The Shannon entropy of Sudoku matrices

Paul K. Newton, Stephen A. DeSalvo


We study properties of an ensemble of Sudoku matrices (a special type of doubly stochastic matrix when normalized) using their statistically averaged singular values. The determinants are very nearly Cauchy distributed about the origin. The largest singular value is Embedded Image, while the others decrease approximately linearly. The normalized singular values (obtained by dividing each singular value by the sum of all nine singular values) are then used to calculate the average Shannon entropy of the ensemble, a measure of the distribution of ‘energy’ among the singular modes and interpreted as a measure of the disorder of a typical matrix. We show the Shannon entropy of the ensemble to be 1.7331±0.0002, which is slightly lower than an ensemble of 9×9 Latin squares, but higher than a certain collection of 9×9 random matrices used for comparison. Using the notion of relative entropy or Kullback–Leibler divergence, which gives a measure of how one distribution differs from another, we show that the relative entropy between the ensemble of Sudoku matrices and Latin squares is of the order of 10−5. By contrast, the relative entropy between Sudoku matrices and the collection of random matrices has the much higher value, being of the order of 10−3, with the Shannon entropy of the Sudoku matrices having better distribution among the modes. We finish by ‘reconstituting’ the ‘average’ Sudoku matrix from its averaged singular components.


  • 1 We note that there are similarities here to the well-studied Wishart matrices, whose distribution arises from the sample covariance matrix obtained from a multivariate normal distribution. These arise frequently in the study of spectral theory of random matrices (Sengupta & Mitra 1999). Also note that sample covariance matrices are well known to be sensitive to statistical outliers.

  • 2 In fact, this will always be the case since the constraints imply that 45 is an eigenvalue of A and AT, with corresponding eigenvectors (1,1,…,1) and (1,1,…,1)T.

    • Received October 5, 2009.
    • Accepted January 7, 2010.
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