46 citations · 414 across the 52 of their papers we have counts for
14 papers · 1 filter
Solution of Large-Scale Supply Chain Models using Graph Sampling & Coarsening
Jiaze Ma, Victor M. Zavala
We present a graph sampling and coarsening scheme (gSC) for computing lower and upper bounds for large-scale supply chain models. An edge sampling scheme is used to build a low-com…
New Measures for Shaping Trajectories in Dynamic Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We propose a new class of measures for shaping time-dependent trajectories in dynamic optimization (DO). The proposed measures are analogous to risk measures used in stochastic opt…
A Unifying Modeling Abstraction for Infinite-Dimensional Optimization
Joshua L. Pulsipher, Weiqi Zhang, Tyler J. Hongisto +1
Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional d…
Spatio-Temporal Economic Properties of Multi-Product Supply Chains
Philip A. Tominac, Weiqi Zhang, Victor M. Zavala
In this work, we analyze the spatio-temporal economic properties of multi-product supply chains. Specifically, we interpret the supply chain as a coordinated market in which stakeh…
Measuring and Optimizing System Reliability: A Stochastic Programming Approach
Joshua L. Pulsipher, Victor M. Zavala
We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subjec…
A Scalable Stochastic Programming Approach for the Design of Flexible Systems
Joshua L. Pulsipher, Victor M. Zavala
We study the problem of designing systems in order to minimize cost while meeting a given flexibility target. Flexibility is attained by enforcing a joint chance constraint, which…
A Computational Framework for Quantifying and Analyzing System Flexibility
Joshua L. Pulsipher, Daniel Rios, Victor M. Zavala
We present a computational framework for analyzing and quantifying system flexibility. Our framework incorporates new features that include: general uncertainty characterizations t…
A Mixed-Integer Conic Programming Formulation for Computing the Flexibility Index under Multivariate Gaussian Uncertainty
Joshua L. Pulsipher, Victor M. Zavala
We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility i…
Remunerating Space-Time, Load-Shifting Flexibility from Data Centers in Electricity Markets
Weiqi Zhang, Victor M. Zavala
We study an electricity market clearing formulation that seeks to remunerate spatio-temporal, load-shifting flexibility provided by data centers (DaCes). Load-shifting flexibility…
On the Tightness of the Lagrangian Dual Bound for Alternating Current Optimal Power Flow
Weiqi Zhang, Kibaek Kim, Victor M. Zavala
We study tightness properties of a Lagrangian dual (LD) bound for the nonconvex alternating current optimal power flow (ACOPF) problem. We show an LD bound that can be computed in…
The Euler Characteristic: A General Topological Descriptor for Complex Data
Alexander Smith, Victor Zavala
Datasets are mathematical objects (e.g., point clouds, matrices, graphs, images, fields/functions) that have shape. This shape encodes important knowledge about the system under st…
Controllability and Observability Imply Exponential Decay of Sensitivity in Dynamic Optimization
Sungho Shin, Victor M. Zavala
We study a property of dynamic optimization (DO) problems (as those encountered in model predictive control and moving horizon estimation) that is known as exponential decay of sen…
Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications
Shengli Jiang, Victor M. Zavala
In this paper we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: i) highlighting connections with techniques from statistics, signal proc…
Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
Sungho Shin, Mihai Anitescu, Victor M. Zavala
We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs. These NLPs arise in many applications such as dynamic optimization, stochastic o…