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pulp
Advanced tools
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
.. image:: https://travis-ci.org/coin-or/pulp.svg?branch=master :target: https://travis-ci.org/coin-or/pulp .. image:: https://img.shields.io/pypi/v/pulp :target: https://pypi.org/project/PuLP/ :alt: PyPI .. image:: https://img.shields.io/pypi/dm/pulp :target: https://pypi.org/project/PuLP/ :alt: PyPI - Downloads
PuLP is an linear and mixed integer programming modeler written in Python. With PuLP, it is simple to create MILP optimisation problems and solve them with the latest open-source (or proprietary) solvers. PuLP can generate MPS or LP files and call solvers such as GLPK_, COIN-OR CLP/CBC, CPLEX, GUROBI_, MOSEK_, XPRESS_, CHOCO_, MIPCL_, HiGHS_, SCIP_/FSCIP_.
The documentation for PuLP can be found here <https://coin-or.github.io/pulp/>_.
PuLP is part of the COIN-OR project <https://www.coin-or.org/>_.
PuLP requires Python 3.10 or newer.
The easiest way to install PuLP is with pip. If pip is available on your system, type::
python -m pip install pulp
Otherwise follow the download instructions on the PyPi page <https://pypi.python.org/pypi/PuLP>_.
PuLP can use a variety of solvers. The default solver is the COIN-OR CBC solver via the COIN_CMD API. Install CBC with::
python -m pip install pulp[cbc]
If CBC is not available on your system, PuLP falls back to other solvers such as the legacy bundled CBC (PULP_CBC_CMD) or GLPK.
To install other solvers, PuLP offers a quick way to install most solvers via their pypi package (some require a commercial license for running or for running large models)::
python -m pip install pulp[cbc]
python -m pip install pulp[gurobi]
python -m pip install pulp[cplex]
python -m pip install pulp[xpress]
python -m pip install pulp[scip]
python -m pip install pulp[highs]
python -m pip install pulp[copt]
python -m pip install pulp[mosek]
If you want to install all open source solvers (scip, highs, cylp), you can use the shortcut:: python -m pip install pulp[open_py]
For more information on how to install solvers, see the guide on configuring solvers <https://coin-or.github.io/pulp/guides/how_to_configure_solvers.html>_.
Create a problem first, then add variables with add_variable (variables are owned by the model). To create a variable x with 0 ≤ x ≤ 3 and a binary variable y::
from pulp import *
prob = LpProblem("myProblem", LpMinimize)
x = prob.add_variable("x", 0, 3)
y = prob.add_variable("y", cat="Binary")
Combine variables in order to create expressions and constraints, and then add them to the problem.::
prob += x + y <= 2
An expression is a constraint without a right-hand side (RHS) sense (one of =, <= or >=). If you add an expression to a problem, it will become the objective::
prob += -4*x + y
To solve the problem with the default solver (CBC via COIN_CMD when installed with pulp[cbc])::
status = prob.solve()
To solve explicitly with CBC::
status = prob.solve(COIN_CMD(msg=0))
If you want to try another solver 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
LpProblem -- Container class for a Linear or Integer programming problem
LpVariable -- Variables that are added into constraints in the LP problem
LpConstraint -- Constraints of the general form
a1x1 + a2x2 + ... + anxn (<=, =, >=) b
LpConstraintVar -- A special type of constraint for constructing column of the model in column-wise modelling
value() -- Finds the value of a variable or expressionlpSum() -- Given a list of the form [a1x1, a2x2, ..., an*xn] will construct a linear expression to be used as a constraint or variablelpDot() -- Given two lists of the form [a1, a2, ..., an] and [x1, x2, ..., xn] will construct a linear expression to be used as a constraint or variableSeveral tutorial are given in documentation <https://coin-or.github.io/pulp/CaseStudies/index.html>_ and pure code examples are available in examples/ directory <https://github.com/coin-or/pulp/tree/master/examples>_ .
The examples use the default solver (CBC via COIN_CMD when available). To use other solvers they must be available (installed and accessible). For more information on how to do that, see the guide on configuring solvers <https://coin-or.github.io/pulp/guides/how_to_configure_solvers.html>_.
If you want to install the latest version from GitHub you can run::
python -m pip install -U git+https://github.com/coin-or/pulp
The PuLP documentation is built with Sphinx <https://www.sphinx-doc.org>. We recommended using a
virtual environment <https://docs.python.org/3/library/venv.html> to build the documentation locally.
To build, run the following in a terminal window, in the PuLP root directory
::
python3 -m pip install --upgrade pip
pip install --group=dev .
cd doc
make html
A folder named html will be created inside the build/ directory.
The home page for the documentation is doc/build/html/index.html which can be opened in a browser.
Instructions for making your first contribution to PuLP are given here <https://coin-or.github.io/pulp/develop/contribute.html>_.
Comments, bug reports, patches and suggestions are very welcome!
PuLP is distributed under an MIT license.
Copyright J.S. Roy, 2003-2005
Copyright Stuart A. Mitchell
See the LICENSE file for copyright information.
.. _Python: http://www.python.org/
.. _GLPK: http://www.gnu.org/software/glpk/glpk.html .. _CBC: https://github.com/coin-or/Cbc .. _CPLEX: http://www.cplex.com/ .. _GUROBI: http://www.gurobi.com/ .. _MOSEK: https://www.mosek.com/ .. _XPRESS: https://www.fico.com/es/products/fico-xpress-solver .. _CHOCO: https://choco-solver.org/ .. _MIPCL: http://mipcl-cpp.appspot.com/ .. _SCIP: https://www.scipopt.org/ .. _HiGHS: https://highs.dev .. _FSCIP: https://ug.zib.de
FAQs
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.
We found that pulp demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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