A Truncated Newton Algorithm for Large Scale Box Constrained Optimization

Authors:
Francisco Facchinei;Stefano Lucidi;Laura Palagi
Affiliations:
-;-;-
Venue:
SIAM Journal on Optimization
Year:
2002

Citing 0
Cited 6

On Affine-Scaling Interior-Point Newton Methods for Nonlinear Minimization with Bound Constraints

Computational Optimization and Applications
An interior-point affine-scaling trust-region method for semismooth equations with box constraints

Computational Optimization and Applications
Modified subspace limited memory BFGS algorithm for large-scale bound constrained optimization

Journal of Computational and Applied Mathematics
An active set quasi-Newton method with projected search for bound constrained minimization

Computers & Mathematics with Applications
An active set feasible method for large-scale minimization problems with bound constraints

Computational Optimization and Applications
An active set truncated Newton method for large-scale bound constrained optimization

Computers & Mathematics with Applications

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Abstract

A method for the solution of minimization problems with simple bounds is presented. Global convergence of a general scheme requiring the approximate solution of a single linear system at each iteration is proved and a superlinear convergence rate is established without requiring the strict complementarity assumption. The algorithm proposed is based on a simple, smooth unconstrained reformulation of the bound constrained problem and may produce a sequence of points that are not feasible. Numerical results and comparison with existing codes are reported.