Computation of Maximal Determinants of Binary Circulant Matrices

01/01/2018
by   Richard P. Brent, et al.
0

We describe algorithms for computing maximal determinants of binary circulant matrices of small orders. Here "binary matrix" means a matrix whose elements are drawn from {0,1} or {-1,1}. We describe efficient parallel algorithms for the search, using Duval's algorithm for generation of Lyndon words and the well-known representation of the determinant of a circulant in terms of roots of unity. Our computations extend earlier results and disprove two plausible conjectures.

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1 Introduction

A circulant matrix of order is an matrix whose elements depend only on . Thus, an circulant is a matrix of the form

. Circulants arise in various applications in signal processing and combinatorics, and have a close connection with Fourier transforms. The set of all circulants of order

(with elements in some fixed ring ) form a commutative algebra, since the sum and product of two circulants is a circulant, and it is easy to see that multiplication of circulants is commutative.

We write for the circulant whose first row is .

By a binary matrix we mean a matrix whose elements are in one of the sets or . It will be clear from the context which of these two cases is being considered. A binary circulant is a circulant matrix whose elements are in or .

There is a natural correspondence between the integers and the binary circulant matrices of order . If has the representation

so may be written in binary as , we associate with , where in the case of , and in the case of .

The maximal determinant problem is concerned with the maximal value of for an binary matrix . The Hadamard bound [20] states that, in the case of binary matrices over , we have

(1)

Moreover, Hadamard’s inequality is sharp for infinitely many , for example, powers of two (Sylvester [38]), or of the form where and is a prime power (Paley [32]).

There is a well-known connection between the determinants of -matrices of order and -matrices of order . This implies that an -matrix always has determinant divisible by . See Neubauer and Radcliffe [28] for details. We give an example with , starting with an binary matrix and ending with an -matrix , with .

The doubling step is the only step where the determinant changes, and there it is multiplied by .

Thus, Hadamard’s bound (1) gives the bound

(2)

which applies for all -matrices of order . We shall refer to both (1) and (2) as Hadamard’s inequality, since it will be clear from the context which inequality is intended.111In fact, Hadamard in [20] proved a more general inequality than (1), and as far as we are aware he never stated (2) explicitly. A simple proof of (1) is given by Cameron [9].

The mapping from -matrices to -matrices is reversible if we are allowed to normalize the first row and column of the -matrix by changing the signs of rows/columns as necessary.

The transformation illustrated above (or its reverse) does not preserve any circulant structure.

Hadamard matrices are square matrices with entries in and mutually orthogonal rows. The order of a Hadamard matrix is , , or a multiple of . It is not known whether a Hadamard matrix of order exists for every positive integer  — this is the Hadamard conjecture.

Various constructions for Hadamard matrices use circulant matrices. For example, the first Paley construction (Paley [32]) uses a circulant matrix of order , where is a prime, , to construct a Hadamard matrix of order . (The Paley construction also works for prime powers, e.g., , but does not involve circulants in such cases.) Fletcher, Gysin and Seberry [16] use two circulants and a border of width two to construct Hadamard matrices. The Williamson construction (Williamson [40]) requires four matrices which satisfy certain conditions, and for computational reasons these matrices are usually taken to be circulants.

Circulant matrices also play an important role in noisy convolutional Gaussian channels. Given a channel in which the output vector is given by the convolution of the input vector with a chosen mask vector, in the presence of additive Gaussian noise, the choice of mask that maximizes the mutual information of the channel in high-SNR regimes is the first row of a

-circulant with near-flat Fourier spectrum, and this circulant is often one with maximal or close to maximal determinant. This has important applications in X-ray and gamma ray astronomy, optics, and computational imaging [1, 2, 11, 15, 25, 42].

It is well-known that the (unnormalized) eigenvectors of

are given by , , where is a primitive -th root of unity. For example, in we can take

. It follows that the eigenvalues are

(3)

and the determinant is

(4)

where

The polynomial is called the associated polynomial of the circulant. Also, is called a Littlewood polynomial if the coefficients , and a Newman polynomial if the and .

If is nonsingular, then (4) gives

This may be regarded as a discrete analogue of the Mahler measure [36]

Using (4) to compute for a circulant matrix takes arithmetic operations, whereas Gaussian elimination does not take advantage of the circulant structure and takes of order operations. If we are considering binary matrices, whose determinants are integers, it is necessary to perform the operations in to sufficient precision to obtain a result with absolute error less than , so that the correct result can be found by rounding to the nearest integer. From the Hadamard bounds (1)–(2), this means that we may have to work with of order bits of precision.

