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Numerous but Rare: An Exploration of Magic Squares

  • Akimasa Kitajima ,

    Affiliations Research and Legislative Reference Bureau, National Diet Library, Chiyoda-ku, Tokyo, Japan, Department of Physics, Graduate School of Science, Osaka university, Toyonaka, Osaka, Japan

  • Macoto Kikuchi

    Affiliations Large-Scale Computational Science Division, Cybermedia center, Osaka University, Toyonaka, Osaka, Japan, Department of Physics, Graduate School of Science, Osaka university, Toyonaka, Osaka, Japan

Numerous but Rare: An Exploration of Magic Squares

  • Akimasa Kitajima, 
  • Macoto Kikuchi


How rare are magic squares? So far, the exact number of magic squares of order n is only known for n ≤ 5. For larger squares, we need statistical approaches for estimating the number. For this purpose, we formulated the problem as a combinatorial optimization problem and applied the Multicanonical Monte Carlo method (MMC), which has been developed in the field of computational statistical physics. Among all the possible arrangements of the numbers 1; 2, …, n2 in an n × n square, the probability of finding a magic square decreases faster than the exponential of n. We estimated the number of magic squares for n ≤ 30. The number of magic squares for n = 30 was estimated to be 6.56(29) × 102056 and the corresponding probability is as small as 10−212. Thus the MMC is effective for counting very rare configurations.


Making magic squares is a popular form of mathematical recreation. It is also used in classrooms as an elementary mathematical exercise. The classic (or ordinary) magic square of order n is defined as follows: Placing the numbers 1, 2, ⋯n2 in a square array using each number once, if all the sums of the numbers in each row, column and diagonal give the same value, Mn=12n(n2+1), the array makes a magic square. Mn is called the magic number. Besides the classic magic squares, there are many variations, and some rigorous results have been found for them. But not much is known about classic magic squares [1]. In this paper, we focus on classic magic squares.

There are some algorithms for making magic squares of any size. They, however, provide some special classes of magic squares, which gives rise to the question: Among all the possible arrangements of numbers in a square of a given size, how many of them form magic squares? Putting the question in another form: Is there any chance of making a magic square by putting numbers randomly in a square? It may be surprising to know that the exact number of possible magic squares is so far only known up to order 5. There is currently no hope of exact enumeration for a larger system. In this paper, we apply a Monte Carlo method to this problem, and estimate the number of the magic squares of each size up to order 30.

To state the problem explicitly, we consider classic magic squares order n. Let Nn denote the total number of magic squares. Since possible configurations increase as n2!, counting the magic squares rapidly becomes more difficult. Currently, only the following three values of Nn are known exactly: N3 = 1, N4 = 880, and N5 = 275,305,224 [1], where the eight equivalent magic squares that can be transformed into each other by rotation and reflection are counted as one.

For larger squares, we need to employ statistical approaches for estimating the number of magic squares. There have been two studies in this direction. Pinn and Wieczerkowski applied the exchange Monte Carlo method (EMC) [2] to this problem [3] and estimated N6 and N7. Their results are N6 = 1.7745(16) × 1019 and N7 = 3.760(52) × 1034, where the digits in parentheses indicate the statistical error of the lowest digits. Trump proposed a more efficient method, called Monte Carlo Backtracking, and estimated Nn for n ≤ 20. [4].

EMC belongs to a family of extended ensemble Monte Carlo methods [5]. Extended ensemble Monte Carlo methods were initially developed in the field of statistical physics, and have found a wide field of applications beyond their original scope. They are especially suitable for estimating the probability of occurrence of very rare events. The work by Pinn and Wieczerkowski is one of the earliest applications of the extended ensemble Monte Carlo methods outside the field of physics. In this paper, we use the Multicanonical Monte Carlo method (MMC) [6], which also belongs to a family of extended ensemble methods. There have also been some studies that used EMC for counting solutions for mathematical puzzles such as the N-Queen problem [7][8]. But the MMC has not been used for problems of this type. Compared to the EMC, which requires a trick for counting the number of configurations that satisfy some specific conditions, the MMC provides the estimates of the number straightforwardly. We thus consider the MMC to be more suitable for problems of this sort than the EMC.


Let us consider magic squares of order n. In order to apply the MMC, we define the energy E(C) of a configuration of numbers C as follows: (1) where Si, Sj, and Sk are the sums of the numbers for the ith column, that for the jth row, and that for the kth diagonal. E(C) is zero if and only if C is a magic square, and it takes a positive value otherwise. Thus, the lowest energy (E = 0) configurations provide magic squares. In other words, finding problem of magic squares are formulated as a combinatorial optimization problem. The number of optimal configurations are very large in this case, and we estimate the number using the MMC.

