Two fundamental convergence theorems for nonlinear conjugate gradient methods and their applications

DOI10.1007/BF02669682zbMath0973.65048OpenAlexW2320871099WikidataQ126226942 ScholiaQ126226942MaRDI QIDQ5931897

Defeng Sun, Guang-Hui Liu, Ji-ye Han, Hong-Xia Yin

Publication date: 6 May 2001

Published in: Acta Mathematicae Applicatae Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02669682

zbMATH Keywords

unconstrained optimizationconvergencenonlinear conjugate gradient methodsFletcher-Reeves algorithmHestenes-Stiefel algorithmPolak-Ribiére algorithm

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items (5)

New nonlinear conjugate gradient formulas for large-scale unconstrained optimization problems ⋮ A class of one parameter conjugate gradient methods ⋮ Two modified scaled nonlinear conjugate gradient methods ⋮ A modified scaled memoryless BFGS preconditioned conjugate gradient method for unconstrained optimization ⋮ A modified PRP conjugate gradient method

Cites Work

• Efficient hybrid conjugate gradient techniques
• Global convergence result for conjugate gradient methods
• Descent Property and Global Convergence of the Fletcher—Reeves Method with Inexact Line Search
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• Line search algorithms with guaranteed sufficient decrease
• Function minimization by conjugate gradients
• Convergence Conditions for Ascent Methods
• Methods of conjugate gradients for solving linear systems
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