140 citations · 317 across the 8 of their papers we have counts for
5 papers · 1 filter
Assessing the Effectiveness of (Parallel) Branch-and-bound Algorithms
Stephen J. Maher, Ted K. Ralphs, Yuji Shinano
Empirical studies are fundamental in assessing the effectiveness of implementations of branch-and-bound algorithms. The complexity of such implementations makes empirical study dif…
A Branch-and-Cut Algorithm for Mixed Integer Bilevel Linear Optimization Problems and Its Implementation
Sahar Tahernejad, Ted K. Ralphs, Scott T. DeNegre
In this paper, we describe a comprehensive algorithmic framework for solving mixed integer bilevel linear optimization problems (MIBLPs) using a generalized branch-and-cut approach…
A Unified Framework for Multistage and Multilevel Mixed Integer Linear Optimization
Suresh Bolusani, Stefano Coniglio, Ted. K. Ralphs +1
We introduce a unified framework for the study of multilevel mixed integer linear optimization problems and multistage stochastic mixed integer linear optimization problems with re…
A Framework for Generalized Benders' Decomposition and Its Application to Multilevel Optimization
Suresh Bolusani, Ted K. Ralphs
We describe a framework for reformulating and solving optimization problems that generalizes the well-known framework originally introduced by Benders. We discuss details of the ap…
On the Complexity of Inverse Mixed Integer Linear Optimization
Aykut Bulut, Ted K. Ralphs
Inverse optimization is the problem of determining the values of missing input parameters for an associated forward problem that are closest to given estimates and that will make a…