We present a probabilistic language model for time-stamped text data which tracks the
semantic evolution of individual words over time. The model represents words and contexts …

This book, as the title suggests, is about first-order methods, namely, methods that exploit
information on values and gradients/subgradients (but not Hessians) of the functions …

We consider derivative-free algorithms for stochastic and nonstochastic convex optimization
problems that use only function values rather than gradients. Focusing on nonasymptotic …

This paper considers a class of constrained stochastic composite optimization problems
whose objective function is given by the summation of a differentiable (possibly nonconvex) …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

To make decisions optimally is a basic human desire. Whenever the situation and the
objectives can be described quantitatively, this desire can be satisfied, to some extent, by …

The mirror descent algorithm (MDA) was introduced by Nemirovsky and Yudin for solving
convex optimization problems. This method exhibits an efficiency estimate that is mildly …

We study accelerated mirror descent dynamics in continuous and discrete time. Combining
the original continuous-time motivation of mirror descent with a recent ODE interpretation of …

In this paper we present a new approach for constructing subgradient schemes for different
types of nonsmooth problems with convex structure. Our methods are primal-dual since they …

4 Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A
Survey Page 1 4 Incremental Gradient, Subgradient, and Proximal Methods for Convex …