Learning for Optimization under Uncertainty

LROPT is a package for decision making under uncertainty. It is based on Python, and built on top of CVXPY. It allows model optimization problems affected by uncertainty using data.

Example

The following code solves a linear programming problem where the objective is to minimize $c^Tx$, while their constraints belong to an ellipsoidal uncertainty set.

import cvxpy as cp
import numpy as np
import lropt

# Data
A = np.array([1, 2]) 
b = np.array([5])     

# Variables
x = cp.Variable(2)

# Objective
c = np.array([1, 1])
objective = cp.Minimize(c.T @ x)

# Uncertainty parameter
u = lropt.UncertainParameter(1, uncertainty_set=lropt.Ellipsoidal())

# Constraints
constraints = [
    A @ x <= b + u,
    x >= 0
]

# Problem
prob = lropt.RobustProblem(objective, constraints)
prob.solve()

LROPT is not a solver. It relies upon the open source solvers listed here.

Contributing

If you'd like to add a new example to our library, or implement a new feature, please get in touch with us first to make sure that your priorities align with ours.

Team

LROPT is built from the contributions of many researchers and engineers. A list of people who contributed to the development of LROPT include Irina Wang, Amit Solomon, Bartolomeo Stellato, Cole Becker, Bart Van Parys and Manav Jairam.

Citing

If you use LROPT for published work, we encourage you to cite this paper.