PuLP 4.0.0a11

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 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, and OR-Tools CP-SAT (via the CPSAT API).

The documentation for PuLP can be found here.

PuLP is part of the COIN-OR project.

Important

CBC is not shipped inside the PuLP package. Older releases bundled a CBC binary and exposed it as PULP_CBC_CMD; that API and the bundled solver are removed. To solve models with CBC through PuLP you should:

• install PuLP with the optional CBC extra: python -m pip install pulp[cbc] (this installs the cbcbox wheel, which provides a CBC executable PuLP can find automatically), or

• install a CBC build yourself and ensure the cbc (or cbc.exe on Windows) executable is on your PATH,

then use the COIN_CMD solver (or call prob.solve() with no arguments when CBC is available; otherwise install another solver such as GLPK or pass an explicit solver). Without CBC or another available solver, the default solve path will raise PulpError: No solver available.

Installation

PuLP requires Python 3.10 or newer.

Recommended: install with CBC support:

python -m pip install pulp[cbc]

Plain python -m pip install pulp installs only the modeler; you must then supply your own CBC on PATH or another solver.

Otherwise follow the download instructions on the PyPi page.

Installing solvers

PuLP can use a variety of solvers. When CBC is available (via pulp[cbc] or cbc on PATH), COIN_CMD is the usual open-source MIP/LP choice and is selected as the default ahead of GLPK. PuLP can also install other solvers via optional PyPI extras (some require a commercial license for running or for large models):

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]
python -m pip install pulp[ortools]
python -m pip install pulp[cylp]
python -m pip install pulp[cbc]

If you want to install all open source solvers (scip, highs, cbc), 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.

Quickstart

Use LpProblem to create a problem, then add variables with add_variable. Create a problem called “myProblem” and a variable x with 0 ≤ x ≤ 3:

from pulp import *
prob = LpProblem("myProblem", LpMinimize)
x = prob.add_variable("x", 0, 3)

To create a binary variable y (values 0 or 1):

y = prob.add_variable("y", cat="Binary")

Combine variables to create expressions and constraints and 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 when installed via pulp[cbc] or cbc on PATH, otherwise another available backend):

status = prob.solve()

If you want to try another solver to solve the problem:

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

To use the OR-Tools CP-SAT solver (install with python -m pip install pulp[ortools]). Every variable must have finite lower and upper bounds; continuous variables are solved on their integer-rounded domain:

from pulp import CPSAT
status = prob.solve(CPSAT(msg=False))

Display the status of the solution:

LpStatus[status]
> 'Optimal'

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

value(x)
> 2.0

Essential Classes

These types form the usual modelling workflow: create an LpProblem, attach LpVariable instances with add_variable, build linear expressions (often with lpSum / lpDot / lpSum_vars / lpSum_vars_coefs), combine them into LpConstraint rows, and call solve.

• LpProblem – Container for an LP or MIP: variables, objective, constraints, and solve API

• LpVariable – A decision variable belonging to one problem; used inside expressions and constraints

• LpAffineExpression – A linear combination of variables and a constant (objectives, constraint bodies, intermediate terms)

• LpConstraint – A single row relating an affine expression to a bound with <=, =, or >=:

  a1x1 + a2x2 + … + anxn (<=, =, >=) b

Useful Functions

• value() – Finds the value of a variable or expression

• lpSum() – Given a list of the form [a1*x1, a2*x2, …, an*xn] 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 expression to be used as a constraint or variable

• lpSum_vars() – Sum of variables with coefficient 1 each (batch construction)

• lpSum_vars_coefs() – Sum of coeff * var pairs from an iterable of (variable, coefficient) (batch construction)

More Examples

Several tutorial are given in documentation and pure code examples are available in examples/ directory .

The examples assume CBC is available (for example after pip install pulp[cbc]). To use other solvers they must be available (installed and accessible). For more information, see the guide on configuring solvers.

For Developers

If you want to install the latest version from GitHub you can run:

python -m pip install -U "pulp[cbc] @ git+https://github.com/coin-or/pulp.git"

Building from source

This version of PuLP includes a Rust extension (pulp._rustcore) that provides the core model, variables, constraints, and expressions. The build uses maturin and requires a Rust toolchain in addition to Python.

Requirements

• Python 3.10 or newer

• Rust (latest stable). Install from https://rustup.rs/

• uv (recommended for install and dev). See the uv documentation for installation.

• OS: Windows, macOS (x86_64, arm64), or Linux (x86_64, arm64). The Rust extension is built for the host platform.

Build steps

From the PuLP root directory, create a virtual environment and install the package in editable mode with dev dependencies:

uv venv
uv pip install --group dev -e .[cbc]

Or with plain pip (maturin will be used automatically by the build backend):

python -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
python -m pip install --upgrade pip
python -m pip install -e ".[cbc]"

Running tests

uv run python -m unittest discover -s pulp/tests -v

Building the documentation

The PuLP documentation is built with Sphinx. Use a virtual environment and the dev install above, then:

cd doc
make html

A folder named html will be created inside doc/build/. Open doc/build/html/index.html in a browser.

Contributing to PuLP

Instructions for making your first contribution to PuLP are given here.

Comments, bug reports, patches and suggestions are very welcome!

• Comments and suggestions: https://github.com/coin-or/pulp/discussions

• PuLP Google Group — community discussion and Q&A

• Bug reports: https://github.com/coin-or/pulp/issues

• Patches: https://github.com/coin-or/pulp/pulls

Copyright and License

PuLP is distributed under an MIT license.

Copyright J.S. Roy, 2003-2005 Copyright Stuart A. Mitchell See the LICENSE file for copyright information.