In the envisioned smart grid, high penetration of uncertain renewables, unpredictable
participation of (industrial) customers, and purposeful manipulation of smart meter readings …

We consider a family of algorithms that successively sample and minimize simple stochastic
models of the objective function. We show that under reasonable conditions on …

In this paper, we generalize the well-known Nesterov's accelerated gradient (AG) method,
originally designed for convex smooth optimization, to solve nonconvex and possibly …

In view of the minimization of a nonsmooth nonconvex function f, we prove an abstract
convergence result for descent methods satisfying a sufficient-decrease assumption, and …

The proximal gradient algorithm for minimizing the sum of a smooth and nonsmooth convex
function often converges linearly even without strong convexity. One common reason is that …

In this paper we develop a randomized block-coordinate descent method for minimizing the
sum of a smooth and a simple nonsmooth block-separable convex function and prove that it …

This paper studies the problem of simultaneously aligning a batch of linearly correlated
images despite gross corruption (such as occlusion). Our method seeks an optimal set of …

Many classic and contemporary face recognition algorithms work well on public data sets,
but degrade sharply when they are used in a real recognition system. This is mostly due to …

This paper considers an important class of convex programming (CP) problems, namely, the
stochastic composite optimization (SCO), whose objective function is given by the …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …