A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

This work addresses decentralized online optimization in nonstationary environments. A
network of agents aim to track the minimizer of a global, time-varying, and convex function …

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 …

We develop the mathematical foundations of the stochastic modified equations (SME)
framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

We study gradient-based optimization methods obtained by directly discretizing a second-
order ordinary differential equation (ODE) related to the continuous limit of Nesterov's …

We consider the problem of sampling from constrained distributions, which has posed
significant challenges to both non-asymptotic analysis and algorithmic design. We propose …

Most of the recent successful applications of neural networks have been based on training
with gradient descent updates. However, for some small networks, other mirror descent …

In view of solving convex optimization problems with noisy gradient input, we analyze the
asymptotic behavior of gradient-like flows under stochastic disturbances. Specifically, we …

This article is concerned with the multiagent optimization problem. A distributed randomized
gradient-free mirror descent (DRGFMD) method is developed by introducing a randomized …