documentation update for installation
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Stuart Mitchell
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95
INSTALL
95
INSTALL
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@ -4,26 +4,26 @@ Installation
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Note that to install PuLP you must first have a working python installation as
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described in `installing python`_.
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PuLP requires Python >= 2.5. Though it can be made to work with Python 2.4
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PuLP requires Python >= 2.6.
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The latest version of PuLP can be freely obtained from coin-or_.
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The latest version of PuLP can be freely obtained from github_.
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Please note that this version of PuLP has not been tested with operating systems
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other than Microsoft Windows and Ubuntu Linux.
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Easy install and pypi installation
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Pip and pypi installation
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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By far the easiest way to install pulp is through the use of EasyInstall_ and
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By far the easiest way to install pulp is through the use of pip_ and
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CheeseShop_.
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* Install EasyInstall
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* In windows (please make sure easy_install is on your path)::
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* Install pip
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* In windows (please make sure pip is on your path)::
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c:\Python26\Scripts\> easy_install -U pulp
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c:\Python26\Scripts\> pip install pulp
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* In Linux::
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$ sudo easy_install -U pulp
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$ sudo pip install pulp
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$ sudo pulptest #needed to get the default solver to work
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* Then follow the instructions below to test your installation
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@ -70,18 +70,10 @@ Linux Installation
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$ sudo apt-get install glpk
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* install gurobi_ (free academic licenses)
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* install cplex_ (and pay $$)
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* or compile coinMP_ and pulp from source using buildout and copy the files
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.. code-block:: sh
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$ python bootstrap.py
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$ bin/buildout -c solvers.cfg
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$ cp parts/lib/* src/pulp/
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$ sudo setup.py install
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* install cplex_ (also a license for academics)
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.. _glpk: http://www.gnu.org/software/glpk/
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.. _CoinMP: http://projects.coin-or.org/CoinMP
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.. _CBC: https://projects.coin-or.org/Cbc
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.. _cplex: http://cplex.com
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.. _gurobi: http://gurobi.com
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@ -90,38 +82,49 @@ Testing your PuLP installation
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To test that that you pulp installation is working correctly please type the
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following into a python interpreter and note that the output should be similar.
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The output below is what you would expect if you have not installed any other
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solvers and the CoinMP_ solver bundled with pulp works.
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solvers and the CBC_ solver bundled with pulp works.
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>>> import pulp
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>>> pulp.pulpTestAll()
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Solver pulp.pulp.COIN_MEM unavailable.
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Solver pulp.pulp.COIN_CMD unavailable.
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Testing continuous LP solution
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Testing maximize continuous LP solution
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Testing unbounded continuous LP solution
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Testing MIP solution
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Testing MIP relaxation
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Testing feasibility problem (no objective)
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Testing an infeasible problem
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Testing an integer infeasible problem (Error to be fixed)
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Testing column based modelling
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Testing column based modelling with empty constraints
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Testing dual variables and slacks reporting
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Testing resolve of problem
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Testing Sequential Solves
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Testing fractional constraints
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Testing elastic constraints (no change)
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Testing elastic constraints (freebound)
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Testing elastic constraints (penalty unchanged)
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Testing elastic constraints (penalty unbounded)
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* Solver pulp.pulp.COINMP_DLL passed.
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Solver pulp.pulp.GLPK_MEM unavailable.
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Solver pulp.pulp.GLPK_CMD unavailable.
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Solver pulp.pulp.XPRESS unavailable.
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Testing zero subtraction
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Testing continuous LP solution
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Testing maximize continuous LP solution
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Testing unbounded continuous LP solution
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Testing Long Names
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Testing repeated Names
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Testing zero constraint
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Testing zero objective
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Testing LpVariable (not LpAffineExpression) objective
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Testing Long lines in LP
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Testing LpAffineExpression divide
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Testing MIP solution
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Testing MIP relaxation
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Testing feasibility problem (no objective)
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Testing an infeasible problem
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Testing an integer infeasible problem
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Testing column based modelling
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Testing dual variables and slacks reporting
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Testing fractional constraints
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Testing elastic constraints (no change)
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Testing elastic constraints (freebound)
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Testing elastic constraints (penalty unchanged)
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Testing elastic constraints (penalty unbounded)
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* Solver pulp.solvers.PULP_CBC_CMD passed.
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Solver pulp.solvers.CPLEX_DLL unavailable
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Solver pulp.solvers.CPLEX_CMD unavailable
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Solver pulp.solvers.CPLEX_PY unavailable
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Solver pulp.solvers.COIN_CMD unavailable
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Solver pulp.solvers.COINMP_DLL unavailable
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Solver pulp.solvers.GLPK_CMD unavailable
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Solver pulp.solvers.XPRESS unavailable
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Solver pulp.solvers.GUROBI unavailable
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Solver pulp.solvers.GUROBI_CMD unavailable
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Solver pulp.solvers.PYGLPK unavailable
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Solver pulp.solvers.YAPOSIB unavailable
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.. _`installing python`: http://www.diveintopython.org/installing_python/index.html
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.. _coin-or: https://projects.coin-or.org/PuLP
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.. _EasyInstall: http://pypi.python.org/pypi/setuptools
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.. _github: https://github.com/stumitchell/pulp-or
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.. _pip: https://pypi.python.org/pypi/pip
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.. _CheeseShop: http://pypi.python.org
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.. _`PuLP zipfile`: http://www.coin-or.org/download/source/PuLP/
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.. _`PuLP zipfile`: https://github.com/stumitchell/pulp-or/archive/master.zip
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@ -158,4 +158,4 @@ We can make our code return all the solutions by editing our code as shown after
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.. literalinclude:: ../../../examples/Sudoku2.py
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:lines: 82-115
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The full file using this is available `Sudoku2.py <https://projects.coin-or.org/PuLP/browser/trunk/examples/Sudoku2.py?format=txt>`_. When using this code for sudoku problems with a large number of solutions, it could take a very long time to solve them all. To create sudoku problems with multiple solutions from unique solution sudoku problem, you can simply delete a starting number constraint. You may find that deleting several constraints will still lead to a single optimal solution but the removal of one particular constraint leads to a sudden dramatic increase in the number of solutions.
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The full file using this is available `Sudoku2.py <https://github.com/stumitchell/pulp-or/raw/master/examples/Sudoku2.py>`_. When using this code for sudoku problems with a large number of solutions, it could take a very long time to solve them all. To create sudoku problems with multiple solutions from unique solution sudoku problem, you can simply delete a starting number constraint. You may find that deleting several constraints will still lead to a single optimal solution but the removal of one particular constraint leads to a sudden dramatic increase in the number of solutions.
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