Nonlinear Optimization

Guest Editorial - Erice 2007 Nonlinear Optimization - Special Issue of COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
Nonlinear Optimization: a Bridge from Theory to Applications
A truncated Newton method for constrained optimization
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...
AN APPROXIMATE INVERSE PRECONDITIONER IN TRUNCATED NEWTON METHODS FOR LARGE SCALE OPTIMIZATION
Optimal redundancy resolution with task scaling under hard bounds in the robot joint space
Quasi-Newton Updates for Preconditioned Nonlinear Conjugate Gradient Methods
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...
Using SVM to combine global heuristics for the Standard Quadratic Problem
Preconditioned nonlinear conjugate gradient methods based on a modified secant equation

This paper includes a twofold result for the Nonlinear Conjugate Gradient (NCG) method, in large scale unconstrained optimization. First we consider a theoretical analysis, where preconditioning is embedded in a strong convergence framework of an NCG method from the literature. Mild conditions to...