In this paper, we consider a stochastic distributed nonconvex optimization problem with the
cost function being distributed over n agents having access only to zeroth-order (ZO) …

We consider smooth stochastic convex optimization problems in the context of algorithms
which are based on directional derivatives of the objective function. This context can be …

We study the theoretical convergence properties of random-search methods when
optimizing non-convex objective functions without having access to derivatives. We prove …

We consider an unconstrained problem of minimizing a smooth convex function which is
only available through noisy observations of its values, the noise consisting of two parts …

We address black-box convex optimization problems, where the objective and constraint
functions are not explicitly known but can be sampled within the feasible set. The challenge …

We consider solving a class of unconstrained optimization problems in which only stochastic
estimates of the objective functions are available. Existing stochastic optimization methods …

This paper addresses black-box smooth optimization problems, where the objective and
constraint functions are not explicitly known but can be queried. The main goal of this work is …

In this paper, we propose a derivative-free Levenberg-Marquardt algorithm for nonlinear
least squares problems, where the Jacobian matrices are approximated via orthogonal …

Zeroth-order (ZO) optimization has been the key technique for various machine learning
applications especially for black-box adversarial attack, where models need to be learned in …

Zeroth-order (ZO) optimization is one key technique for machine learning problems where
gradient calculation is expensive or impossible. Several variance, reduced ZO proximal …