7 citations · 13 across the 8 of their papers we have counts for
8 papers
Conformal Uncertainty Sets for Robust Optimization
Chancellor Johnstone, Bruce Cox
Decision-making under uncertainty is hugely important for any decisions sensitive to perturbations in observed data. One method of incorporating uncertainty into making optimal dec…
Developing a Resilient, Robust and Efficient Supply Network in Africa
Bruce A. Cox, Christopher M. Smith, Timothy W. Breitbach +2
Supply chains need to balance competing objectives; in addition to efficiency they need to be resilient to adversarial and environmental interference, and robust to uncertainties i…
Decomposition of Large Scale Linear Programming Problems Containing both Linking Variables and Constraints via Accuracy Certificates
Bruce A. Cox
Several well known large scale linear programming decomposition methodologies exist. Benders Decomposition, which covers the case where some small subset of variables link the othe…
Comparing Greedy Constructive Heuristic Subtour Elimination Methods for the Traveling Salesman Problem
Petar D. Jackovich, Bruce A. Cox, Raymond R. Hill
This paper further defines the class of fragment constructive heuristics used to compute feasible solutions for the Traveling Salesman Problem into arc-greedy and node-greedy subcl…
Effects of Aggregation Methodology on Uncertain Spatiotemporal Data
Zachary T. Hornberger, Bruce A. Cox, Raymond R. Hill
Large spatiotemporal demand datasets can prove intractable for location optimization problems, motivating the need to aggregate such data. However, demand aggregation introduces er…
Optimal Heterogeneous Asset Location Modeling for Expected Spatiotemporal Search and Rescue Demands using Historic Event Data
Zachary T. Hornberger, Bruce A. Cox, Brian J. Lunday
The United States Coast Guard is charged with the coordination of all search and rescue missions in maritime regions within the United States purview. Given the size of the Pacific…
Decomposition Techniques for Bilinear Saddle Point Problems and Variational Inequalities with Affine Monotone Operators on Domains Given by Linear Minimization Oracles
Bruce Cox, Anatoli Juditsky, Arkadi Nemirovski
The majority of First Order methods for large-scale convex-concave saddle point problems and variational inequalities with monotone operators are proximal algorithms which at every…
Dual subgradient algorithms for large-scale nonsmooth learning problems
Bruce Cox, Anatoli Juditsky, Arkadi Nemirovski
"Classical" First Order (FO) algorithms of convex optimization, such as Mirror Descent algorithm or Nesterov's optimal algorithm of smooth convex optimization, are well known to ha…