Nonlinear Optimization

A CLASS OF PRECONDITIONERS FOR LARGE INDEFINITE LINEAR SYSTEMS, AS BY PRODUCTOF KRYLOV SUBSPACE METHODS: PART 1
An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization

Starting from the paper by Nash and Sofer (1990), we propose a heuristic adaptive truncation criterionfor the inner iterations within linesearch-based truncated Newton methods. Our aim is to possibly avoid ‘‘over-solving’’ of the Newton equation, based on a comparison between the predicted...
Guest editorial
Discrete-time velocity control of redundant robots with acceleration/torque optimization properties
Global optimization issues in deep network regression: an overview
Iterative computation of negative curvature directions in large scale optimization
Preconditioning strategies for nonlinear conjugate gradient methods, based on quasi-Newton updates

This paper reports two proposals of possible preconditioners for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. On one hand, the common idea of our preconditioners is inspired to L-BFGS quasi–Newton updates, on the other hand we aim at explicitly...
ERICE 2010 - Nonlinear Optimization - Special Issue of Computational optimization and Applications
A novel class of approximate inverse preconditioners for large positive definite linear systems in optimization

We propose a class of preconditioners for large positive definite linear systems, arising in nonlinear optimization frameworks. These preconditioners can be computed as by-product of Krylov-subspace solvers. Preconditioners in our class are chosen by setting the values of some user-dependent...
A truncated Newton method for constrained optimization