 An adaptive family of projection methods for constrained monotone nonlinear equations with applicationsPeiting Gao, Chuanjiang He and Yang Liu Applied Mathematics and Computation, 2019, vol. 359, issue C, 1-16 Abstract: An adaptive class of conjugate gradient methods is proposed in this paper, which all possess descent property under strong-wolfe line search. The adaptive parameter in the search direction is determined by minimizing the distance between relative matrix and self-scaling memoryless BFGS update by Oren in the Frobenius norm. Two formulas of the adaptive parameter are further obtained, which are presented as those given by Oren and Luenberger (1973/74) and respectively Oren and Spedicato (1976). By projection technology, two adaptive projection algorithms are developed for solving monotone nonlinear equations with convex constraints. Some numerical comparisons show that these two algorithms are efficient. Last, the proposed algorithms are used to recover a sparse signal from incomplete and contaminated sampling measurements; the results are promising. Keywords: Monotone equations; Conjugate gradient method; Self-scaling memoryless BFGS update; Projection method; Compressed senseing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:359:y:2019:i:c:p:1-16 DOI: 10.1016/j.amc.2019.03.064 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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