The MMC was proposed by Berg and Neuhaus in the field of statistical physics to overcome slow convergence of Metropolis-type Markov chain Monte Carlo methods when applied to the sampling of low temperature states of complex systems [6]. In contrast to the Metropolis method which provides the canonical ensemble, namely the ensemble of configurations at the thermal equilibrium of a given temperature T as the steady-state distribution of the Markov process, the MMC aims to give a flat energy distribution over the entire energy range. This flatness enables us to estimate the number of configurations of any energy. For that purpose, a predetermined weight function W(E) is used in the MMC instead of the canonical weight eE/T used in the Metropolis method. W(E) is prepared so that the energy histogram H(E) obtained by Monte Carlo sampling is sufficiently flat. Since H(E) is proportional to the product of W(E) and the number of states g(E) having energy E, we can then estimate relative values of g(E) from W(E) and H(E) as (2)

The appearance probability of energy E = ɛ in randomly arranged configurations of numbers from 1 to n2 is estimated by (3) where the summation in the denominator is taken over all the possible energies. Thus the appearance probability of magic square Pn is given by Pn = P(E = 0). Since the total number of configurations is n2!, we can also estimate the total number of magic squares Nn by Pn × n2!/8. It should be noted that, in principle, MMC gives statistically unbiased estimates for the number of magic squares.

To determine W(E), we carried out preliminary runs using the Wang–Landau method [9], in which W(E) is updated at each Monte Carlo trial until it finally gives a sufficiently flat histogram H(E). We then fixed W(E) and carried out long measurement runs using the entropic sampling method [10], which is equivalent to MMC in the present study because we assigned a single value of energy to each bin of the histogram. We made independent measurement runs many times for each n, and took averages of Nn and Nn2 over them. The statistical error of Nn was then estimated as three times the standard error: (4) where ⟨⋅⟩ is the mean value of ⋅ and t is the number of the measurement runs.

Only the sequential transposition of adjacent numbers, 1 with 2, then 2 with 3, ⋯, n2−1 with n2 were used as an elementary process of the Monte Carlo trial, following Ref. [3]. By this method, we can avoid a large energy difference between successive configurations in Markov chains, which causes inefficiencies in Monte Carlo methods. We employed Mersenne-Twister as the pseudo-random-number generator [11].

The number of measurement runs and the length of each run are different for each n. For the largest system with n = 30, for example, we made 40 independent measurement runs of 1.1 × 1012 Monte Carlo trials each. Flatness of the histogram was confirmed by a long independent run that was four times longer than the measurement run. The number of configurations in each bin of the histogram falls within the range from 0.93 to 1.01 of the mean value, which we decided as sufficiently flat.


Fig 1 shows a semi-log plot of the n dependence of Pn. Our estimates of Pn and Nn up to n = 30 are listed in Table 1. Exact values for of Nn for n = 3,4 and 5 and the previous estimates of Nn by Trump for 6 ≤ n ≤ 20 are also shown for comparison. We obtained N30 = 6.56(29) × 102056. In contrast to this extremely large value, its appearance probability, P30, is smaller than 10−212. Thus the magic squares are numerous but rare.

Fig 1. Semi-log plot of the appearance probability Pn of magic squares (•).

Pn decreases faster than exponentially with the size n. Two fitted functions are also shown: exp((An + B)ln(n) + Cn + D) (solid line) and exp((En + F)ln(n + G) + H) (dotted line) with A = −4.99 and E = −4.88. We used Pn of n ≥ 10 for the fitting. Enlarged plot for n < 6 is shown in the inset, in which difference of two functions are visible.

The largest size of n = 30 is much larger than that which has been calculated previously. The estimates of N3, N4, and N5 agree with the exact values within the statistical error, and the estimates up to n = 17 are consistent with Trump’s values within the statistical error. However, there are appreciable discrepancies between the present results and those of Trump for n = 18 and 19; our values are larger for these sizes. In fact, Trump himself pointed out that the true values for N18 and N19 might be smaller than his estimates based on his own extrapolation formula. We thus think that our estimates are reliable. He also gave estimates of Nn for n > 20 obtained by the abovementioned extrapolation formula. Compared to the present estimates, his extrapolation results have two-digits accuracy up to n = 30.

