46 citations · 414 across the 52 of their papers we have counts for
9 papers · 1 filter
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
Alexander Engelmann, Sungho Shin, François Pacaud +1
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of i…
New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization
Leonardo D. González, Victor M. Zavala
Bayesian optimization (BO) is one of the most effective methods for closed-loop experimental design and black-box optimization. However, a key limitation of BO is that it is an inh…
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
David Cole, Sungho Shin, François Pacaud +2
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method red…
A Julia Framework for Graph-Structured Nonlinear Optimization
David L Cole, Sungho Shin, Victor Zavala
Graph theory provides a convenient framework for modeling and solving structured optimization problems. Under this framework, the modeler can arrange/assemble the components of an…
Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links
Weiqi Zhang, Philip A. Tominac, Victor M. Zavala
Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility. While the United States government aims to promote E…
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
Weiqi Zhang, Line A. Roald, Victor M. Zavala
Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. How…
Data Analysis using Riemannian Geometry and Applications to Chemical Engineering
Alexander Smith, Benjamin Laubach, Ivan Castillo +1
We explore the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices (SPD). An SPD matrix is a versatile data representation that is common…
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom +1
We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We sho…
Random Field Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of r…