Nonlinear Optimization

An Augmented Lagrangian Method Exploiting an Active-Set Strategy and Second-Order Information
Trust-region methods for the derivative-free optimization of nonsmooth black-box functions
A Class of Approximate Inverse Preconditioners Based on Krylov-Subspace Methods for Large-Scale Nonconvex Optimization

We introduce a class of positive definite preconditioners for the solution of large symmetric indefinite linear systems or sequences of such systems, in optimization frameworks. The preconditioners are iteratively constructed by collecting information on a reduced eigenspace of the indefinite...
A new branch-and-bound algorithm for standard quadratic programming problems

In this paper we propose convex and LP bounds for standard quadratic programming (StQP) problems and employ them within a branch-and-bound approach. We first compare different bounding strategies for StQPs in terms both of the quality of the bound and of the computation times. It turns out that the...
A multi-objective DIRECT algorithm for ship hull optimization
Issues on the use of a modified Bunch and Kaufman decomposition for large scale Newton’s equation

In this work, we deal with Truncated Newton methods for solving large scale (possibly nonconvex) unconstrained optimization problems. In particular,we consider the use of amodified Bunch and Kaufman factorization for solving the Newton equation, at each (outer) iteration of the method. The Bunch...
An algorithmic framework based on primitive directions and nonmonotone line searches for black-box optimization problems with integer variables
Iterative Grossone-Based Computation of Negative Curvature Directions in Large-Scale Optimization

We consider an iterative computation of negative curvature directions, in large-scale unconstrained optimization frameworks, needed for ensuring the convergence toward stationary pointswhich satisfy second-order necessary optimality conditions. We show that to the latter purpose, we can fruitfully...
A derivative-free approach to constrained multiobjective nonsmooth optimization
Improving ADMMs for solving doubly nonnegative programs through dual factorization

Alternating direction methods of multipliers (ADMMs) are popular approaches to handle large scale semidefinite programs that gained attention during the past decade. In this paper, we focus on solving doubly nonnegative programs (DNN), which are semidefinite programs where the elements of the...