The goal of this paper is to study approaches to bridge the gap between first-order and
second-order type methods for composite convex programs. Our key observations are: (1) Many …

Stochastic gradient method and its variants are simple yet effective for minimizing an expectation
function over a closed convex set. However, none of these methods are applicable to …

In this work, we present a globalized stochastic semismooth Newton method for solving
stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in …

We consider a mini-batch stochastic Bregman proximal gradient method and a mini-batch
stochastic Bregman proximal extragradient method for stochastic convex composite …

In this article, we consider a DC (difference of two convex functions) function approach for
solving joint chance-constrained programs (JCCP), which was first established by Hong et al. (…

This paper considers the problem of minimizing a convex expectation function with a set of
inequality convex expectation constraints. We propose a stochastic augmented Lagrangian-…

In this paper, we first introduce a preconditioned primal-dual gradient algorithm based on
conjugate duality theory. This algorithm is designed to solve composite optimization problem …

In this paper, a new monotonicity, M-monotonicity, is introduced, and the resolvent operator
of an M-monotone operator is proved to be single valued and Lipschitz continuous. With the …

In this work, a subsampled Levenberg-Marquardt algorithm is proposed for solving nonconvex
finite-sum optimization problem. At each iteration, based on subsampled function value, …

The impacts of road congestion charges on traffic safety are often overlooked in evaluating
the benefits of congestion charging practices and searching for traffic safety strategies. This …