62 citations · 151 across the 15 of their papers we have counts for
6 papers · 1 filter
Scalable Semidefinite Programming
Alp Yurtsever, Joel A. Tropp, Olivier Fercoq +2
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct ra…
An Optimal-Storage Approach to Semidefinite Programming using Approximate Complementarity
Lijun Ding, Alp Yurtsever, Volkan Cevher +2
This paper develops a new storage-optimal algorithm that provably solves generic semidefinite programs (SDPs) in standard form. This method is particularly effective for weakly con…
Frank-Wolfe Style Algorithms for Large Scale Optimization
Lijun Ding, Madeleine Udell
We introduce a few variants on Frank-Wolfe style algorithms suitable for large scale optimization. We show how to modify the standard Frank-Wolfe algorithm using stochastic gradien…
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Song Zhou, Swati Gupta, Madeleine Udell
The original simplicial method (OSM), a variant of the classic Kelley's cutting plane method, has been shown to converge to the minimizer of a composite convex and submodular objec…
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
Alp Yurtsever, Madeleine Udell, Joel A. Tropp +1
This paper concerns a fundamental class of convex matrix optimization problems. It presents the first algorithm that uses optimal storage and provably computes a low-rank approxima…
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
Damek Davis, Brent Edmunds, Madeleine Udell
We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex,…