This module implements functions useful for studying binary self-dual codes. The main function is self_dual_codes_binary, which is a case-by-case list of entries, each represented by a Python dictionary.
Format of each entry: a Python dictionary with keys “order autgp”, “spectrum”, “code”, “Comment”, “Type”, where
Python dictionaries were used since they seemed to be both human-readable and allow others to update the database easiest.
The following double for loop can be time-consuming but should be run once in awhile for testing purposes. It should only print True and have no trace-back errors:
for n in [4,6,8,10,12,14,16,18,20,22]:
C = self_dual_codes_binary(n); m = len(C.keys())
for i in range(m):
C0 = C["%s"%n]["%s"%i]["code"]
print n, ' ',i, ' ',C["%s"%n]["%s"%i]["spectrum"] == C0.spectrum()
print C0 == C0.dual_code()
G = C0.automorphism_group_binary_code()
print C["%s"%n]["%s"%i]["order autgp"] == G.order()
To check if the “Riemann hypothesis” holds, run the following code:
R = PolynomialRing(CC,"T")
T = R.gen()
for n in [4,6,8,10,12,14,16,18,20,22]:
C = self_dual_codes_binary(n); m = len(C["%s"%n].keys())
for i in range(m):
C0 = C["%s"%n]["%s"%i]["code"]
if C0.minimum_distance()>2:
f = R(C0.sd_zeta_polynomial())
print n,i,[z[0].abs() for z in f.roots()]
You should get lists of numbers equal to 0.707106781186548.
Here’s a rather naive construction of self-dual codes in the binary case:
For even m, let A_m denote the mxm matrix over GF(2) given by adding the all 1’s matrix to the identity matrix (in MatrixSpace(GF(2),m,m) of course). If M_1, ..., M_r are square matrices, let \(diag(M_1,M_2,...,M_r)\) denote the”block diagonal” matrix with the \(M_i\) ‘s on the diagonal and 0’s elsewhere. Let \(C(m_1,...,m_r,s)\) denote the linear code with generator matrix having block form \(G = (I, A)\), where \(A = diag(A_{m_1},A_{m_2},...,A_{m_r},I_s)\), for some (even) \(m_i\) ‘s and \(s\), where \(m_1+m_2+...+m_r+s=n/2\). Note: Such codes \(C(m_1,...,m_r,s)\) are SD.
SD codes not of this form will be called (for the purpose of documenting the code below) “exceptional”. Except when n is “small”, most sd codes are exceptional (based on a counting argument and table 9.1 in the Huffman+Pless [HP], page 347).
AUTHORS:
REFERENCES:
x.__init__(...) initializes x; see help(type(x)) for signature
For internal use; returns the floor(n/2) x n matrix space over GF(2).
EXAMPLES:
sage: import sage.coding.sd_codes as sd_codes
sage: sd_codes.MS(2)
Full MatrixSpace of 1 by 2 dense matrices over Finite Field of size 2
sage: sd_codes.MS(3)
Full MatrixSpace of 1 by 3 dense matrices over Finite Field of size 2
sage: sd_codes.MS(8)
Full MatrixSpace of 4 by 8 dense matrices over Finite Field of size 2
For internal use; returns the floor(n/2) x floor(n/2) matrix space over GF(2).
EXAMPLES:
sage: import sage.coding.sd_codes as sd_codes
sage: sd_codes.MS2(8)
Full MatrixSpace of 4 by 4 dense matrices over Finite Field of size 2
For internal use; returns a list of square matrices over GF(2) \((a_{ij})\) of sizes 0 x 0, 1 x 1, ..., n x n which are of the form \((a_{ij} = 1) + (a_{ij} = \delta_{ij})\).
EXAMPLES:
sage: import sage.coding.sd_codes as sd_codes
sage: sd_codes.matA(4)
[
[0 1 1]
[0 1] [1 0 1]
[], [0], [1 0], [1 1 0]
]
For internal use; returns a list of identity matrices over GF(2) of sizes (floor(n/2)-j) x (floor(n/2)-j) for j = 0 ... (floor(n/2)-1).
EXAMPLES:
sage: import sage.coding.sd_codes as sd_codes
sage: sd_codes.matId(6)
[
[1 0 0]
[0 1 0] [1 0]
[0 0 1], [0 1], [1]
]
Returns the dictionary of inequivalent sd codes of length n.
For n=4 even, returns the sd codes of a given length, up to (perm) equivalence, the (perm) aut gp, and the type.
The number of inequiv “diagonal” sd binary codes in the database of length n is (“diagonal” is defined by the conjecture above) is the same as the restricted partition number of n, where only integers from the set 1,4,6,8,... are allowed. This is the coefficient of \(x^n\) in the series expansion \((1-x)^{-1}\prod_{2^\infty (1-x^{2j})^{-1}}\). Typing the command f = (1-x)(-1)*prod([(1-x(2*j))(-1) for j in range(2,18)]) into Sage, we obtain for the coeffs of \(x^4\), \(x^6\), ... [1, 1, 2, 2, 3, 3, 5, 5, 7, 7, 11, 11, 15, 15, 22, 22, 30, 30, 42, 42, 56, 56, 77, 77, 101, 101, 135, 135, 176, 176, 231] These numbers grow too slowly to account for all the sd codes (see Huffman+Pless’ Table 9.1, referenced above). In fact, in Table 9.10 of [HP], the number B_n of inequivalent sd binary codes of length n is given:
n 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30
B_n 1 1 1 2 2 3 4 7 9 16 25 55 103 261 731
According to http://oeis.org/classic/A003179, the next 2 entries are: 3295, 24147.
EXAMPLES:
sage: C = self_dual_codes_binary(10)
sage: C["10"]["0"]["code"] == C["10"]["0"]["code"].dual_code()
True
sage: C["10"]["1"]["code"] == C["10"]["1"]["code"].dual_code()
True
sage: len(C["10"].keys()) # number of inequiv sd codes of length 10
2
sage: C = self_dual_codes_binary(12)
sage: C["12"]["0"]["code"] == C["12"]["0"]["code"].dual_code()
True
sage: C["12"]["1"]["code"] == C["12"]["1"]["code"].dual_code()
True
sage: C["12"]["2"]["code"] == C["12"]["2"]["code"].dual_code()
True