01a Articolo in rivista

Novel preconditioners based on quasi–Newton updates for nonlinear conjugate gradient methods

In this paper we study new preconditioners to be used within the nonlinear conjugate gradient (NCG) method, for large scale unconstrained optimization. The rationale behind our proposal draws inspiration from quasi-Newton updates, and its aim is to possibly approximate in some sense the inverse of...
Exploiting damped techniques for nonlinear conjugate gradient methods

In this paper we propose the use of damped techniques within Nonlinear Conjugate Gradient (NCG) methods. Damped techniques were introduced by Powell and recently reproposed by Al-Baali and till now, only applied in the framework of quasi--Newton methods. We extend their use to NCG methods in large...
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 derivative-free approach for a simulation-based optimization problem in healthcare
A simulation-based multiobjective optimization approach for health care service management
Preconditioning Newton-Krylov methods in nonconvex large scale optimization
Preconditioning Large Indefinite Linear Systems
Iterative computation of negative curvature directions in large scale optimization
AN APPROXIMATE INVERSE PRECONDITIONER IN TRUNCATED NEWTON METHODS FOR LARGE SCALE OPTIMIZATION
Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization