Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

In this work, we propose a simple modification of the forward-backward splitting method for
finding a zero in the sum of two monotone operators. Our method converges under the same …

Variational inequalities have recently attracted considerable interest in machine learning as
a flexible paradigm for models that go beyond ordinary loss function minimization (such as …

We propose mS2GD: a method incorporating a mini-batching scheme for improving the
theoretical complexity and practical performance of semi-stochastic gradient descent …

Finding a fixed point to a nonexpansive operator, ie, x^*=Tx^*, abstracts many problems in
numerical linear algebra, optimization, and other areas of data science. To solve fixed-point …

We propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by
Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual …

We propose stochastic variance reduced algorithms for solving convex-concave saddle
point problems, monotone variational inequalities, and monotone inclusions. Our framework …

This paper examines the long-run behavior of learning with bandit feedback in non-
cooperative concave games. The bandit framework accounts for extremely low-information …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

Convergent sequences of real numbers play a fundamental role in many different problems
in system theory, eg, in Lyapunov stability analysis, as well as in optimization theory and …