semvec/pypi-packages huggingface dataset:
flipy 0.0.5
FreeWheel Linear Programming Interface for Python

# Flipy

![flipy_logo_60pt](https://user-images.githubusercontent.com/1311594/73421313-6cea4500-42f3-11ea-9ea7-25a0af70ac4f.png)

[![supported](https://img.shields.io/pypi/pyversions/flipy.svg)](https://pypi.python.org/pypi/flipy/)
[![Build Status](http://img.shields.io/travis/freewheel/flipy.svg?style=flat-square)](https://travis-ci.com/freewheel/flipy) 
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Flipy is a Python linear programming interface library, originally developed by [FreeWheel](https://freewheel.com). It currently supports Gurobi and CBC as the backend solver.

To use Gurobi, make sure you have a Gurobi license file, and gurobipy is installed in your Python environment. You can find details from [Gurobi’s documentation](https://www.gurobi.com/documentation/8.1/quickstart_mac/the_gurobi_python_interfac.html).

Flipy requires Python 3.6 or newer.

## Installation

The latest offical version of Flipy can be installed with `pip`:


```
pip install flipy
```

The latest development version can be get with Git:

```
git clone https://github.com/freewheel/flipy.git
cd flipy
python setup.py install
```

## Quickstart

Here is a simple example for Flipy:

```python
import flipy

# 1 <= x <= 3.5
x = flipy.LpVariable('x', low_bound=1, up_bound=3.5)
# 2 <= y <= 4
y = flipy.LpVariable('y', low_bound=2, up_bound=4)

# 5x + y <= 12
lhs = flipy.LpExpression('lhs', {x: 2.5, y: 1})
rhs = flipy.LpExpression('rhs', constant=12) 
constraint = flipy.LpConstraint(lhs, 'leq', rhs)

# maximize: 3x + 2y
objective = flipy.LpObjective('test_obj', {x: 3, y: 2}, sense=flipy.Maximize)
problem = flipy.LpProblem('test', objective, [constraint])

solver = flipy.CBCSolver()
status = solver.solve(problem)
```

## Get the solution 

After solving, a status is returned to indicate whether the solver has found a optimal solution for the problem:

```python
print(status)
# <SolutionStatus.Optimal: 1>
```

The objective value can be retrieved with `objective.evaluate()`:

```python
print(objective.evaluate())
# 17.6
```

The value of variables can be retrieved with `.evaluate()` as well:

```python
print(x.evaluate())
# 3.2
print(y.evaluate())
# 4.0
```