59 citations · 82 across the 4 of their papers we have counts for
7 papers
Machine Learning for AC Optimal Power Flow
Neel Guha, Zhecheng Wang, Matt Wytock +1
We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and eng…
Dynamic Energy Management
Nicholas Moehle, Enzo Busseti, Stephen Boyd +1
We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optim…
A New Architecture for Optimization Modeling Frameworks
Matt Wytock, Steven Diamond, Felix Heide +1
We propose a new architecture for optimization modeling frameworks in which solvers are expressed as computation graphs in a framework like TensorFlow rather than as standalone pro…
Probabilistic Segmentation via Total Variation Regularization
Matt Wytock, J. Zico Kolter
We present a convex approach to probabilistic segmentation and modeling of time series data. Our approach builds upon recent advances in multivariate total variation regularization…
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…
Contextually Supervised Source Separation with Application to Energy Disaggregation
Matt Wytock, J. Zico Kolter
We propose a new framework for single-channel source separation that lies between the fully supervised and unsupervised setting. Instead of supervision, we provide input features f…
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…