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* Add an alias for the Xpress command line solver.

* Implement a solver that uses the XPRESS Python API.

* Added XPRESS_PY solver to tests

* Fix casting of control value: last attempt should change a string.

* Fix checking for flush messages.

* Mention warmstart support in documentation.

* Support warmstart also for non-MIP.

* Fix handling of infeasible cases.

If there is no solution then `getlpsol()` and/or `getmipsol()` will
raise an exception.

* Expect that `XPRESS_PY` errors on duplicate names.

* Map XPRESS exceptions to PulpSolverError.

* Formatted xpress_api.py to pass PR checks

* Add support SOS constraints.

* Fixed handling of SOS constraints.

- There was a typo in the code that handles SOS.
- If there are SOS constraints, then the model is a MIP from XPRESS'
  point of view.
- In the console we should use `LPOPTIMIZE` and `MIPOPTIMIZE` rather
  than `MINIM`/`MAXIM` followed by `GLOBAL`. The latter approach
  seems to have problems with models that contain SOSs but no integer
  variables.

* Adapt expected results for `XPRESS_CMD`.

This interface is an alias for `XPRESS` and thus behaves the same.

* Get back things that were list during rebase.

* Fix formatting.

* Remove unused argument.

* Rename `_xprs` attribute to `solverModel` to align with documentation.

* Split `_solve()` into `callSolver()` and `findSolutionValues()`.

This aligns the code with the documentation.

* Align solution proocess to documentation.

Solving a model is now performed in three steps:
- self.buildSolverModel()
- self.callSolver()
- self.findSolutionValues()

This aligns the code to the way how adding 3rd party solvers is
documented.

* Change definition of alias.

* Fix some problems that the reviewer found.

Co-authored-by: Chris Brown <cbrown@fico.com>
ff8d48f

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pulp

https://travis-ci.org/coin-or/pulp.svg?branch=master

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.

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 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

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

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.