lparray#

Module provides objects that can be used to manipulate linear programming problems.

The module provides a set of classes and functions that can be used to manipulate linear programming problems. These objects are:

  • lparray : Numpy array with homogeneous LpVariables, LpAffineExpression or LpConstraints.

  • lp_minmax : Get the minimum or maximum from a list of linear expressions.

  • addAbs: Add absolute value constraints to a linear programming problem.

  • lp_multiply: Multiplication of a binary and continuous variables.

class wip.modules.lparray.HasShape(*args, **kwargs)[source]#

Bases: Protocol

Protocol for objects that have a shape attribute.

This protocol defines a contract that classes can adhere to without having to inherit from a common base class. It’s useful when writing functions or classes that expect an object with a specific attribute, in this case, shape.

Variables

shape (Tuple[int, ]) – Tuple indicating the dimensions of the object. e.g., (2, 3) for a 2x3 matrix.

Notes

The HasShape protocol can be used in type annotations to indicate that a function or method expects an object with a shape attribute.

shape: Tuple[int, ...]#
wip.modules.lparray.add_abs(prob: pulp.LpProblem, var: pulp.LpVariable | pulp.LpAffineExpression, big_m: float | int = 100000, abs_var_name: str | None = None) → pulp.LpVariable[source]#

Create an LP variable with the absolute value of a variable or expression.

This function introduces an auxiliary variable to the linear programming problem that represents the absolute value of a provided variable or expression. It also adds the necessary constraints to ensure this auxiliary variable correctly captures the absolute value.

Parameters

  • prob (pulp.LpProblem) – The optimization problem to which the absolute value variable is added.

  • var (pulp.LpVariable | pulp.LpAffineExpression) – The variable or expression whose absolute value is to be represented.

  • big_m (float | int, default 100000) – A large constant required to create the auxiliary constraints needed to create the variable that equals the absolute value of :param:`var`. The value needs to be greater than any value that :param:`var` can have.

  • abs_var_name (str | None, optional) – The name for the absolute value variable. If None, a name is generated automatically.

Returns

The auxiliary variable representing the absolute value of the provided variable or expression.

Return type

pulp.LpVariable

Examples

The following example demonstrates how the add_abs() can be used, to find the absolute value for the x variable, that has a range of: -10 \leq{} \text{x} \leq{} 0:

>>> prob = pulp.LpProblem("MyProblem", sense=pulp.LpMaximize)
>>> x = pulp.LpVariable("x", lowBound=-10, upBound=0)
>>> abs_x = add_abs(prob, x)
>>> prob.setObjective(abs_x)

>>> print(pulp.LpStatus[prob.solve(pulp.PULP_CBC_CMD(msg=False))])
'Optimal'

>>> print(f"Objective Value: {prob.objective.value():.0f}")
'Objective Value: 10'

>>> for name, lpvar in prob.variablesDict().items():
...     print(f"{name} = {lpvar.value():.0f}")
abs(x) = 10
x = -10
binary_var(x) = 0
wip.modules.lparray.count_out(iterable: Iterable[Any]) → List[int][source]#

Return indices of items in the given iterable.

Given some iterable, this function generates a list of indices corresponding to each item in the iterable.

Parameters

iterable (Iterable[Any]) – Input iterable of any type.

Returns

List of indices for each item in the input iterable.

Return type

List[int]

Examples

>>> count_out(["a", "b", "c"])
[0, 1, 2]
>>> count_out((1, 2, 3, 4))
[0, 1, 2, 3]
wip.modules.lparray.find_unique_name(prob: LpProblem, name: str) → str[source]#

Find a unique variable name for the problem.

If a variable with the given name exists, append ‘_<number>’ to it, where <number> starts from 1 and increases until a unique name is found.

Parameters

  • prob (pulp.LpProblem) – The problem to check for existing variable names.

  • name (str) – The base name to check for uniqueness.

Returns

A unique variable name.

Return type

str

wip.modules.lparray.lp_define_or_constraint(prob: pulp.LpProblem, lp_expression: pulp.LpVariable | pulp.LpAffineExpression, lp_binary_var: pulp.LpVariable, low_bound: float, up_bound: float)[source]#

Define or constrain a linear programming problem in PuLP.

