Alessandra Papini Direct Search algorithms are classical derivative-free methods for optimization. Though endowed with solid theoretical properties, they are not well suited for large-scale problems due to slow convergence and scaling issues. In this paper, we discuss how such limitations can be circumvented, on problems for which a hierarchy of objective functions is available, by using multilevel … Read more
Many optimization problems of practical interest arise from the discretization of continuous problems. Classical examples can be found in calculus of variations, optimal control and image processing. In recent years a number of strategies have been proposed for the solution of such problems, broadly known as multilevel methods. Inspired by classical multigrid schemes for linear … Read more
We propose a Generating Search Set method for solving the oblique l_1 Procrustes problem. Implementative details, algorithmic choices and theoretical properties of the method are discussed. The results of some numerical experiments are reported. Citation in Applied and Industrial Mathematics in Italy III – Proceedings of the 9th Conference SIMAI, De Bernardis et. Al. (eds), … Read more
We consider a direct search approach for solving nonsmooth minimization problems where the objective function is locally Lipschitz continuous and piecewise continuously differentiable on a finite family of polyhedra. A generating set search method is proposed, which is named “structured” because the structure of the set of nondifferentiability near the current iterate is exploited to … Read more
We propose a pattern search method to solve a classical nonsmooth optimization problem. In a deep analogy with pattern search methods for linear constrained optimization, the set of search directions at each iteration is defined in such a way that it conforms to the local geometry of the set of points of nondifferentiability near the … Read more