A new regularized limited memory BFGS-type method based on modified secant conditions for unconstrained optimization problems

DOI10.1007/s10898-015-0310-7zbMath1337.90043OpenAlexW893842695MaRDI QIDQ897051

N. E. Zubov

Publication date: 16 December 2015

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-015-0310-7

zbMATH Keywords

unconstrained optimizationlarge-scale optimizationL-BFGS methodmodified quasi-Newton equationhybridize genetic algorithm

Mathematics Subject Classification ID

Large-scale problems in mathematical programming (90C06) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (5)

A regularized limited memory BFGS method for large-scale unconstrained optimization and its efficient implementations ⋮ Global convergence of a family of modified BFGS methods under a modified weak-Wolfe-Powell line search for nonconvex functions ⋮ A regularized limited memory subspace minimization conjugate gradient method for unconstrained optimization ⋮ A new subspace minimization conjugate gradient method based on modified secant equation for unconstrained optimization ⋮ An improved Dai-Kou conjugate gradient algorithm for unconstrained optimization

Uses Software

• L-BFGS
• CUTEr
• LBFGS-B

Cites Work

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