A nonmonotone trust region method based on simple conic models for unconstrained optimization

DOI10.1016/j.amc.2013.09.038zbMath1334.90135OpenAlexW2019541445MaRDI QIDQ275735

Fen Zhou, Fengxue Cao, Qunyan Zhou

Publication date: 26 April 2016

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

Full work available at URL: https://doi.org/10.1016/j.amc.2013.09.038

zbMATH Keywords

unconstrained optimizationglobal convergencenonmonotone trust region methodsimple conic model

Mathematics Subject Classification ID

Abstract computational complexity for mathematical programming problems (90C60) Nonconvex programming, global optimization (90C26)

Related Items (3)

A nonmonotone hybrid method of conjugate gradient and Lanczos-type for solving nonlinear systems ⋮ A new class of conjugate gradient methods for unconstrained smooth optimization and absolute value equations ⋮ Extended Dai-Yuan conjugate gradient strategy for large-scale unconstrained optimization with applications to compressive sensing

Uses Software

• minpack

Cites Work

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