A globally and R-linearly convergent hybrid HS and PRP method and its inexact version with applications

DOI10.1007/s11253-015-1118-9zbMath1352.65160OpenAlexW2189424086MaRDI QIDQ326018

Weijun Zhou

Publication date: 12 October 2016

Published in: Ukrainian Mathematical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11253-015-1118-9

zbMATH Keywords

algorithmconvergencenonconvex optimizationcomputational efficiencyconjugate gradient methodsMoreau-Yosida regularizationlarge-scale unconstrained optimizationnonsmooth convex optimizationHestenes-Stiefel methodPolak-Ribiére-Polyak method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Nonconvex programming, global optimization (90C26) Complexity and performance of numerical algorithms (65Y20)

Uses Software

• DFO

Cites Work

• Unnamed Item
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• A Globally and Superlinearly Convergent Algorithm for Nonsmooth Convex Minimization
• A Characterization of Superlinear Convergence and Its Application to Quasi-Newton Methods
• A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
• A New Conjugate Gradient Method with Guaranteed Descent and an Efficient Line Search
• Function minimization by conjugate gradients
• Methods of conjugate gradients for solving linear systems
• New conjugacy conditions and related nonlinear conjugate gradient methods
• An efficient hybrid conjugate gradient method for unconstrained optimization

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