This function adds constraints to a given linear programming (LP) problem based on a specified LP expression, some specified boundary conditions and a binary variable. It uses the binary variable to impose the lower and upper bounds on the LP expression, or set it to 0, otherwise. The function handles both single pulp.LpVariables and pulp.LpAffineExpressions.

Parameters

  • prob (pulp.LpProblem) – The linear programming problem to which the constraints will be added.

  • lp_expression (pulp.LpVariable | pulp.LpAffineExpression) – The linear programming expression (either a variable or an affine expression) that is to be constrained.

  • lp_binary_var (pulp.LpVariable) – The binary variable to link with the expression, to constraint the expression from :param:`lp_expression` to be equal to 0, when this binary variable is also 0, or a value between :param:`low_bound` and :param:`up_bound` otherwise.

  • low_bound (float) – The lower bound for the LP expression. The expression will be constrained to be greater than or equal to this value times a binary auxiliary variable.

  • up_bound (float) – The upper bound for the LP expression. The expression will be constrained to be less than or equal to this value times a binary auxiliary variable.

Examples

>>> prob = pulp.LpProblem("Example_Problem", pulp.LpMaximize)
>>> x = pulp.LpVariable("x", 0, 3)
>>> xb = pulp.LpVariable("xb", cat=pulp.LpBinary)
>>> lp_define_or_constraint(prob, x, xb, 0.8, 1)

This example adds constraints to the problem ‘prob’ that enforce variable ‘x’ to be between 0.8 and 1 when a certain condition (represented by the binary auxiliary variable) is true.

wip.modules.lparray.lp_minmax(lp_expression, prob: LpProblem, name: str, which, cat, lb=None, ub=None, bigM=1000)[source]#
Parameters

  • prob (LpProblem) –

  • name (str) –

wip.modules.lparray.lp_multiply(bin_lpvar: pulp.LpVariable, cont_lpvar: pulp.LpVariable, prob: pulp.LpProblem, big_m: int | float = 100000) → pulp.LpProblem[source]#

Multiply a binary with a continuous pulp.LpVariable.

Introduces constraints to a given pulp.LpProblem that represent the multiplication between a binary and a continuous pulp.LpVariable, and returns the modified pulp.LpProblem.

Parameters

  • bin_lpvar (pulp.LpVariable) – Binary linear programming variable.

  • cont_lpvar (pulp.LpVariable) – Continuous linear programming variable, which should have both its lower- and upper-bounds set.

  • prob (pulp.LpProblem) – The linear programming problem to which the multiplication constraints will be added.

  • big_m (int | float, default 100_000) – A value that is greater than the lower and upper bounds of cont_lpvar.

Returns

The modified pulp.LpProblem with the added constraints.

Return type

pulp.LpProblem

Examples

To illustrate how to use the function with pulp:

>>> import pulp
>>> prob = pulp.LpProblem("ExampleProblem", pulp.LpMaximize)
>>> bin_var = pulp.LpVariable("binary", 0, 1, pulp.LpBinary)
>>> cont_var = pulp.LpVariable("continuous", 0, 10)
>>> prob = lp_multiply(bin_var, cont_var, prob)
>>> prob += bin_var == 0
>>> prob.setObjective(cont_var)
>>> prob.solve(pulp.PULP_CBC_CMD(msg=False))
>>> print(cont_var.value())
0.0
class wip.modules.lparray.lparray[source]#

Bases: ndarray, Generic[LP]

Numpy array with homogeneous LpVariables, LpAffineExpression or LpConstraints.

All variables in the array will have the same:

  • Lp* type

  • (intrinsic) upper bound

  • (intrinsic) lower bound

Also, all vectorized operations will preserve this invariant. External manipulations that break this invariant will lead to wrong behavior.

All variables in an array are named, and share the same base name, which is extended by indexes into a collection of index sets whose product spans the elements of the array. These index sets can be named or anonymous – anonymous index sets are int ranges.

Implements vectorized versions of various LP of LpConstraint-type lparrays support the constrain method, which will bind the constraints to an LpProblem.

