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Convex Optimization under Uncertainty

cvxro 최신버젼 다운로드

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Convex Optimization under Uncertainty
=====================

CVXRO is a package for decision making under uncertainty. It is based on Python, and built on top of [CVXPY](https://www.cvxpy.org). 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.

```python3
import cvxpy as cp
import numpy as np
import cvxro

# 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 = cvxro.UncertainParameter(1, uncertainty_set=cvxro.Ellipsoidal())

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

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

CVXRO is not a solver. It relies upon the open source solvers listed [here](https://www.cvxpy.org/tutorial/solvers/index.html#solve-method-options).

## 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

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

## Citing

If you use CVXRO for published work, we encourage you to cite this [paper](https://arxiv.org/abs/2305.19225).

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