# PuLP

PuLP is an LP modeler written in python. PuLP can generate MPS or LP files and call GLPK, COIN CLP/CBC, CPLEX, and GUROBI to solve linear problems.

PuLP requires Python >= 2.5. Or with some extra requirements Python2.4 can work.

PuLP uses bzr and launchpad for development and is found on https://launchpad.net/pulp-or

## Installation

The examples require at least a solver in your PATH or a shared library file. CoinMP is bundled with the CheeseShop install.

Install pulp by following the instructions on http://www.coin-or.org/PuLP/ or use easy_install as follows

```\$ easy_install -U pulp
```

```\$ bzr branch lp:pulp-or pulp-or
```

Coin-or hosts the svn archive for PuLP so to download from coin

```\$ svn co https://projects.coin-or.org/svn/PuLP/stable/x.y pulp
```

## Usage

Comprehensive documentation can be found at http://www.coin-or.org/PuLP/ or http://pulp-or.googlecode.com/

In python when PuLP has been installed

```>>> import pulp
```

Use pulp.LpVariable() to create new variables. To create a variable 0 <= x <= 3

```>>> x = pulp.LpVariable("x", 0, 3)
```

To create a variable 0 <= y <= 1

```>>> y = pulp.LpVariable("y", 0, 1)
```

Use pulp.LpProblem() to create new problems. Create "myProblem"

```>>> prob = pulp.LpProblem("myProblem", pulp.LpMinimize)
```

Combine variables to create expressions and constraints and add them to the problem.

```>>> prob += x + y <= 2
```

If you add an expression (not a constraint), it will become the objective.

```>>> prob += -4*x + y
```

Solve a problem

```>>> status = prob.solve()
```

Or choose a solver and solve the problem.

```>>> status = prob.solve(GLPK(msg = 0))
```

Display the status of the solution

```>>> pulp.LpStatus[status]
'Optimal'
```

You can get the value of the variables using value(). ex:

```>>> pulp.value(x)
2.0
```

Exported Classes:

• LpProblem -- Container class for a Linear programming problem
• LpVariable -- Variables that are added to constraints in the LP
• LpConstraint -- A constraint of the general form a1x1+a2x2 ...anxn (<=, =, >=) b
• LpConstraintVar -- Used to construct a column of the model in column-wise modelling

Exported Functions:

• value() -- Finds the value of a variable or expression
• lpSum() -- given a list of the form [a1*x1, a2x2, ..., anxn] will construct a linear expression to be used as a constraint or variable
• lpDot() --given two lists of the form [a1, a2, ..., an] and [ x1, x2, ..., xn] will construct a linear epression to be used as a constraint or variable