cvxpyrepair is a Python package that implements the algorithms described in our paper Automatic repair of convex optimization problems.
To install, run:
pip install .Navigate to the examples folder.
To run the spacecraft landing example:
python spacecraft_landing.py
To run the arbitrage example:
python arbitrage.py
To run the boolean LP example:
python boolean_lp.py
The package has a single API, the repair function, which takes in a CVXPY
problem, a list of parameters, and a regularization function, and
tries to repair the problem by changing the parameters and keeping the
regularization function small.
The repair function has the signature:
def repair(prob, params, r=None, verbose=True, maxiter=10, maxiter_pgm=25,
lam=1, lam_factor=2, lr=.1):
"""
Repairs prob by altering params.
Minimizes r(params) subject to the constraint that prob is solvable.
Args:
- prob: cvxpy Problem object
- params: list of cvxpy Parameters involved in prob
- r (optional): callable that takes a list of cvxpy Variables as input
(of the same dimension as params), and returns a cvxpy expression
representing the performance metric (default=None).
- verbose (optional): whether or not to print diagnostic informtion (default=True).
- maxiter (optional): Maximum number of outer iterations (default=10).
- maxiter_pgm (optional): Maximum number of inner iterations (default=25).
- lam (optional): Starting value for 1/lambda, multiplied by lam_factor
each iteration (default=1).
- lam_factor (optional): Factor to multiply lambda by each iteration (default=2).
- lr (optional): initial step size for proximal gradient method (default=.1).
"""
This repository carries an Apache 2.0 license.
If you use cvxpyrepair in your research, please consider citing our accompanying paper:
@article{barratt2020automatic,
author={Barratt, S. and Angeris, G. and Boyd, S.},
title={Automatic Repair of Convex Optimization Problems},
year={2020},
journal={Manuscript}
}