In addition, a number of more sophisticated mathematical operations are supported, many of which involve the creation of auxiliary lparrays with variables behind the scenes.

Attributes
T

View of the transposed array.

base

Base object if memory is from some other object.

ctypes

An object to simplify the interaction of the array with the ctypes module.

data

Python buffer object pointing to the start of the array’s data.

dtype

Data-type of the array’s elements.

flags

Information about the memory layout of the array.

flat

A 1-D iterator over the array.

imag

The imaginary part of the array.

itemsize

Length of one array element in bytes.

nbytes

Total bytes consumed by the elements of the array.

ndim

Number of array dimensions.

real

The real part of the array.

shape

Tuple of array dimensions.

size

Number of elements in the array.

strides

Tuple of bytes to step in each dimension when traversing an array.

values

Return the underlying values of the PuLP variables.

Methods

abs(prob, name, **kwargs)

Return variable equal to |self|.

abs_decompose(prob, name, *[, bigM])

Constraint that generates two arrays, xp and xm, that sum to abs(self).

all([axis, out, keepdims, where])

Returns True if all elements evaluate to True.

any([axis, out, keepdims, where])

Returns True if any of the elements of a evaluate to True.

argmax([axis, out, keepdims])

Return indices of the maximum values along the given axis.

argmin([axis, out, keepdims])

Return indices of the minimum values along the given axis.

argpartition(kth[, axis, kind, order])

Returns the indices that would partition this array.

argsort([axis, kind, order])

Returns the indices that would sort this array.

astype(dtype[, order, casting, subok, copy])

Copy of the array, cast to a specified type.

byteswap([inplace])

Swap the bytes of the array elements

choose(choices[, out, mode])

Use an index array to construct a new array from a set of choices.

clip([min, max, out])

Return an array whose values are limited to [min, max].

compress(condition[, axis, out])

Return selected slices of this array along given axis.

conj()

Complex-conjugate all elements.

conjugate()

Return the complex conjugate, element-wise.

constrain(prob, name)

Apply the constraints contained in lparray to the problem.

copy([order])

Return a copy of the array.

create(name, index_sets, *[, lowBound, ...])

Creates a lparray with shape from a cartesian product of index sets.

create_anon(name, shape, **kwargs)

Create a lparray with a given shape and nameless index sets.

create_like(name, like, **kwargs)

Create a lparray with the same shape as the passed array.

cumprod([axis, dtype, out])

Return the cumulative product of the elements along the given axis.

cumsum([axis, dtype, out])

Return the cumulative sum of the elements along the given axis.

diagonal([offset, axis1, axis2])

Return specified diagonals.

dump(file)

Dump a pickle of the array to the specified file.

dumps()

Returns the pickle of the array as a string.

fill(value)

Fill the array with a scalar value.

flatten([order])

Return a copy of the array collapsed into one dimension.

getfield(dtype[, offset])

Returns a field of the given array as a certain type.

item(*args)

Copy an element of an array to a standard Python scalar and return it.

itemset(*args)

Insert scalar into an array (scalar is cast to array's dtype, if possible)

logical_clip(prob, name[, bigM])

Assumes self is an integer >= 0.

lp_bin_and(prob, name, *ins)

Constrain the array using logical AND operation of binary inputs.

lp_bin_max(prob, name, **kwargs)

Return an array corresponding to the maximum value along an axis.

lp_bin_min(prob, name, **kwargs)

Return an array corresponding to the minimum value along an axis.

lp_bin_or(prob, name, *ins)

Constrain the array using the logical OR operation of binary inputs

lp_int_max(prob, name, lb, ub, **kwargs)

Return an array corresponding to the maximum value along an axis.

lp_int_min(prob, name, lb, ub, **kwargs)

Return an array corresponding to the maximum value along an axis.

lp_real_max(prob, name, **kwargs)

Return an array corresponding to the maximum value along an axis.

lp_real_min(prob, name, **kwargs)

Return an array corresponding to the minimum value along an axis.

max([axis, out, keepdims, initial, where])

Return the maximum along a given axis.

mean([axis, dtype, out, keepdims, where])

Returns the average of the array elements along given axis.

min([axis, out, keepdims, initial, where])

Return the minimum along a given axis.

newbyteorder([new_order])

Return the array with the same data viewed with a different byte order.

nonzero()

Return the indices of the elements that are non-zero.

partition(kth[, axis, kind, order])

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array.

prod([axis, dtype, out, keepdims, initial, ...])

