A python Linear Programming API
.. image:: https://travis-ci.org/coin-or/pulp.svg?branch=master :target: 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, COIN-OR CLP/
CBC, CPLEX, GUROBI, MOSEK, XPRESS, CHOCO, MIPCL, SCIP_ to solve linear problems.
The easiest way to install pulp is via
If pip is available on your system::
python -m pip install pulp
Otherwise follow the download instructions on the PyPi page.
If you want to install the latest version from github you can run the following::
python -m pip install -U git+https://github.com/coin-or/pulp
On Linux and OSX systems the tests must be run to make the default solver executable.
See the examples directory for examples.
PuLP requires Python 2.7 or Python >= 3.4.
The examples use the default solver (CBC). 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_.
Documentation is found on https://coin-or.github.io/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, then 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
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
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
The PuLP documentation is built with
Sphinx. We recommended using a
virtual environmentto build the documentation locally.
To build, run the following in a terminal window, in the PuLP root directory
python -m pip install -r requirements-dev.txt doc/make html
A folder named pulp-or-docs will be created in the same folder as the PuLP root directory. The home page for the documentation is pulp-or-docs/html/index.html which can be opened in a browser.
Comments, bug reports, patches and suggestions are welcome.
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/