A new regularized limited memory BFGS-type method based on modified secant conditions for unconstrained optimization problems
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Publication:897051
DOI10.1007/s10898-015-0310-7zbMath1337.90043OpenAlexW893842695MaRDI QIDQ897051
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
unconstrained optimizationlarge-scale optimizationL-BFGS methodmodified quasi-Newton equationhybridize genetic algorithm
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
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