Return the product of the array elements over the given axis

ptp([axis, out, keepdims])

Peak to peak (maximum - minimum) value along a given axis.

put(indices, values[, mode])

Set a.flat[n] = values[n] for all n in indices.

ravel([order])

Return a flattened array.

repeat(repeats[, axis])

Repeat elements of an array.

reshape(shape[, order])

Returns an array containing the same data with a new shape.

resize(new_shape[, refcheck])

Change shape and size of array in-place.

round([decimals, out])

Return a with each element rounded to the given number of decimals.

searchsorted(v[, side, sorter])

Find indices where elements of v should be inserted in a to maintain order.

setfield(val, dtype[, offset])

Put a value into a specified place in a field defined by a data-type.

setflags([write, align, uic])

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.

sort([axis, kind, order])

Sort an array in-place.

squeeze([axis])

Remove axes of length one from a.

std([axis, dtype, out, ddof, keepdims, where])

Returns the standard deviation of the array elements along given axis.

sum([axis, dtype, out, keepdims, initial, where])

Return the sum of the array elements over the given axis.

sumit(*args, **kwargs)

Equivalent to self.sum().item().

swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

take(indices[, axis, out, mode])

Return an array formed from the elements of a at the given indices.

tobytes([order])

Construct Python bytes containing the raw data bytes in the array.

tofile(fid[, sep, format])

Write array to a file as text or binary (default).

tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

tostring([order])

A compatibility alias for tobytes, with exactly the same behavior.

trace([offset, axis1, axis2, dtype, out])

Return the sum along diagonals of the array.

transpose(*axes)

Returns a view of the array with axes transposed.

var([axis, dtype, out, ddof, keepdims, where])

Returns the variance of the array elements, along given axis.

view([dtype][, type])

New view of array with the same data.

dot
BINARY_LB: int = 0#

Lower bound value for binary LpVariables.

BINARY_UB: int = 1#

Upper bound value for binary LpVariables.

_lp_int_minmax(prob: LpProblem, name: str, which: Literal['min', 'max'], lb: int, ub: int, **kwargs: Any) → lparray[LpVariable][source]#

Internal method for lparray.lp_int_max.

Parameters

  • prob (LpProblem) –

  • name (str) –

  • which (Literal['min', 'max']) –

  • lb (int) –

  • ub (int) –

  • kwargs (Any) –

Return type

lparray[LpVariable]

_lp_minmax(prob: LpProblem, name: str, which: Literal['min', 'max'], cat: Literal['Binary', 'Integer', 'Continuous'], *, lb: Optional[Union[int, float]] = None, ub: Optional[Union[int, float]] = None, bigM: Union[int, float] = 1000, axis: Optional[Union[int, tuple[int, ...]]] = None) → lparray[LpVariable][source]#

Return lparray with its min/max along an axis.

The Axis can be multidimensional.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base LpVariable name for the min/max output array.

  • which (Literal[``”min”, ``"max"]) – Choice of operation. Can be either “min” or “max” – determines the operation

  • cat (LpVarType) – LpCategory of the output lparray

  • lb (Optional[Number]) – Lower bound on the output array

  • ub (Optional[Number]) – Upper bound on the output array

  • bigM (Number) – big M value used for auxiliary variable inequalities. It Should be larger than any value that can appear in self in a feasible solution.

  • axis (Optional[Union[int, tuple[int, ]]]) – Axes along which to take the maximum

Returns

lparray with min/max along a given axis.

Return type

lparray[LpVariable]

Raises

  • ValueError –

    • If axis is None and self is a scalar.

    • If self.ndim is not equal to len(axis).

    • If cat is not “Binary” and lb and ub are not provided.

    • If which is not “min” or “max”.

  • TypeError – If axis values are not integers.

abs(prob: LpProblem, name: str, **kwargs: Any) → lparray[LpAffineExpression][source]#

Return variable equal to |self|.

