. A large number of numerical experiments for the presented method and its comparisons are executed, which indicates that the presented method is very promising. Finally, the proposed method is applied to deal with the recovery of sparse signals.