Nonlinear Optimization

AN APPROXIMATE INVERSE PRECONDITIONER IN TRUNCATED NEWTON METHODS FOR LARGE SCALE OPTIMIZATION
Quasi-Newton Updates for Preconditioned Nonlinear Conjugate Gradient Methods
Quasi-Newton based preconditioning and damped quasi-Newton schemes for nonlinear conjugate gradient methods

In this paper, we deal with matrix-free preconditioners for nonlinear conjugate gradient (NCG) methods. In particular, we review proposals based on quasi-Newton updates, and either satisfying the secant equation or a secant-like equation at some of the previous iterates. Conditions are given...
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...
Fast redundancy resolution for high-dimensional robots executing prioritized tasks under hard bounds in the joint space
Using SVM to combine global heuristics for the Standard Quadratic Problem
LARGE SCALE UNCONSTRAINED OPTIMIZATION
A nonmonotone GRASP

A greedy randomized adaptive search procedure (GRASP) is an itera- tive multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local...
Preconditioning Newton-Krylov methods in nonconvex large scale optimization
Extracting feasible robot parameters from dynamic coefficients using nonlinear optimization methods

We consider the problem of extracting a complete set of numerical parameters that characterize the robot dynamics, starting from the identified values of dynamic coefficients that linearly parametrize the robot dynamic equations. This information is relevant when realistic dynamic simulations have...