Thin wrapper around abs_decompose

Parameters

  • prob (LpProblem :) –

  • name (str) – Base name of extra constraints xm and xp that’ll be created

  • kwargs (Any) –

Returns

Array of linear expressions that are always bigger or equal to 0

Return type

lparray[LpAffineExpression]

abs_decompose(prob: LpProblem, name: str, *, bigM: Union[int, float] = 1000.0, **kwargs: Any) → Tuple[lparray[LpVariable], lparray[LpVariable]][source]#

Constraint that generates two arrays, xp and xm, that sum to abs(self).

Constraint uses the following properties:

\begin{array}{} xp \geq 0 \\ xm \geq 0 \\ xp == 0 \or xm == 0 \\ \Sum_{i=1}^{n} (xp_i + xm_i) = |self| = X \\ \text{where:} \\ xp \rightarrow \text{Positive half of X} \\ xm \rightarrow \text{Negative half of X} \\ \end{array} xp >= 0 xm >= 0 xp == 0 XOR xm == 0

Use the big M method. Generates 2 * self.size visible new variables. Generates 1 * self.size binary auxiliary variables.

Parameters

  • prob (LpProblem) – Problem to bind aux variables to

  • name (str) – Base names for generated variables

  • bigM (Number) – The -lower and upper bound on self to assume. Default value = 1000.0

  • kwargs (Any) – Extra arguments to create

  • self (lparray[LPV]) –

Returns

Arrays, xp and xm, that sum to |self|

Return type

Tuple[lparray[LpVariable], lparray[LpVariable]]

Examples

Let xb == binary variable (domain: {0, 1}) and x == integer expression comprised of multiple variables with (domain: {-1000, 1000})

If we create the following constraints:

x <= 10000 * (1 - xb) -> c_lb x >= -10000 * xb -> c_ub

If x is negative, then both c_lb and c_ub constraints will only be True, when xb == 1

On the other hand, if x is positive, xb must be equal to 0.

constrain(prob: LpProblem, name: str) → None[source]#

Apply the constraints contained in lparray to the problem.

Parameters

  • prob (LpProblem) – Lp Problem to add the lp_constraints to.

  • name (str) – Name of to the lp_constraints.

  • ..Versionadded: – 0.4.0: Add “try/except” clause that adds lp_constraints to prob instance without using the specified name if name is already taken.

Return type

None

classmethod create(name: str, index_sets: tuple[Collection[Any], ...], *, lowBound: Optional[Union[int, float]] = None, upBound: Optional[Union[int, float]] = None, cat: Literal['Binary', 'Integer', 'Continuous'] = 'Continuous') → lparray[LpVariable][source]#

Creates a lparray with shape from a cartesian product of index sets.

Each LpVariable in the array at an index [i_0, …, i_n] will be named as “{name}_(ix_sets[0][i_0], …, ix_sets[n][i_n])”

Parameters

  • name (str) – Base names for the underlying LpVariables

  • index_sets (tuple[Collection[Any], ]) – An iterable of iterables containing the dimension names for the array.

  • lowBound (Optional[Number]) – Passed to LpVariable, uniform for an array

  • upBound (Optional[Number]) – Passed as to LpVariable, uniform for the array

  • cat (LpVarType) – Passed to LpVariable, uniform for an array defining the category of each LpVariable. Defaults to “Integer”

Returns

Array of LP Variables

Return type

lparray[LpVariable]

classmethod create_anon(name: str, shape: Tuple[int, ...], **kwargs: Any) → lparray[LpVariable][source]#

Create a lparray with a given shape and nameless index sets.

Parameters

  • name (str) – Base names for the underlying LpVariables

  • shape (tuple[int, ]) – Array shape, same as for numpy arrays

  • **kwargs (Any) – Pulp LpVariable extra arguments that you want to define

Returns

Array of LpVariables

Return type

lparray[LpVariable]

classmethod create_like(name: str, like: HasShape, **kwargs: Any) → lparray[LpVariable][source]#

Create a lparray with the same shape as the passed array.

