Limited-Memory Reduced-Hessian Methods for Large-Scale Unconstrained Optimization

DOI10.1137/S1052623497319973zbMath1046.65045MaRDI QIDQ4441962

Philip E. Gill, Michael W. Leonard

Publication date: 19 January 2004

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

zbMATH Keywords

unconstrained optimizationnumerical resultsBFGS methodconjugate-direction methodsBroyden-Fletcher-Goldfarb-Shanno quasi-Newton methodsreduced-Hessian methods

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53)

Related Items (13)

Damped techniques for enforcing convergence of quasi-Newton methods ⋮ A new class of supermemory gradient methods ⋮ On efficiently combining limited-memory and trust-region techniques ⋮ New cautious BFGS algorithm based on modified Armijo-type line search ⋮ Iterative diagonalization of symmetric matrices in mixed precision and its application to electronic structure calculations ⋮ A combined class of self-scaling and modified quasi-Newton methods ⋮ A limited memory descent Perry conjugate gradient method ⋮ New BFGS method for unconstrained optimization problem based on modified Armijo line search ⋮ A robust implementation of a sequential quadratic programming algorithm with successive error restoration ⋮ Exact linesearch limited-memory quasi-Newton methods for minimizing a quadratic function ⋮ Null-Space Preconditioners for Saddle Point Systems ⋮ Shifted limited-memory variable metric methods for large-scale unconstrained optimization ⋮ Variable metric methods for unconstrained optimization and nonlinear least squares

Uses Software

• L-BFGS-B
• SNOPT
• L-BFGS
• CUTEr
• TOLMIN

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