Python implementation of algorithms and experiments described in Algorithms and Learning for Fair Portfolio Design by Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, and Juba Ziani

Suggested citation: coming soon

Usage: python experiments_paper.py e1 e2 e3... where the e's are the experiments to run.

E.g. python experiments_paper.py 1 4 11 2 will run Experiments 1, 2, 4, and 11.

• Experiment 1: No regret convergence time as a function of the step size.
• Experiment 2: Comparison of ILP, sparsify, greedy, greedy MM
• Experiment 3: Groups are drawn from gaussians with the same mean, different variance
• Experiment 4: Groups are drawn from gaussians with different means, same variance
• Experiment 5: Groups are drawn from gaussians, the second's mean and variance are scaled multiples of the first
• Experiment 6: One group is relatively small compared to the other
• Experiment 7: Generalization
• Experiment 8: Examining sparsification
• Experiment 9: Comparison of algorithms (Figure 2)
• Experiment 10: Product dependence (+timing)
• Experiment 11: Consumer dependence (+timing)
• Experiment 12: Generalization (Figure 3)

Edit configs.py to change important global variables, including the solver used by CVXPY. Currently supported: Gurobi and Mosek; other values will result in the default solvers being called.

Required packages are listed in requirements.txt and installed with pip install -r requirements.txt.