Combining nonmonotone conic trust region and line search techniques for unconstrained optimization

DOI10.1016/j.cam.2010.10.044zbMath1215.65107OpenAlexW2015103152MaRDI QIDQ629507

Boying Wu, Zhaocheng Cui, Shao-Jian Qu

Publication date: 9 March 2011

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.2010.10.044

zbMATH Keywords

unconstrained optimizationglobal convergencenumerical experimentsline searchconic modelnonmonotone trust region method

Mathematics Subject Classification ID

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

Related Items (3)

On the global convergence of a projective trust region algorithm for nonlinear equality constrained optimization ⋮ Nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization ⋮ Nonmonotone conic trust region method with line search technique for bound constrained optimization

Uses Software

• L-BFGS
• minpack

Cites Work

• A nonmonotone trust region method for unconstrained optimization
• A conic trust-region method for optimization with nonlinear equality and inequality constrains via active-set strategy
• A nonmonotone trust-region method of conic model for unconstrained optimization
• A class of nonmonotone trust region algorithms for unconstrained optimization problems
• Nonmonotonic trust region algorithm
• Nonmonotone trust region method for solving optimization problems
• A nonmonotone trust region method based on nonincreasing technique of weighted average of the successive function values
• Incorporating nonmonotone strategies into the trust region method for unconstrained optimization
• A trust-region method with a conic model for unconstrained optimization
• On the global convergence of trust region algorithms for unconstrained minimization
• Global Convergence of a Class of Trust Region Algorithms for Optimization with Simple Bounds
• Conic Approximations and Collinear Scalings for Optimizers
• The Q-Superlinear Convergence of a Collinear Scaling Algorithm for Unconstrained Optimization
• Testing Unconstrained Optimization Software
• Numerical Optimization
• A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
• A Nonmonotone Line Search Technique for Newton’s Method
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