A hybrid ODE-based method for unconstrained optimization problems

DOI10.1007/s10589-012-9455-1zbMath1259.90139OpenAlexW2044204225MaRDI QIDQ694546

Guan-shu Wang, Yi-gui Ou

Publication date: 12 December 2012

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-012-9455-1

zbMATH Keywords

unconstrained optimizationglobal convergenceODE-based methodsfixed step-lengthIMPBOT algorithm

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Related Items (4)

A nonmonotone ODE-based method for unconstrained optimization ⋮ A hybrid method for solving systems of nonsmooth equations with box constraints ⋮ A nonmonotone supermemory gradient algorithm for unconstrained optimization ⋮ Differential evolution using a superior-inferior crossover scheme

Uses Software

• L-BFGS
• minpack

Cites Work

• A nonmonotone trust region method for unconstrained optimization
• Subspace methods for large scale nonlinear equations and nonlinear least squares
• An ODE-based trust region method for unconstrained optimization problems
• On the limited memory BFGS method for large scale optimization
• Combining trust-region techniques and Rosenbrock methods to compute stationary points
• Some effective methods for unconstrained optimization based on the solution of systems of ordinary differential equations
• Representations of quasi-Newton matrices and their use in limited memory methods
• A quasi-Newton trust-region method
• The convergence of subspace trust region methods
• A subspace implementation of quasi-Newton trust region methods for unconstrained optimization
• R-linear convergence of the Barzilai and Borwein gradient method
• The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization Problem
• Two-Point Step Size Gradient Methods
• Testing Unconstrained Optimization Software
• Trust Region Algorithms and Timestep Selection
• Benchmarking optimization software with performance profiles.
• Global convergence of conjugate gradient methods without line search
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