Parameters

  • name (str) – Base names for the LpVariables in the array

  • like (HasShape) – An object supporting .shape whose shape will be used

  • kwargs (Any) –

Returns

Anonymous lparray with the same shape as a passed array.

Return type

lparray[LpVariable]

logical_clip(prob: LpProblem, name: str, bigM: Union[int, float] = 1000) → lparray[LpVariable][source]#

Assumes self is an integer >= 0.

Returns an array of the same shape as self-containing:

z_… = max(self_…, 1)

Generates self.size new variables.

Parameters

  • prob (LpProblem) – Problem to bind aux variables to

  • name (str) – Base names for generated variables

  • bigM (Number) – The -lower and upper bound on self to assume. Default value = 1000.0

Returns

  • lparray[LpVariable] – Array of the same shape as self-containing max value between problem variables and 1.

  • warning:: – It is extremely important to correctly configure the value for bigM. This value should be greater than the module of every lpVariable, to be compared. If the problem variables have values in range of -100,000,000 to 50,000,000, then bigM must be AT LEAST equal to 100,000,001.

    On the other hand, setting bigM to values far greater than the variables’ range might result in considerable processing time.

Return type

lparray[LpVariable]

lp_bin_and(prob: LpProblem, name: str, *ins: Union[lparray[LpVariable], lparray[LpAffineExpression], ndarray]) → lparray[LPV][source]#

Constrain the array using logical AND operation of binary inputs.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base name for the lp_constraints.

  • *ins (Union[lparray[LpVariable], lparray[LpAffineExpression], ndarray]) –

  • self (lparray[LPV]) –

Returns

Array with implemented logic AND gate.

Return type

lparray[LPV]

lp_bin_max(prob: LpProblem, name: str, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the maximum value along an axis.

Binary variable type.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base-name for the output array.

  • **kwargs (Any) –

Returns

Array with the max along one of the axes.

Return type

lparray[LpVariable]

lp_bin_min(prob: LpProblem, name: str, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the minimum value along an axis.

Binary variable type.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base-name for the output array,

  • **kwargs (Any) –

Returns

Array with min along one of the axes.

Return type

lparray[LpVariable]

lp_bin_or(prob: LpProblem, name: str, *ins: Union[lparray[LpVariable], lparray[LpAffineExpression], ndarray]) → lparray[LPV][source]#

Constrain the array using the logical OR operation of binary inputs

Parameters
Return type

lparray[LPV]

lp_int_max(prob: LpProblem, name: str, lb: int, ub: int, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the maximum value along an axis.

The array corresponds to the maximum value along specified axes.

Parameters

  • prob (LpProblem) –

  • name (str) –

  • lb (int) –

  • ub (int) –

  • kwargs (Any) –

Return type

lparray[LpVariable]

lp_int_min(prob: LpProblem, name: str, lb: int, ub: int, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the maximum value along an axis.

The Method can be used on Integer variables.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints

  • name (str) – Base-name for the output array

  • lb (Optional[Number]) – Lower bound on the output array

  • ub (Optional[Number]) – Upper bound on the output array

  • kwargs (Any) –

Returns

Array with min along one of the axes.

Return type

lparray[LpVariable]

lp_real_max(prob: LpProblem, name: str, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the maximum value along an axis.

Continuous variable type.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base name for the output array

  • **kwargs (Any) –

Returns

Array with max along one of the axes.

Return type

lparray[LpVariable]

lp_real_min(prob: LpProblem, name: str, **kwargs: Any) → lparray[LpVariable][source]#

Return an array corresponding to the minimum value along an axis.

Continuous variable type.

Parameters

  • prob (LpProblem) – Problem instance to which to apply the constraints.

  • name (str) – Base name for the output array

  • **kwargs (Any) –

Returns

Array with min along one of the axes.

Return type

lparray[LpVariable]

sumit(*args: Any, **kwargs: Any) → LpVariable[source]#

Equivalent to self.sum().item().

Parameters

  • args (Any) –

  • kwargs (Any) –

Return type

LpVariable

property values: ndarray#

Return the underlying values of the PuLP variables.

Values are returned by calling pulp.value on each element of the lparray. If the problem is not solved, all entries are returned as None.

Returns

Numpy array with variable values

Return type

np.ndarray