Nonlinear Optimization

Dynamic scaling based preconditioning for truncated Newton methods in large scale unconstrained optimization
Data and performance profiles applying an adaptive truncation criterion, within linesearch-based truncated Newton methods in large scale nonconvex optimization

In this paper, we report data and experiments related to the research article entitled “An adaptive truncation criterion, for linesearch-based truncated Newton methods in large scale nonconvex optimization” by Caliciotti et. Al. [1]. In particular, in [1], large scale unconstrained optimization...
A CLASS OF PRECONDITIONERS FOR LARGE INDEFINTIE LINEAR SYSTEMS, AS BY PRODUCT OF KRYLOV SUBSPACE METHODS: PART 2
Fast redundancy resolution for high-dimensional robots executing prioritized tasks under hard bounds in the joint space
Nonlinear optimization: a bridge from theory to applications
Using SVM to combine global heuristics for the Standard Quadratic Problem
Faster Motion on Cartesian Paths Exploiting Robot Redundancy at the Acceleration Level

The problem of minimizing the transfer time along a given Cartesian path for redundant robots can be approached in two steps, by separating the generation of a joint path associated to the Cartesian path from the exact minimization of motion time under kinematic/dynamic bounds along the obtained...
New Problems and Innovative Methods in Nonlinear Optimization
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...
Guest editorial