6 papers · 1 filter
De-risking solutions to optimization problems
Daniel Bienstock, Blake Sisson
We develop a cutting-plane methodology that adjusts solutions to optimization problems so as to reduce features that bring about exposure to risk, such as concentration of assets o…
Probabilistic Modeling versus Robust Optimization: A tutorial based on a humanitarian logistics use case
Justin Kilb, Daniel Bienstock, Alexandra M. Newman
This tutorial contrasts probabilistic modeling and robust optimization to determine decisions in humanitarian logistics, specifically supply chains subject to adversarial (natural…
Advanced Cutting-Plane Algorithms for ACOPF
Daniel Bienstock, Matias Villagra
We propose a disciplined, numerically stable, and scalable approach to SDP relaxations of the ACOPF problem based on linear cutting-planes. Our method can be warm-started and, owin…
Solving convex QPs with structured sparsity under indicator conditions
Daniel Bienstock, Tongtong Chen
We study convex optimization problems where disjoint blocks of variables are controlled by binary indicator variables that are also subject to conditions, e.g., cardinality. Severa…
Accurate Linear Cutting-Plane Relaxations for ACOPF
Daniel Bienstock, Matias Villagra
We present a pure linear cutting-plane relaxation approach for rapidly proving tight and accurate lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF) and it…
Accurate and Warm-Startable Linear Cutting-Plane Relaxations for ACOPF
Daniel Bienstock, Matias Villagra
We present a linear cutting-plane relaxation approach that rapidly proves tight lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF). Our method leverages ou…