4.3k citations · 8.6k across the 159 of their papers we have counts for
5 papers · 1 filter
Adversarially Robust Learning for Security-Constrained Optimal Power Flow
Priya L. Donti, Aayushya Agarwal, Neeraj Vijay Bedmutha +2
In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been ex…
A Semismooth Newton Method for Fast, Generic Convex Programming
Alnur Ali, Eric Wong, J. Zico Kolter
We introduce Newton-ADMM, a method for fast conic optimization. The basic idea is to view the residuals of consecutive iterates generated by the alternating direction method of mul…
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
Po-Wei Wang, Wei-Cheng Chang, J. Zico Kolter
In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints. The approach, which we call the Mixing method, is…
Convex programming with fast proximal and linear operators
Matt Wytock, Po-Wei Wang, J. Zico Kolter
We present Epsilon, a system for general convex programming using fast linear and proximal operators. As with existing convex programming frameworks, users specify convex optimizat…
A Fast Algorithm for Sparse Controller Design
Matt Wytock, J. Zico Kolter
We consider the task of designing sparse control laws for large-scale systems by directly minimizing an infinite horizon quadratic cost with an penalty on the feedback con…