To avoid the problem of rounding errors altogether, we can work over a finite field. If is a prime such that , and is a primitive root (mod ), then222It is not necessary to know a primitive root (mod ). We can choose a random , compute , and check if are distinct (mod ). If not, reject and repeat with another random . In this way we work in a (small) group of order , instead of a (large) group of order , and there is no need to factor . The expected number of iterations is .

is a primitive -th root of unity in the finite field , and we can use (4) to compute . If is an upper bound on , and , then the result is sufficient to determine . Thus, if we use a Hadamard bound for , the prime should have of order bits. Alternatively, we could use several smaller primes with a sufficiently large product, and reconstruct the result using the Chinese Remainder Theorem.333Tests indicate that, at least for , it is faster to use a single prime. One reason for this is that the value needs to be reconstructed for each circulant , so the cost of the reconstruction steps is not negligible.

2 Lyndon words and necklaces

The usual definition of a Lyndon word is a nonempty string that is strictly smaller in lexicographic order than all of its proper rotations. Thus, the first six Lyndon words over are , , , , , and . Lyndon words were introduced by Shirshov [34] (who called them “regular words”) and Lyndon [26] (who called them “standard lexicographic sequences”).

Since we consider words of a fixed length , it is convenient to use the concept of a (binary) necklace. We say that is a necklace of length if is not larger (in the lexicographic order) than any of its rotations. This corresponds to Duval’s “representative of a class of words of length ” [12, (3) on p. 258], where two words are said to be in the same class if one is a rotation of the other.

For example, according to our definition, the six necklaces of length over are , , , , , and . It can be seen that, if we strip off leading zeros, we obtain the first six Lyndon words. Thus, the concepts of “Lyndon word” and “necklace” are closely related, and algorithms for one may often by modified to apply to the other.

The number of necklaces of length over a binary alphabet is

(5)

where is Euler’s phi function. is tabulated in OEIS A000031.

If is a circulant, then is invariant under rotations of the first row . Thus, when searching for circulants of order  with maximal determinants, it is sufficient to consider circulants whose first row is a necklace of length . From (5), this saves a factor of approximately .

In our computations we use two nontrivial algorithms related to Lyndon words/necklaces. One is the algorithm of Booth [5], which determines in linear time if a word is in fact a necklace.444We use a simplified version of Booth’s algorithm since we do not need to know the rotation that would convert into a necklace.

Booth’s algorithm is closely related to the initial phase of the Knuth, Morris and Pratt fast pattern-matching algorithm 

[22].

The other algorithm that we use is Duval’s algorithm [12] which, given a necklace of length , returns the next necklace (of length ) in lexicographic order555Duval’s paper [12] considers Lyndon words but, using [12, comment (3) on p. 258], we easily get a similar algorithm for necklaces., in amortized (i.e., average) constant time, see [4]. Using Duval’s algorithm we can cycle through all necklaces of length  in time .

Other algorithms could be used. For example, Shiloach [33] gives an algorithm that reduces the number of comparisons used by Booth’s algorithm. We used Booth’s algorithm because it was sufficient for our purposes, and simpler to implement than Shiloach’s algorithm. The overall complexity of our algorithms is dominated by the time required to evaluate determinants using (4), not by the time required to check or enumerate necklaces.

3 Fast evaluation of circulant determinants

Standard algorithms of linear algebra, such as Gaussian elimination, require of order operations to evaluate the determinant of an matrix . Using formula (4), this can be reduced to order if is a circulant. In fact, using the fast Fourier transform (FFT), operations suffice.

However, in our application we can do even better. Because Duval’s algorithm takes constant time (on average), the number of symbols that are changed as we go from one necklace to the next is on average.666We find experimentally that the mean number of symbols changed is as . The limiting value is the same as the mean number of bits changed when counting up in binary. Thus, each value given by (3) can be updated in operations (on average), and the determinant, given by (4), can be updated with operations (on average). Since there are necklaces of length , the computation of all the relevant determinants can be done with operations. The cost of precomputing a table of powers is negligible.

Note that we used the term “operations” rather than “time”, because the arithmetic operations need to be performed using of order bits of precision, as noted above. Thus, the overall complexity is , where is the time required to multiply -bit numbers.

In theory, a slightly better complexity can be attained by using several small primes and reconstructing the result via the Chinese Remainder Theorem. However, the cost of reconstructions must be taken into account. In practice, is never large, because of the exponentially growing factor in the complexity, so the difference between the two approaches is essentially an implementation-dependent constant factor.

