invo - An Inverse Optimization Library

Setup

Invo is available on PyPi, so you can just pip install it.

pip install invo

Invo uses numpy, scipy, and cvxpy as dependencies, so you may need to install them first.

Usage

For more detailed documentation, refer to our ReadTheDocs

An invo problem has two stages. You first define a forward model, and then you solve the corresponding inverse optimization problem. Currently, we assume forward problems are given in inequality form as follows:

import numpy as np
from invo.LinearModels import AbsoluteDualityGap

# Construct a random forward problem.
vertices = [ np.random.rand(4) for i in range(8) ]
from invo.utils.fwdutils import fit_convex_hull
A, b = fit_convex_hull(vertices)

# Construct a set of optimal observed decisions.
optimalPoints = [ np.random.rand(4) for i in range(4) ]


# Add the forward problem, then run inverse optimization.
model = AbsoluteDualityGap()
model.FOP(A, b)
model.solve(optimalPoints)
print (model.c)

Currently completed

• Absolute Duality Gap linear model
• Relative Duality Gap linear model
• pNorm linear model