Improved Hessian approximations for the limited memory BFGS method

DOI10.1023/A:1019142304382zbMath0949.65063OpenAlexW1638334753MaRDI QIDQ1964059

Mehiddin Al-Baali

Publication date: 7 September 2000

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1019142304382

zbMATH Keywords

algorithmsnumerical experimentsBFGS methodquasi-Newton methodslarge scale optimizationHessian approximations

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Methods of successive quadratic programming type (90C55)

Related Items (10)

A new class of supermemory gradient methods ⋮ A new regularized limited memory BFGS-type method based on modified secant conditions for unconstrained optimization problems ⋮ A limited-memory optimization method using the infinitely many times repeated BNS update and conjugate directions ⋮ A novel three-phase trajectory informed search methodology for global optimization ⋮ Memory gradient method with Goldstein line search ⋮ Damped techniques for the limited memory BFGS method for large-scale optimization ⋮ A regularized limited memory BFGS method for nonconvex unconstrained minimization ⋮ Extra-updates criterion for the limited memory BFGS algorithm for large scale nonlinear optimization ⋮ Approximation BFGS methods for nonlinear image restoration ⋮ Diagonal BFGS updates and applications to the limited memory BFGS method

Uses Software

• L-BFGS

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