New Subspace Method for Unconstrained Derivative-Free Optimization

Abstract

This paper defines an efficient subspace method, called SSDFO , for unconstrained derivative-free optimization problems where the gradients of the objective function are Lipschitz continuous but only exact function values are available. SSDFO employs line searches along directions constructed on the basis of quadratic models. These approximate the objective function in a subspace spanned by some previous search directions. A worst case complexity bound on the number of iterations and function evaluations is derived for a basic algorithm using this technique. Numerical results for a practical variant with additional heuristic features show that, on the unconstrained CUTEst test problems, SSDFO has superior performance compared to the best solvers from the literature.