A Newton-like trust region method for large-scale unconstrained nonconvex minimization
type: Item
instance of: scholarly article
title: A Newton-like trust region method for large-scale unconstrained nonconvex minimization
zbMATH Open document ID: 1291.90192
DOI: 10.1155/2013/478407
author: Yang Weiwei
author: Yang Yueting
author: Zhang Chenhui
author: Cao Mingyuan
published in: Abstract and Applied Analysis
publication date: 2014-06-23T00:00:00Z
review text: Summary: We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new straightforward limited memory quasi-Newton updating based on the modified quasi-Newton equation is deduced to construct the trust region subproblem, in which the information of both the function value and gradient is used to construct approximate Hessian. The global convergence of the algorithm is proved. Numerical results indicate that the proposed method is competitive and efficient on some classical large-scale nonconvex test problems.
Mathematics Subject Classification ID: 90C26
Mathematics Subject Classification ID: 90C53
Mathematics Subject Classification ID: 90C06
zbMATH DE Number: 6306863
Wikidata QID: Q58916376
describes a project that uses: GALAHAD
describes a project that uses: SNOPT
describes a project that uses: L-BFGS
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/478407
OpenAlex ID: W2032089608
cites work: A New Algorithm for Unconstrained Optimization
cites work: Q3840111
cites work: Numerical Optimization
cites work: Trust Region Methods
cites work: Q3609088
cites work: Global Convergence of a a of Trust-Region Methods for Nonconvex Minimization in Hilbert Space
cites work: A trust region algorithm for equality constrained optimization
cites work: A modified BFGS method and its global convergence in nonconvex minimization
cites work: Cubic regularization of Newton method and its global performance
cites work: Global convergence of a modified BFGS-type method for unconstrained non-convex minimization
cites work: Recursive Trust-Region Methods for Multiscale Nonlinear Optimization
cites work: On the Complexity of Steepest Descent, Newton's and Regularized Newton's Methods for Nonconvex Unconstrained Optimization Problems
cites work: Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
cites work: A structured trust region method for nonconvex programming with separable structure
cites work: Updating Quasi-Newton Matrices with Limited Storage
cites work: On the limited memory BFGS method for large scale optimization
cites work: Representations of quasi-Newton matrices and their use in limited memory methods
cites work: A survey of quasi-Newton equations and quasi-Newton methods for optimization
cites work: A compact limited memory method for large scale unconstrained optimization
cites work: A subspace limited memory quasi-Newton algorithm for large-scale nonlinear bound constrained optimization
cites work: A limited-memory multipoint symmetric secant method for bound constrained optimization
cites work: SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization
cites work: Q3640943
cites work: Testing Unconstrained Optimization Software
cites work: GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization
description: scientific article; zbMATH DE number 6306863