Nonlinear Optimization

Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines. We propose a decomposition algorithmic scheme suitable to parallel...
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...
Guest Editorial - Erice 2007 Nonlinear Optimization - Special Issue of COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
Motion control of redundant robots under joint constraints: Saturation in the null space
Nonlinear Optimization: a Bridge from Theory to Applications
An unconstrained minimization method for solving low-rank SDP relaxations of the maxcut problem
AN APPROXIMATE INVERSE PRECONDITIONER IN TRUNCATED NEWTON METHODS FOR LARGE SCALE OPTIMIZATION
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...
Quasi-Newton Updates for Preconditioned Nonlinear Conjugate Gradient Methods
A Reverse Priority approach to multi-task control of redundant robots