Please feel free to email Dr. Gupta if you do not find the code you are looking for. 

Optimization and Learning
  • Combinatorial Instances for Quantum Benchmarking (CI-QuBe)
  • Walking in the Shadow: This code is available for use on Github. It contains an implementation of our algorithms Shadow-Walk and Shadow-CG and benchmarking experiments for a comparison with Away-step FW, Decomposition Invariant Conditional Gradient, Pairwise FW and Projected Gradient Descent. Details of the experiments can be found in the following paper: 
    Hassan Mortagy, Swati Gupta, and Sebastian Pokutta. “Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization.” arXiv preprint arXiv:2006.08426 (2020).
  • MQLib: This code is available for use on Github. It contains:
    • An implementation of dozens of heuristics for the Max-cut and QUBO combinatorial optimization problems.
    • A machine learning-based hyper-heuristic that tries to select the best heuristic for a given instance.
    • Scripts to evaluate heuristics on Amazon EC2 and to analyze the results.

    This library and the related systematic heuristic evaluation strategy are described in the paper:
    “Dunning, Iain, Swati Gupta, and John Silberholz. “What works best when? A systematic evaluation of heuristics for Max-Cut and QUBO.” INFORMS Journal on Computing 30.3 (2018): 608-624.”

Fairness in Applied OR
  • Equity in Facility Location: This code is available for use on Github. It contains all the scripts for our experimental case study on Equity in Facility Location and the potential closure of the Alta Bates Medical Center.