Historically, scalability has been a major challenge for the successful application of
semidefinite programming in fields such as machine learning, control, and robotics. In this …

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 …

We provide stronger and more general primal-dual convergence results for Frank-Wolfe-
type algorithms (aka conditional gradient) for constrained convex optimization, enabled by a …

Since its beginning, optimization has played a vital role in data science. The analysis and
solution methods for many statistical and machine learning models rely on optimization. The …

Algorithms in varied fields use the idea of maintaining a distribution over a certain set and
use the multiplicative update rule to iteratively change these weights. Their analyses are …

Semidefinite programming (SDP) is a powerful framework from convex optimization that has
striking potential for data science applications. This paper develops a provably correct …

The main theme of this paper is to develop a novel eigenvalue optimization framework for
learning a Mahalanobis metric. Within this context, we introduce a novel metric learning …

The computational bottleneck in applying online learning to massive data sets is usually the
projection step. We present efficient online learning algorithms that eschew projections in …

Abstract The Frank-Wolfe method (aka conditional gradient algorithm) for smooth
optimization has regained much interest in recent years in the context of large scale …

Optimization problems with a nuclear norm regularization, such as eg low norm matrix
factorizations, have seen many applications recently. We propose a new approximation …