Metadata-Version: 2.1
Name: PuLP
Version: 2.1
Summary: 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.
Home-page: https://github.com/coin-or/pulp
Author: J.S. Roy and S.A. Mitchell
Author-email: pulp@stuartmitchell.com
License: UNKNOWN
Description: pulp
        **************************
        .. 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[1], COIN CLP/CBC[2], CPLEX[3], and GUROBI[4] to solve linear
        problems.
        
        Installation
        ================
        
        The easiest way to install pulp is via `PyPi <https://pypi.python.org/pypi/PuLP>`_
        
        If pip is available on your system::
        
             pip install pulp
        
        Otherwise follow the download instructions on the PyPi page.
        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.
        
        The examples use the default solver (CBC), to use other solvers they must be available.
        
        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 epression to be used as a constraint or variable
        
        Comments, bug reports, patches and suggestions are welcome.
        pulp-or-discuss@googlegroups.com
        
             Copyright J.S. Roy (js@jeannot.org), 2003-2005
             Copyright Stuart A. Mitchell (stu@stuartmitchell.com)
             See the LICENSE file for copyright information.
        
        References:
        
        [1] http://www.gnu.org/software/glpk/glpk.html
        [2] http://www.coin-or.org/
        [3] http://www.cplex.com/
        [4] http://www.gurobi.com/
         
Keywords: Optimization,Linear Programming,Operations Research
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Mathematics
Description-Content-Type: text/x-rst
