PuLP 1.6.10

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.

Modifications Copyright (c) 2007- Stuart Anthony Mitchell ([e-mail])

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Description: # pulp ](https://travis-ci.org/coin-or/pulp)

PuLP is an LP modeler written in python. PuLP can generate MPS or LP files and call GLPK[1], COIN CLP/CBC[2], CPLEX[3], and GUROBI[4] to solve linear problems.

## Installation

The easiest way to install pulp is via [PyPi](https://pypi.python.org/pypi/PuLP)

If pip is available on your system

$pip install pulp

Otherwise follow the download instructions on the PyPi page On Linux and OSX systems the tests must be run to make the default solver executable.

$sudo pulptest

## Examples

See the examples directory for examples.

PuLP requires Python >= 2.6.

The examples use the default solver (cbc), to use other solvers they must be available.

# Documentation Documentation is found on https://pythonhosted.org/PuLP/.

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

>>> x = LpVariable("x", 0, 3)

To create a variable 0 <= y <= 1

>>> y = LpVariable("y", 0, 1)

Use LpProblem() to create new problems. Create “myProblem”

>>> prob = LpProblem("myProblem", 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

To solve with the default included solver

>>> status = prob.solve()

To use another sovler to solve the problem.

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

Display the status of the solution

>>> LpStatus[status]
'Optimal'

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

>>> 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

Comments, bug reports, patches and suggestions are welcome. [e-mail]

Copyright J.S. Roy ([e-mail]), 2003-2005 Copyright Stuart A. Mitchell ([e-mail]) See the LICENSE file for copyright information.

References: [1] http://www.gnu.org/software/glpk/glpk.html [2] http://www.coin-or.org/ [3] http://www.cplex.com/ [4] http://www.gurobi.com/

Keywords: Optimization,Linear Programming,Operations Research Platform: UNKNOWN Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: BSD License Classifier: Natural Language :: English Classifier: Programming Language :: Python Classifier: Topic :: Scientific/Engineering :: Mathematics Description-Content-Type: text/markdown