A nonmonotone conic trust region method based on line search for solving unconstrained optimization

DOI10.1016/j.cam.2008.05.028zbMath1160.90008OpenAlexW2037454731MaRDI QIDQ1002190

Yue-Ting Yang, Shao-Jian Qu, Qing-Pu Zhang

Publication date: 25 February 2009

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2008.05.028

zbMATH Keywords

algorithmunconstrained optimizationconvergencenumerical experimentstrust region methodconic modelnonmonotone techniqueline search technique

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Interior-point methods (90C51)

Related Items (1)

On the global convergence of a projective trust region algorithm for nonlinear equality constrained optimization

Uses Software

• minpack

Cites Work

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• Deriving collinear scaling algorithms as extensions of quasi-Newton methods and the local convergence of DFP- and BFGS-related collinear scaling algorithms
• On the global convergence of trust region algorithms for unconstrained minimization
• A Family of Trust-Region-Based Algorithms for Unconstrained Minimization with Strong Global Convergence Properties
• Conic Approximations and Collinear Scalings for Optimizers
• The Q-Superlinear Convergence of a Collinear Scaling Algorithm for Unconstrained Optimization
• Testing Unconstrained Optimization Software
• A Nonmonotone Line Search Technique for Newton’s Method
• A nonmonotone trust region method for nonlinear programming with simple bound constraints
• Global convergence of conjugate gradient methods without line search

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