This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

The purpose of this survey is to serve both as a gentle introduction and a coherent overview
of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for …

This article investigates distributed online convex optimization in the presence of an
aggregative variable without any global/central coordinators over a multiagent network. In …

In many online learning problems the computational bottleneck for gradient-based methods
is the projection operation. For this reason, in many problems the most efficient algorithms …

We study a class of online convex optimization problems with long-term budget constraints
that arise naturally as reliability guarantees or total consumption constraints. In this general …

We present new efficient\emph {projection-free} algorithms for online convex optimization
(OCO), where by projection-free we refer to algorithms that avoid computing orthogonal …

In this paper, we propose three online algorithms for submodular maximization. The first
one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from …

Motivated by online decision-making in time-varying combinatorial environments, we study
the problem of transforming offline algorithms to their online counterparts. We focus on …

We present a novel framework to train a large deep neural network (DNN) for only $\textit
{once} $, which can then be pruned to $\textit {any sparsity ratio} $ to preserve competitive …

In this paper, we revisit Stochastic Continuous Submodular Maximization in both offline and
online settings, which can benefit wide applications in machine learning and operations …