4 Parallel algorithms

Suppose we wish to use processors in parallel. If the necklaces of length are , we would like processor () to compute the determinants corresponding (approximately) to necklaces . The problem is how to determine the starting point for processor , without enumerating . Kociumaka et al. [23] sketch a deterministic polynomial-time algorithm for this problem, but it is very complicated. We used a simpler, randomized algorithm.777A similar algorithm, although not specifically intended for parallel computation, is described by Briggs and Ying [7]. For related references, see Ghosh and Pasupathy [17].

The idea is to take a random sample of necklaces, each of length . Sort the sample, and then divide it into equal-sized segments. Modify the initial segment to start with and the final segment to end with . Thus, each processor has approximately words to process. Also, we know the necklace starting each segment, so we can use Duval’s algorithm to enumerate all necklaces in a segment.

How large does the sample size need to be? Suppose the procedure just described gives the -th processor necklaces to process, where and . Define by , so measures the relative difference between and the mean . Using extreme-value theory it can be shown that, as , we have

(6)

Simulations show that (6) gives a reasonable approximation even for small , provided . Thus, for a given (one-sided) tolerance , we should take

(7)

With this choice888In fact, our program used simply , equivalent to taking in (7). We had , so . of we can expect that, on average, the slowest processor will take only about times as long as the average processor.999This ignores any random variations in speed due to other users in a shared multi-processor computer system. In practice such variations may be larger than the variations caused by random sampling.

We now describe how to randomly and uniformly sample the set of all necklaces of length . Generate a random binary string of length , and test (using Booth’s algorithm) if it corresponds to a necklace. If so, accept the string. Otherwise, reject the string and try again.101010It is not sufficient to rotate the string until we obtain a necklace. This would not give each necklace an equal probability of selection. Consider, for example, the case , where the three necklaces , , and would be selected with probabilities , , and respectively. This process is repeated until we have the desired number

of necklaces (not necessarily distinct). Clearly each necklace is equally likely to appear in the final list. Since the probability of a random binary string of length

being a necklace is , the expected number of random binary strings that are needed is . The algorithm that we have described is an example of Von Neumann’s rejection method [29]. Other examples may be found in Devroye’s book [10].

For we can speed up the generation of random necklaces, if we exclude the special cases and , and take advantage of the fact that all other necklaces have the form , where .

5 Computational results

Our computations extend some entries in the OEIS [30]. Tables 12 give computational results for the maximal determinants of -circulants of orders . The third column of each table gives the ratio , where is the maximum of for -circulants of order , and is an upper bound on .

Similarly, Tables 34 give computational results for the maximal determinants of -circulants of orders . Here the third column is the ratio , where is an upper bound on . In Tables 34 we scale the determinants of -circulants by dividing by the known factor . In the last column of Table 3, “” and “” are used as abbreviations for and respectively.

The bounds and are defined as follows. Let

(8)

Then is an upper bound on for -matrices of order . The case is due to Hadamard [20]; the case is due to Ehlich [13] and Wojtas [41]; and the remaining case (odd) is due to Barba [3], Ehlich [13], and Wojtas [41]. We do not use Ehlich’s slightly sharper, but more complicated, bound that applies when . For this bound, see Ehlich [14] or Orrick [31].

In view of the discussion in §1, we take

(9)

and

(10)

It is an open question whether attains the bound for any . (If we restrict attention to the cases , this is the circulant Hadamard problem.) On the other hand, for all primes . This follows from the first Paley construction [32], which constructs a Hadamard matrix of order with a circulant submatrix of order . Inspection of Tables 12 reveals that in some other cases, specifically .

Table 2 extends the list of values given for in OEIS A086432 and the associated b-file. Table 4 extends the list of values given for in OEIS A215897. This implies a corresponding extension for OEIS A215723, which lists the unscaled values .

As an indication of the time required to compute the tables, we note that the computation of using our parallel program (implemented in C using GMP [19]) took processor-years using 128 Intel Xeon3 (2.2GHz) and 224 Xeon4 (2.6GHz) processors. The computation time for order was roughly proportional to .

For verification, all the values given in the tables for orders were computed at least twice, using different programs and/or different prime moduli . No discrepancies were found.

In Sections 68 we discuss some plausible conjectures that motivated our computations.

6 Conjecture A

From the third column of Table 1, the determinant of a -circulant can attain the upper bound in the cases . The Paley construction explains this for , and larger cases where is a prime and . However, it does not explain the case . Also, the upper bound is not attained for . Thus, a plausible conjecture is that the upper bound can be attained whenever is the product of at most two distinct primes. Support is provided by the computation for , since .

