`pulp`: Pulp classes¶

`LpProblem`([name, sense])	An LP Problem
`LpVariable`(name[, lowBound, upBound, cat, e])	This class models an LP Variable with the specified associated parameters
`LpAffineExpression`([e, constant, name])	A linear combination of `LpVariables`.
`LpConstraint`([e, sense, name, rhs])	An LP constraint
`LpConstraint.makeElasticSubProblem`(*args, ...)	Builds an elastic subproblem by adding variables to a hard constraint
`FixedElasticSubProblem`(constraint[, ...])	Contains the subproblem generated by converting a fixed constraint $\sum_{i}a_i x_i = b$ into an elastic constraint.

Todo

LpFractionConstraint, FractionElasticSubProblem

The LpProblem Class¶

class pulp.LpProblem(name='NoName', sense=1)¶

Bases: object

An LP Problem

Creates an LP Problem

This function creates a new LP Problem with the specified associated parameters

Parameters:	name – name of the problem used in the output .lp file sense – of the LP problem objective. Either `LpMinimize` (default) or `LpMaximize`.
Returns:	An LP Problem

Three important attributes of the problem are:

objective¶: The objective of the problem, an LpAffineExpression

constraints¶: An ordered dictionary of constraints of the problem - indexed by their names.

status¶: The return status of the problem from the solver.

Some of the more important methods:

solve(solver=None, **kwargs)¶

Solve the given Lp problem.

This function changes the problem to make it suitable for solving then calls the solver.actualSolve() method to find the solution

Parameters:	solver – Optional: the specific solver to be used, defaults to the default solver.

Side Effects:

The attributes of the problem object are changed in actualSolve() to reflect the Lp solution

roundSolution(epsInt=1e-05, eps=1e-07)¶

Rounds the lp variables

Inputs:

none

Side Effects:

The lp variables are rounded

setObjective(obj)¶

Sets the input variable as the objective function. Used in Columnwise Modelling

Parameters:	obj – the objective function of type `LpConstraintVar`

Side Effects:

The objective function is set

writeLP(filename, writeSOS=1, mip=1)¶

Write the given Lp problem to a .lp file.

This function writes the specifications (objective function, constraints, variables) of the defined Lp problem to a file.

Parameters:	filename – the name of the file to be created.

Side Effects:

The file is created.

Variables and Expressions¶

class pulp.LpElement(name)¶: Base class for LpVariable and LpConstraintVar

class pulp.LpVariable(name, lowBound=None, upBound=None, cat='Continuous', e=None)¶

This class models an LP Variable with the specified associated parameters

Parameters:

name – The name of the variable used in the output .lp file
lowbound – The lower bound on this variable’s range. Default is negative infinity
upBound – The upper bound on this variable’s range. Default is positive infinity
cat – The category this variable is in, Integer, Binary or Continuous(default)
e – Used for column based modelling: relates to the variable’s existence in the objective function and constraints

addVariableToConstraints(e)¶: adds a variable to the constraints indicated by the LpConstraintVars in e

classmethod dicts(name, indexs, lowBound=None, upBound=None, cat='Continuous', indexStart=[])¶

Creates a dictionary of LP variables

This function creates a dictionary of LP Variables with the specified: associated parameters.

Parameters:

name – The prefix to the name of each LP variable created
indexs – A list of strings of the keys to the dictionary of LP variables, and the main part of the variable name itself
lowbound – The lower bound on these variables’ range. Default is negative infinity
upBound – The upper bound on these variables’ range. Default is positive infinity
cat – The category these variables are in, Integer or Continuous(default)

Returns:

A dictionary of LP Variables

setInitialValue(val)¶: sets the initial value of the Variable to val may of may not be supported by the solver

Example:

>>> x = LpVariable('x',lowBound = 0, cat='Continuous')
>>> y = LpVariable('y', upBound = 5, cat='Integer')

gives $x \in [0,\infty)$ , $y \in (-\infty, 5]$ , an integer.

class pulp.LpAffineExpression(e=None, constant=0, name=None)¶

Bases: collections.OrderedDict

A linear combination of LpVariables. Can be initialised with the following:

e = None: an empty Expression
e = dict: gives an expression with the values being the coefficients of the keys (order of terms is undetermined)
e = list or generator of 2-tuples: equivalent to dict.items()
e = LpElement: an expression of length 1 with the coefficient 1
e = other: the constant is initialised as e

Examples:

>>> f=LpAffineExpression(LpElement('x'))
>>> f
1*x + 0
>>> x_name = ['x_0', 'x_1', 'x_2']
>>> x = [LpVariable(x_name[i], lowBound = 0, upBound = 10) for i in range(3) ]
>>> c = LpAffineExpression([ (x[0],1), (x[1],-3), (x[2],4)])
>>> c
1*x_0 + -3*x_1 + 4*x_2 + 0

In brief, $\textsf{LpAffineExpression([(x[i],a[i]) for i in I])} = \sum_{i \in I} a_i x_i$ where (note the order):

x[i] is an LpVariable

a[i] is a numerical coefficient.

pulp.lpSum(vector)¶

Calculate the sum of a list of linear expressions

Parameters:	vector – A list of linear expressions

Constraints¶

class pulp.LpConstraint(e=None, sense=0, name=None, rhs=None)¶

Bases: pulp.pulp.LpAffineExpression

An LP constraint

Parameters:	e – an instance of `LpAffineExpression` sense – one of `LpConstraintEQ`, `LpConstraintGE`, `LpConstraintLE` (0, 1, -1 respectively) name – identifying string rhs – numerical value of constraint target

makeElasticSubProblem(*args, **kwargs)¶

Builds an elastic subproblem by adding variables to a hard constraint

uses FixedElasticSubProblem

Elastic Constraints¶

A constraint $C(x) = c$ (equality may be replaced by $\le$ or $\ge$ ) can be elasticized to the form

$C(x) \in D$

where $D$ denotes some interval containing the value $c$ .