As seen in Fig 1, the appearance probability of magic squares Pn decreases rapidly with n. In other words, magic squares become rarer rapidly as n increases. This raises the question: how fast does their appearance probability decrease? It clearly decreases faster than the exponential function exp(−an) with constant a. On the other hand, since the number of possible configurations is n2!, Pn should decrease slower than exp(−n2logn). Considering these facts, we first tried to fit logPn for n ≥ 10 by the second-order polynomial. But the reduced χ2 was larger than 6000 and thus the fitting function was inappropriate. Next we tried functions including logn. The fitting function (An + B)logn + Cn + D with the constants A, B, C and D gave A = −4.99 ± 0.07 with the reduced χ2 = 2.55 and the fitting function (En + F)log(n + G) + H with the constants E, F, G and H gave E = −4.880 ± 0.008 with the reduced χ2 = 2.42. Fitted curves using these two functions are shown in Fig 1. The two curves are virtually indistinguishable on this scale except for very small values of n. Other functions we tried gave larger values of reduced χ2. The reduced χ2 for both functions, however, are still large, and the fittings are not fully satisfactory. We consider the reason is that n = 10 is not yet at the asymptotic region. In fact, when we tried to fit the same functions to data only for larger sizes, n ≥ 20, we obtained the reduced χ2 = 1.30 and 1.37, respectively. Although the errors of the parameters are large, A = −4.6 ± 0.5 and E = −4.88 ± 0.06, reduced χ2 for both functions are rather satisfactory.

Fig 2 shows the ratio Pn/exp{(An + B)logn + Cn + D} and Pn/exp{(En + F)log(n + G) + H}. Both functions seem to express Pn equally well. In any case, since the slope of logPn varies slowly, it is difficult to determine the appropriate functional form from the present results. We need Pn for much larger systems to provide a solid conclusion. Even so, we may conjecture that the logPn decreases asymptotically as an log n with a ≃ 5.

Fig 2. The ratio of the appearance probability Pn to two fitted functions.

Pn/exp{(An + B)ln(n) + Cn + D}} (•) and Pn/exp{(En + F)ln(n + G) + H} (×) with A = −4.99 and E = −4.88. Both functions seem to express Pn equally well.

In this paper, we applied the Multicanonical Monte Carlo method to a combinatorial optimization problem by defining appropriate an energy function E(C). The MMC directly gives the number of the optimal configurations from the histogram of the lowest energy configurations. The present work demonstrates that the MMC is a powerful tool for counting rare configurations of combinatorial problems. We can estimate the appearance probabilities of the optimal configurations as small as 10−212.


We are grateful to Koji Hukushima, Yukito Iba, and Nen Saito for valuable discussions and continuous encouragement. Numerical experiments were carried out on PC clusters at the Cybermedia Center, Osaka University.

Author Contributions

Conceived and designed the experiments: AK MK. Performed the experiments: AK. Analyzed the data: AK MK. Contributed reagents/materials/analysis tools: AK MK. Wrote the paper: AK MK.


  1. 1. Pickover C (2001) The Zen of Magic Squares, Circles, and Stars. Princeton University Press.
  2. 2. Hukushima K, Nemoto K (1996) Exchange Monte Carlo Method and Application to Spin Glass Simulations. J Phys Soc Jpn 65 (1996): 1604–1608.
  3. 3. Pinn K, Wieczerkowski C (1998) Number of Magic Squares from Parallel Tempering Monte Carlo. Int J Mod Phys C9: 541–546.
  4. 4. Trump W (2005). Available: Accessed 17 December 2014.
  5. 5. Iba Y (2001) Extended ensemble Monte Carlo. Int J Mod Phys C12: 623–656.
  6. 6. Berg BA, Neuhaus T (1991) Multicanonical algorithms for first order phase transitions. Phys Lett B267: 249–253.
  7. 7. Hukushima K (2002) Extended ensemble Monte Carlo approach to hardly relaxing problems. Computer physics communications 147: 77–82.
  8. 8. Zhang C, Ma J (2009) Counting solutions for the N-queens and Latin-square problems by Monte Carlo simulations. Phys Rev E79: 016703.
  9. 9. Wang F, Landau DP (2001) Efficient, Multiple-Range Random Walk Algorithm to Calculate the Density of States. Phys Rev Lett 86: 2050–2053. pmid:11289852
  10. 10. Lee J (1993) New Monte Carlo Algorithm: Entropic Sampling. Phys Rev Lett 71: 211–214. pmid:10054892
  11. 11. Matsumoto M, Nishimura T (1998) Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Trans on Modeling and Computer Simulation 8: 3–30.