In many contemporary optimization problems such as those arising in machine learning, it can be computationally challenging or even infeasible to evaluate an entire function its derivatives. This motivates the use of stochastic algorithms that sample problem data, which jeopardize guarantees obtained through classical globalization techniques optimization, a line search. Using subsampled values...

We propose a new class of rigorous methods for derivative-free optimization with the aim delivering efficient and robust numerical performance functions all types, from smooth to non-smooth, under different noise regimes. To this end, we have developed methods, called Full-Low Evaluation organized around two main types iterations. The first iteration type (called Full-Eval) is expensive in func...