quadratic_assignment#
- scipy.optimize.quadratic_assignment(A, B, method='faq', options=None)[source]#
- 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:
- A2-D array, square
- The square matrix \(A\) in the objective function above. 
- B2-D array, square
- The square matrix \(B\) in the objective function above. 
- methodstr in {‘faq’, ‘2opt’} (default: ‘faq’)
- The algorithm used to solve the problem. ‘faq’ (default) and ‘2opt’ are available. 
- optionsdict, 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- mis not greater than the number of nodes, \(n\).
- rngnumpy.random.Generator, optional
- Pseudorandom number generator state. When rng is None, a new - numpy.random.Generatoris created using entropy from the operating system. Types other than- numpy.random.Generatorare passed to- numpy.random.default_rngto instantiate a- Generator.- Changed in version 1.15.0: As part of the SPEC-007 transition from use of - numpy.random.RandomStateto- numpy.random.Generatoris occurring. Supplying np.random.RandomState to this function will now emit a DeprecationWarning. In SciPy 1.17 its use will raise an exception. In addition relying on global state using np.random.seed will emit a FutureWarning. In SciPy 1.17 the global random number generator will no longer be used. Use of an int-like seed will raise a FutureWarning, in SciPy 1.17 it will be normalized via np.random.default_rng rather than np.random.RandomState.
 - For method-specific options, see - show_options('quadratic_assignment').
 
- Returns:
- resOptimizeResult
- OptimizeResultcontaining 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 >>> rng = np.random.default_rng() >>> 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, options={'rng': rng}) >>> print(res) fun: 3260 col_ind: [0 3 2 1] nit: 9 - The see the relationship between the returned - col_indand- fun, use- col_indto 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_assignmenthappens 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, options={'rng': rng}) >>> 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 = {'rng': rng, 'partial_guess': guess}) >>> print(res) fun: 176 col_ind: [1 2 3 0] nit: 17