An assessment of nonmonotone linesearch techniques for unconstrained optimization

Authors:
Philippe L. Toint
Affiliations:
-
Venue:
SIAM Journal on Scientific Computing
Year:
1996

Citing 0
Cited 25

Numerical Experiences with New Truncated Newton Methodsin Large Scale Unconstrained Optimization

Computational Optimization and Applications
Numerical Experience with a Reduced Hessian Methodfor Large Scale Constrained Optimization

Computational Optimization and Applications
On the nonmonotone line search

Journal of Optimization Theory and Applications
Global convergence of nonmonotone descent methods for unconstrained optimization problems

Journal of Computational and Applied Mathematics - Special issue: Papers presented at the 1st Sino--Japan optimization meeting, 26-28 October 2000, Hong Kong, China
A nonmonotone trust region method based on nonincreasing technique of weighted average of the successive function values

Journal of Computational and Applied Mathematics
Incorporating nonmonotone strategies into the trust region method for unconstrained optimization

Computers & Mathematics with Applications
A new nonmonotone line search technique for unconstrained optimization

Journal of Computational and Applied Mathematics
Global convergence of the nonmonotone MBFGS method for nonconvex unconstrained minimization

Journal of Computational and Applied Mathematics
Numerical research on the sensitivity of nonmonotone trust region algorithms to their parameters

Computers & Mathematics with Applications
A new family of conjugate gradient methods

Journal of Computational and Applied Mathematics
A modified SQP method with nonmonotone technique and its global convergence

Computers & Mathematics with Applications
A nonmonotone quasi-Newton trust-region method of conic model for unconstrained optimization

Optimization Methods & Software
A globally convergent BFGS method with nonmonotone line search for non-convex minimization

Journal of Computational and Applied Mathematics
A new nonmonotone trust-region method of conic model for solving unconstrained optimization

Journal of Computational and Applied Mathematics
A Nonmonotone trust region method with adaptive radius for unconstrained optimization problems

Computers & Mathematics with Applications
Nonmonotone second-order Wolfe's line search method for unconstrained optimization problems

Computers & Mathematics with Applications
Computing Points that Satisfy Second Order Necessary Optimality Conditions for Unconstrained Minimization

SIAM Journal on Optimization
A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation

SIAM Journal on Scientific Computing
Modified nonmonotone Armijo line search for descent method

Numerical Algorithms
Nonmonotone algorithm for minimization on closed sets with applications to minimization on Stiefel manifolds

Journal of Computational and Applied Mathematics
Non-monotone projection gradient method for non-negative matrix factorization

Computational Optimization and Applications
An efficient nonmonotone trust-region method for unconstrained optimization

Numerical Algorithms
Modified subspace Barzilai-Borwein gradient method for non-negative matrix factorization

Computational Optimization and Applications
A modified ODE-based algorithm for unconstrained optimization problems

Numerical Algorithms
A nonmonotone trust region method with new inexact line search for unconstrained optimization

Numerical Algorithms

Quantified Score

Hi-index	0.01

Visualization

Abstract

The purpose of this paper is to discuss the potential of nonmonotone techniques for enforcing convergence of unconstrained minimization algorithms from starting points distant from the solution. Linesearch-based algorithms are considered for both small and large problems, and extensive numerical experiments show that this potential is sometimes considerable. A new variant is introduced in order to limit some of the identified drawbacks of the existing techniques. This variant is again numerically tested and appears to be competitive. Finally, the impact of preconditioning on the considered methods is examined.