In this paper, we consider a stochastic distributed nonconvex optimization problem with the cost function being over n agents having access only to zeroth-order (ZO) information of cost. This has various machine learning applications. As solution, propose two ZO algorithms, in which at each iteration agent samples local oracle points time-varying smoothing parameter. We show that proposed algor...

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve problems with only noisy objective function evaluations. Toward this, our main contribution propose estimators of gradient and Hessian from evaluations, based on a version Gaussian smoothing technique. The proposed overcome difficulty nonlinearity manifold constr...

We consider the problem of minimizing a smooth, Lipschitz, convex function over compact, set using sub-zeroth-order oracles: an oracle that outputs sign directional derivative for given point and direction, compares values pair points, noisy value point. show sample complexity optimization these oracles is polynomial in relevant parameters. The algorithm we provide comparator first with known r...

Let D be a digraph with vertex set V(D) .For vertex v V(D), the degree of v, denoted by d(v), is defined as the minimum value if its out-degree  and its in-degree . Now let D be a digraph with minimum degree  and edge-connectivity If  is real number, then the zeroth-order general Randic index is defined by   .  A digraph is maximally edge-connected if . In this paper we present sufficient condi...

In this paper, we propose a class of penalty methods with stochastic approximation for solving stochastic nonlinear programming problems. We assume that only noisy gradients or function values of the objective function are available via calls to a stochastic first-order or zeroth-order oracle. In each iteration of the proposed methods, we minimize an exact penalty function which is nonsmooth an...