<p style='text-indent:20px;'>The aim of this paper is to study a wide class non-convex sweeping processes with moving constraint whose translation and deformation are represented by regulated functions, i. e., functions not necessarily bounded variation admitting one-sided limits at every point. Assuming that the time-dependent uniformly prox-regular has non-empty interior, we prove exist...

The preferential CO oxidation (so-called CO-PROX) is the selective amid H2-rich atmospheres, a process where ceria-based materials are consolidated catalysts. This article aims to disentangle potential CO–H2 synergism under CO-PROX conditions on low-index ceria surfaces (111), (110) and (100). Polycrystalline ceria, nanorods nanocubes were prepared assess physicochemical features of targeted su...

We present a fast accelerated prox-level (FAPL) method for large scale ball constrained and unconstrained convex optimization. It achieves optimal iteration complexity in theory, and reduces computation time and increases accuracy significantly in practice. This is accomplished by reducing the number of sub-problems involved in most existing bundle level type methods, and the novel algorithm to...

B. S. MORDUKHOVICH and T. T. A. NGHIA Abstract. The paper introduces and characterizes new notions of Lipschitzian and Hölderian full stability of solutions to general parametric variational systems described via partial subdifferential of prox-regular functions acting in finite-dimensional and Hilbert spaces. These notions, postulated certain quantitative properties of single-valued localizati...

This paper is concerned with the study of a class of prox-penalization methods for solving variational inequalities of the form Ax + NC(x) 3 0 where H is a real Hilbert space, A : H ⇒ H is a maximal monotone operator and NC is the outward normal cone to a closed convex set C ⊂ H. Given Ψ : H → R ∪ {+∞} which acts as a penalization function with respect to the constraint x ∈ C, and a penalizatio...

We present and analyze statistically optimal, communication and memory efficient distributed stochastic optimization algorithms with near-linear speedups (up to log-factors). This improves over prior work which includes methods with near-linear speedups but polynomial communication requirements (accelerated minibatch SGD) and communication efficient methods which do not exhibit any runtime spee...