Our computation for disproves this conjecture, since is a product of two distinct primes, but . Another counter-example is . We find that .

After our computations were completed, we found an explanation for the cases and . In each case has the form , where and are both prime. Such are covered by case (2) of the following theorem, which we quote (with a change in notation) from Kotsireas et al. [24]. Note that a “circulant core” of order refers to a -circulant matrix of order which can be used to construct a Hadamard matrix of order using the correspondence between -matrices and -matrices described in §1.

Theorem 1 (Hadamard circulant core construction).

A Hadamard matrix of order with circulant core of order exists if

  • is a prime;

  • , where and are prime;

  • , where is a positive integer; or

  • , where is a positive integer and is a prime.

Proof.

Case (1) is due to Paley [32]; case (2) is due to Stanton and Sprott [37] and also Whiteman [39]; case (3) is due to Singer [35]; and case (4) is due to Hall [21, Theorem 2.2]. ∎

Hall [21, p. ] remarks that case (4) is subsumed by case (1), since , but we mention case (4) since Hall’s construction is different from that of Paley.

We do not know if the list given by Theorem 1 is exhaustive. The computational results given in Tables 12 show that, for , only those given by Theorem 1 can provide a Hadamard matrix of order with a circulant core. Also, a circulant -matrix of order can achieve the upper bound (10) if and only if or satisfies condition (1), (2) or (3) of Theorem 1.

7 Conjecture B, case

When considering maximal determinants of matrices with real elements in the interval , we can see that the maximum occurs at extreme points of the polytope.111111This is already implicit in Hadamard [20]. To prove this, we need only note that the determinant of a square matrix is a linear function of each variable considered separately. Thus, if a local maximum of occurs for some , we can replace by (at least one of) or without decreasing .

This argument does not apply if is restricted to be a circulant of order , because then the free parameters are just the elements of the first row of , and is not a linear function of each considered separately. For example, if we have . Nevertheless, inspection of small cases suggests the conjecture that the maximum of occurs at extreme points of the -dimensional polytope.

We were unable to prove the conjecture, so wrote a program to check it numerically, and found that, in general, the conjecture is false.

The idea is as follows. Consider all possible circulants of order with entries in . If , check if a small perturbation of towards the interior of the polytope would increase . Although such behaviour is rare, it does occur.121212For reasons of efficiency, our program takes as input a list (generated during the computation of Tables 12) of necklaces that define circulants with maximal , then considers all possible rotations of these circulants.

The smallest examples occur for . Consider with . We have , but . If for some small , then , so for sufficiently small . In fact, .

For , an example is , . Replacing by , we obtain .

We found examples of such behaviour for , and no other up to the limit of Table 2. However, there is a different class of examples that occur when is a prime, e.g., , etc. For this class we make a small modification to the Uniformly Redundant Arrays (URAs) of [8, 15], which are equivalent to Abelian difference sets [21].131313Our construction is also close to the “modified” URAs (MURAs) of [18]. Define

where is a quadratic character, defined by the Legendre symbol

Then corresponds to a -D URA, but is not generally maximal in the class of circulant determinants. However, may be larger than the corresponding entry in Tables 12. It may be shown141414The proof uses the identity . that, for an odd prime,

(11)

In particular, , , and

It may be verified numerically that exceeds the maximal determinant given in Tables 12 for and . The next possibility, , is beyond the range of Table 2.

We observe that the maximum of for is not at . One can show, by logarithmic differentiation of (11), that a local maximum occurs at

and

For example, if , we have , and

8 Conjecture B, case

Replacing by , we find analogous behaviour to that described in §7, for , , and no other up to the limit of Table 4.

The case is trivial because, for circulants of order  over , we necessarily have at the extreme points .

The other cases are non-trivial. For example, if , consider

We find that

so sufficiently small gives . Indeed, we can take , as .

If , we find that

and

so in both cases a sufficiently small disproves the conjecture. A different type of exceptional case is illustrated by

where we find that is an even polynomial in , and

Similarly, for order , consider

Then

Since (see Table 3), we have .

Our search was not exhaustive, so there may be other within the range of Tables 34 for which the maximum determinant does not occur at an extreme point of .

9 Remarks on periodic autocorrelations

It is hard to discern a pattern in the lex-least words given in Tables 14. It seems more fruitful to consider the periodic autocorrelations of the first rows of the corresponding circulants. Equivalently, we can consider the first rows of the Gram matrices , where is the relevant circulant.

