To help you get started, we've collected the most common ways that PuLP is being used within popular public projects.

pulp / pulpcore / test / unit / test_repo_controller.py View on Github

def add_success_result(self, repo_id, offset):
        started = datetime.datetime.now(dateutils.local_tz())
        completed = started + datetime.timedelta(days=offset)
        r = RepoSyncResult.expected_result(repo_id, 'foo', 'bar', dateutils.format_iso8601_datetime(started), dateutils.format_iso8601_datetime(completed), 1, 1, 1, '', '', RepoSyncResult.RESULT_SUCCESS)
        RepoSyncResult.get_collection().save(r, safe=True)

View more ways to use PuLP

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, HiGHS, SCIP/FSCIP to solve linear problems.

Installation

The easiest way to install pulp is via PyPi

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.

sudo pulptest

Examples

See the examples directory for examples.

PuLP requires Python 3.7 or newer.

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

Documentation is found on .

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

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 expression to be used as a constraint or variable

Building the documentation

The PuLP documentation is built with Sphinx. We recommended using a virtual environment to build the documentation locally.

To build, run the following in a terminal window, in the PuLP root directory

cd pulp
python -m pip install -r requirements-dev.txt
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.

Comments, bug reports, patches and suggestions are welcome.

• Comments and suggestions:

• Bug reports:

• Patches:

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