# scipy.optimize.quadratic_assignment

scipy.optimize.quadratic_assignment(A, B, method='faq', options=None)

  Approximates solution to the quadratic assignment problem and the graph matching problem.

  Quadratic assignment solves problems of the following form:

  \[\begin{split}\min_P & \ {\ \text{trace}(A^T P B P^T)}\\ \mbox{s.t. } & {P \ \epsilon \ \mathcal{P}}\\\end{split}\]

  where \(\mathcal{P}\) is the set of all permutation matrices, and \(A\) and \(B\) are square matrices.

  Graph matching tries to maximize the same objective function. This algorithm can be thought of as finding the alignment of the nodes of two graphs that minimizes the number of induced edge disagreements, or, in the case of weighted graphs, the sum of squared edge weight differences.

  Note that the quadratic assignment problem is NP-hard. The results given here are approximations and are not guaranteed to be optimal.

  Parameters:
    A 2-D array, square

      The square matrix \(A\) in the objective function above.

    B 2-D array, square

      The square matrix \(B\) in the objective function above.

    method str in {‘faq’, ‘2opt’} (default: ‘faq’)

      The algorithm used to solve the problem. ‘faq’ (default) and ‘2opt’ are available.

    options dict, optional

      A dictionary of solver options. All solvers support the following:

      maximizebool (default: False)

        Maximizes the objective function if `True`.

      partial_match2-D array of integers, optional (default: None)

        Fixes part of the matching. Also known as a “seed” [2].

        Each row of partial_match specifies a pair of matched nodes: node `partial_match[i, 0]` of A is matched to node `partial_match[i, 1]` of B. The array has shape `(m, 2)`, where `m` is not greater than the number of nodes, \(n\).

      rng{None, int, `numpy.random.Generator`,

        > `numpy.random.RandomState`}, optional

        If seed is None (or np.random), the `numpy.random.RandomState` singleton is used. If seed is an int, a new `RandomState` instance is used, seeded with seed. If seed is already a `Generator` or `RandomState` instance then that instance is used.

      For method-specific options, see `show_options('quadratic_assignment')`.

  Returns:
    res OptimizeResult

      `OptimizeResult` containing the following fields.

      col_ind1-D array

        Column indices corresponding to the best permutation found of the nodes of B.

      funfloat

        The objective value of the solution.

      nitint

        The number of iterations performed during optimization.

  Notes

  The default method ‘faq’ uses the Fast Approximate QAP algorithm [1]; it typically offers the best combination of speed and accuracy. Method ‘2opt’ can be computationally expensive, but may be a useful alternative, or it can be used to refine the solution returned by another method.

  References

  [1]

  J.T. Vogelstein, J.M. Conroy, V. Lyzinski, L.J. Podrazik, S.G. Kratzer, E.T. Harley, D.E. Fishkind, R.J. Vogelstein, and C.E. Priebe, “Fast approximate quadratic programming for graph matching,” PLOS one, vol. 10, no. 4, p. e0121002, 2015, DOI:10.1371/journal.pone.0121002

  [2]

  D. Fishkind, S. Adali, H. Patsolic, L. Meng, D. Singh, V. Lyzinski, C. Priebe, “Seeded graph matching”, Pattern Recognit. 87 (2019): 203-215, DOI:10.1016/j.patcog.2018.09.014

  [3]

  “2-opt,” Wikipedia. https://en.wikipedia.org/wiki/2-opt

  Examples

      >>> import numpy as np
      >>> from scipy.optimize import quadratic_assignment
      >>> A = np.array([[0, 80, 150, 170], [80, 0, 130, 100],
      ...               [150, 130, 0, 120], [170, 100, 120, 0]])
      >>> B = np.array([[0, 5, 2, 7], [0, 0, 3, 8],
      ...               [0, 0, 0, 3], [0, 0, 0, 0]])
      >>> res = quadratic_assignment(A, B)
      >>> print(res)
           fun: 3260
       col_ind: [0 3 2 1]
           nit: 9

  The see the relationship between the returned `col_ind` and `fun`, use `col_ind` to form the best permutation matrix found, then evaluate the objective function \(f(P) = trace(A^T P B P^T )\).

      >>> perm = res['col_ind']
      >>> P = np.eye(len(A), dtype=int)[perm]
      >>> fun = np.trace(A.T @ P @ B @ P.T)
      >>> print(fun)
      3260

  Alternatively, to avoid constructing the permutation matrix explicitly, directly permute the rows and columns of the distance matrix.

      >>> fun = np.trace(A.T @ B[perm][:, perm])
      >>> print(fun)
      3260

  Although not guaranteed in general, `quadratic_assignment` happens to have found the globally optimal solution.

      >>> from itertools import permutations
      >>> perm_opt, fun_opt = None, np.inf
      >>> for perm in permutations([0, 1, 2, 3]):
      ...     perm = np.array(perm)
      ...     fun = np.trace(A.T @ B[perm][:, perm])
      ...     if fun < fun_opt:
      ...         fun_opt, perm_opt = fun, perm
      >>> print(np.array_equal(perm_opt, res['col_ind']))
      True

  Here is an example for which the default method, ‘faq’, does not find the global optimum.

      >>> A = np.array([[0, 5, 8, 6], [5, 0, 5, 1],
      ...               [8, 5, 0, 2], [6, 1, 2, 0]])
      >>> B = np.array([[0, 1, 8, 4], [1, 0, 5, 2],
      ...               [8, 5, 0, 5], [4, 2, 5, 0]])
      >>> res = quadratic_assignment(A, B)
      >>> print(res)
           fun: 178
       col_ind: [1 0 3 2]
           nit: 13

  If accuracy is important, consider using ‘2opt’ to refine the solution.

      >>> guess = np.array([np.arange(len(A)), res.col_ind]).T
      >>> res = quadratic_assignment(A, B, method="2opt",
      ...                            options = {'partial_guess': guess})
      >>> print(res)
           fun: 176
       col_ind: [1 2 3 0]
           nit: 17