In the case of -circulants, it can be useful to map , and consider the first row of . Provided , the upper bound is achieved in Tables 12 if and only if the first row of is . See, for example, MacWilliams and Sloane [27].

In some cases the maximal determinants given in Tables 14 have only small prime factors. For example, the entry for in Table 4 factors as , and this can be explained if we observe that the first row of is . Thus, we can write , where is the “circular shift” matrix. Similarly, the entry for in Table 4 is , and here .

10 Acknowledgements

We thank Jörg Arndt for correcting our terminology, Alex Arkhipov for his helpful comments about Hadamard matrices with circulant cores and related group theory, and the authors of Magma [6] and GMP [19] for their invaluable software. An anonymous referee helped us to clarify several points in §2 and §4. Computing resources were provided by the Australian National University and the University of Newcastle (Australia). The first author was supported in part by Australian Research Council grant DP140101417.

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11 Appendix: tables of maximal determinants

order maximal ratio lex-least lex-least
determinant word word
(decimal) (over )
1
01
011
0111
01111
001011
0010111
00101111
000101111
0000110111
00010110111
000110110111
0010111110111
00001011101111
000100110101111
0000101101110111
00000101101110111
000010011010101111
0000101011110011011
00000110110101110111
000001011001110101111
0000011100101101110111
00001010011001101011111
000011001101110101101111
0000010011011110101001111
Table 1: Maximal determinants of -circulants of order .
order maximal ratio to lex-least word
determinant upper bound (decimal)
Table 2: Maximal determinants of -circulants, .
order maximal ratio lex-least lex-least
scaled det word word
(decimal) (over )
1 1 1.0000 0 -
2 0 0.0000 0 --
3 1 1.0000 1 --+
4 2 1.0000 1 ---+
5 3 1.0000 1 ----+
6 4 0.8000 1 -----+
7 8 0.6667 11 ---+-++
8 18 0.5625 11 ----+-++
9 27 0.4154 11 -----+-++
10 44 0.3056 11 ------+-++
11 267 0.5973 39 -----+--+++
12 1024 0.7023 83 -----+-+--++
13 3645 1.0000 83 ------+-+--++
14 6144 0.6483 83 -------+-+--++
15 23859 0.6886 359 ------+-++--+++
16 50176 0.3828 691 ------+-+-++--++
17 187377 0.4977 1643 ------++--++-+-++
18 531468 0.4770 2215 ------+---+-+--+++
19 3302697 0.7176 9895 -----+--++-+-+--+++
20 10616832 0.5436 6483 -------++--+-+-+--++
21 39337984 0.6291 67863 ----+----+--+---+-+++
22 102546588 0.5000 21095 -------+-+--+--++--+++
23 568833245 0.6087 72519 ------+---++-++-+---+++
24 3073593600 0.7060 144791 ------+---++-+-++--+-+++
25 8721488875 0.5724 108199 --------++-+--++-+-+--+++
Table 3: Maximal scaled determinants of -circulants of order .
order maximal scaled det ratio to lex-least word
upper bound (decimal)
26 32998447572 0.6064 355463
27 164855413835 0.6125 604381
28 572108938470 0.4218 1289739
29 2490252810073 0.4863 1611219
30 10831449635712 0.5507 1680711
31 68045615234375 0.6520 6870231
32 282773291271138 0.5023 12817083
33 1592413932070703 0.7017 18635419
34 5234078743146888 0.5635 55100887
35 33374247484277975 0.6366 149009085
36 198124573871046186 0.6600 160340631
37 787413957917252603 0.6140 415804239
38 3195257068570067448 0.5754 829121815
39 22999238901574021485 0.6946 4737823097
40 117140061677844350646 0.5857 1446278811
41 536469708946538168543 0.5961 3001209959
42 2417648227367853639168 0.5897 19153917469
43 14611334654738350617599 0.5689 52222437727
44 65738632907943707712320 0.4038 20159598251
45 438910341492340511320163 0.5715 166482220965
46 2010768410464246499566152 0.5489 90422521191
47 12779930756727248097293989 0.5324 115099593371
48 100192997081088000000000000 0.6302 242235026743
49 408375323859124630659059549 0.5216 1416138805685
50 2152519997519833685106486024 0.5526 2380679727935
51 14098690136202107270366810369 0.5300 2716242515341
52 99371059004238555166801920000 0.5416 1758408815375
Table 4: Maximal scaled determinants of -circulants, .