Define the constraint in two steps:

instantiate constraint (subclass of LpConstraint) with target $c$ .

call its makeElasticSubProblem() method which returns an object of type FixedElasticSubProblem (subclass of LpProblem) - its objective is the minimization of the distance of $C(x)$ from $D$ .

constraint = LpConstraint(..., rhs = c)
elasticProblem = constraint.makeElasticSubProblem(
                       penalty = <penalty_value>,
                       proportionFreeBound = <freebound_value>,
                       proportionFreeBoundList = <freebound_list_value>,
                       )

where:

<penalty_value> is a real number
<freebound_value> $a \in [0,1]$ specifies a symmetric target interval $D = (c(1-a),c(1+a))$ about $c$
<freebound_list_value> = [a,b], a list of proportions $a, b \in [0,1]$ specifying an asymmetric target interval $D = (c(1-a),c(1+b))$ about $c$

The penalty applies to the constraint at points $x$ where $C(x) \not \in D$ . The magnitude of <penalty_value> can be assessed by examining the final objective function in the .lp file written by LpProblem.writeLP().

Example:

>>> constraint_1 = LpConstraint('ex_1',sense=1,rhs=200)
>>> elasticProblem_1 = constraint_1.makeElasticSubproblem(penalty=1, proportionFreeBound = 0.01)
>>> constraint_2 = LpConstraint('ex_2',sense=0,rhs=500)
>>> elasticProblem_2 = constraint_2.makeElasticSubproblem(penalty=1,
proportionFreeBoundList = [0.02, 0.05])

constraint_1 has a penalty-free target interval of 1% either side of the rhs value, 200
constraint_2 has a penalty-free target interval of - 2% on left and 5% on the right side of the rhs value, 500

Following are the methods of the return-value:

class pulp.FixedElasticSubProblem(constraint, penalty=None, proportionFreeBound=None, proportionFreeBoundList=None)¶

Bases: pulp.pulp.LpProblem

Contains the subproblem generated by converting a fixed constraint $\sum_{i}a_i x_i = b$ into an elastic constraint.

Parameters:

constraint – The LpConstraint that the elastic constraint is based on
penalty – penalty applied for violation (+ve or -ve) of the constraints
proportionFreeBound – the proportional bound (+ve and -ve) on constraint violation that is free from penalty
proportionFreeBoundList – the proportional bound on constraint violation that is free from penalty, expressed as a list where [-ve, +ve]

alterName(name)¶: Alters the name of anonymous parts of the problem

deElasticize()¶: de-elasticize constraint

findDifferenceFromRHS()¶: The amount the actual value varies from the RHS (sense: LHS - RHS)

findLHSValue()¶: for elastic constraints finds the LHS value of the constraint without the free variable and or penalty variable assumes the constant is on the rhs

isViolated()¶: returns true if the penalty variables are non-zero

reElasticize()¶: Make the Subproblem elastic again after deElasticize

Combinations and Permutations¶

pulp.combination(orgset, k=None)¶

returns an iterator that lists the combinations of orgset of length k

Parameters:	orgset – the list to be iterated k – the cardinality of the subsets
Returns:	an iterator of the subsets

example:

>>> c = combination([1,2,3,4],2)
>>> for s in c:
...     print s
(1, 2)
(1, 3)
(1, 4)
(2, 3)
(2, 4)
(3, 4)

pulp.allcombinations(orgset, k)¶

returns all permutations of orgset with up to k items

Parameters:	orgset – the list to be iterated k – the maxcardinality of the subsets
Returns:	an iterator of the subsets

example:

>>> c = allcombinations([1,2,3,4],2)
>>> for s in c:
...     print s
(1,)
(2,)
(3,)
(4,)
(1, 2)
(1, 3)
(1, 4)
(2, 3)
(2, 4)
(3, 4)

pulp.permutation(orgset, k=None)¶

returns an iterator that lists the permutations of orgset of length k

Parameters:	orgset – the list to be iterated k – the cardinality of the subsets
Returns:	an iterator of the subsets

example:

>>> c = permutation([1,2,3,4],2)
>>> for s in c:
...     print s
(1, 2)
(1, 3)
(1, 4)
(2, 1)
(2, 3)
(2, 4)
(3, 1)
(3, 2)
(3, 4)
(4, 1)
(4, 2)
(4, 3)

pulp.allpermutations(orgset, k)¶

returns all permutations of orgset with up to k items

Parameters:	orgset – the list to be iterated k – the maxcardinality of the subsets
Returns:	an iterator of the subsets

example:

>>> c = allpermutations([1,2,3,4],2)
>>> for s in c:
...     print s
(1,)
(2,)
(3,)
(4,)
(1, 2)
(1, 3)
(1, 4)
(2, 1)
(2, 3)
(2, 4)
(3, 1)
(3, 2)
(3, 4)
(4, 1)
(4, 2)
(4, 3)

pulp.value(x)¶: Returns the value of the variable/expression x, or x if it is a number

`pulp`: Pulp classes¶

The LpProblem Class¶

Variables and Expressions¶

Constraints¶

Elastic Constraints¶

Combinations and Permutations¶

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pulp: Pulp classes¶

The LpProblem Class¶

Variables and Expressions¶

Constraints¶

Elastic Constraints¶

Combinations and Permutations¶

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`pulp`: Pulp classes¶