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#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 >= 2.5.
The examples use the default solver (cbc), to use other solvers they must be available.
#Documentation
Documentation is found on https://pythonhosted.org/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 and 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/
#pulp [![Build Status](https://travis-ci.org/stumitchell/pulp-or.svg?branch=feature%2Ftravis)](https://travis-ci.org/stumitchell/pulp-or)
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 >= 2.5.
The examples use the default solver (cbc), to use other solvers they must be available.
#Documentation
Documentation is found on https://pythonhosted.org/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 and 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/