dimod.generators.quadratic_assignment#

quadratic_assignment(distance_matrix: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], flow_matrix: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes]) → ConstrainedQuadraticModel[source]#

Generates a constrained quadratic model encoding a quadratic-assignment problem.

Given distance and flow matrices, generates a ConstrainedQuadraticModel for the corresponding quadratic-assignment problem.

Parameters:

  • distances – Distances between locations \(i\) and \(j\) as a NumPy array.

  • flows – Flows between facilities \(i\) and \(j\) as a NumPy array.

Returns:

The constrained quadratic model encoding the quadratic-assignment problem. Variables are denoted as x_{i}_{j} where x_{i}_{j} == 1 means that facility i is placed in location j.