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Yuan, G.L., Meng, Z.H. and Li, Y. (2016) A Modified Hestenes and Stiefel Conjugate Gradient Algorithm for Large-Scale Nonsmooth Minimizations and Nonlinear Equations. Journal of Optimization Theory and Applications, 168, 129-152.
https://doi.org/10.1007/s10957-015-0781-1

has been cited by the following article:

• TITLE: An Efficient Projected Gradient Method for Convex Constrained Monotone Equations with Applications in Compressive Sensing

  AUTHORS: Yaping Hu, Yujie Wang

  KEYWORDS: Projection Method, Monotone Equations, Conjugate Gradient Method, Compressive Sensing

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.8 No.6, June 1, 2020

  ABSTRACT: In this paper, a modified Polak-Ribière-Polyak conjugate gradient projection method is proposed for solving large scale nonlinear convex constrained monotone equations based on the projection method of Solodov and Svaiter. The obtained method has low-complexity property and converges globally. Furthermore, this method has also been extended to solve the sparse signal reconstruction in compressive sensing. Numerical experiments illustrate the efficiency of the given method and show that such non-monotone method is suitable for some large scale problems.