
TL;DR
This paper constructs a unique optimal binary linear code of parameters [46,9,20], proves its uniqueness and asymmetry, and establishes the non-existence of certain related codes, advancing the understanding of code optimality and uniqueness.
Contribution
It presents the first known [46,9,20]_2 code, proves its uniqueness and asymmetry, and shows non-existence of specific larger or related codes.
Findings
Constructed a [46,9,20]_2 code and proved its uniqueness.
Showed the code is asymmetric with a trivial automorphism group.
Established non-existence of [47,10,20]_2 and [85,9,40]_2 codes.
Abstract
The minimum distance of all binary linear codes with dimension at most eight is known. The smallest open case for dimension nine is length with known bounds . Here we present a code and show its uniqueness. Interestingly enough, this unique optimal code is asymmetric, i.e., it has a trivial automorphism group. Additionally, we show the non-existence of and codes.
| k / n | 20 | 24 | 28 | 30 | 32 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ||||||
| 2 | 1 | 1 | 2 | 0 | 3 | 0 | 3 | 0 | ||||||||
| 3 | 1 | 1 | 2 | 4 | 6 | 9 | ||||||||||
| 4 | 1 | 4 | 13 | 26 | ||||||||||||
| 5 | 3 | 15 | 163 | |||||||||||||
| 6 | 24 | 3649 | ||||||||||||||
| 7 | 5 | 337794 |
| k / n | 40 | 48 | 56 | 60 | 64 | 68 | 70 | 72 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||||||
| 2 | 1 | 1 | 2 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | ||||||||
| 3 | 1 | 1 | 2 | 0 | 3 | 0 | 5 | 0 | ||||||||||
| 4 | 1 | 1 | 2 | 3 | 6 | 10 | ||||||||||||
| 5 | 1 | 3 | 11 | 16 | ||||||||||||||
| 6 | 2 | 8 | 106 | |||||||||||||||
| 7 | 7 | 5613 |
| 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
| 25773 | 48792 | 26091 | 5198 | 450 | 17 | 1 |
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The code is unique
Sascha Kurz
Sascha Kurz, University of Bayreuth, 95440 Bayreuth, Germany
Abstract.
The minimum distance of all binary linear codes with dimension at most eight is known. The smallest open case for dimension nine is length with known bounds . Here we present a code and show its uniqueness. Interestingly enough, this unique optimal code is asymmetric, i.e., it has a trivial automorphism group. Additionally, we show the non-existence of and codes.
Keywords: Binary linear codes, optimal codes
1. Introduction
An -code is a -ary linear code with length , dimension , and minimum Hamming distance . Here we will only consider binary codes, so that we also speak of -codes. Let be the smallest integer for which an -code exists. Due to Griesmer [7] we have
[TABLE]
where denotes the smallest integer . As shown by Baumert and McEliece [1] for every fixed dimension there exists an integer such that for all , i.e., the determination of is a finite problem for every fixed dimension . For , the function has been completely determined by Baumert and McEliece [1] and van Tilborg [12]. After a lot of work of different authors, the determination of has been completed by Bouyukliev, Jaffe, and Vavrek [4]. For results on we refer e.g. to [5] and the references therein. The smallest open case for dimension nine is length with known bounds . Here we present a code and show its uniqueness. Interestingly enough, this unique optimal code is asymmetric, i.e., it has a trivial automorphism group. Speaking of a -divisible code for codes whose weights of codewords all are divisible by , we can state that the optimal code is -divisible. -divisible codes are also called doubly-even and -divisible codes are called even. Additionally, we show the non-existence of and codes. Our main tools – described in the next section – are the standard residual code argument (Proposition 2.2), the MacWilliams identities (Proposition 2.3), a result based on the weight distribution of Reed-Muller codes (Proposition 2.4), and the software packages Q-Extension [2], LinCode [8] to enumerate linear codes with a list of allowed weights. For an easy access to the known non-existence results for linear codes we have used the online database [6].
2. Basic tools
Definition 2.1**.**
Let be an -code and be a codeword of weight . The restriction to the support of is called the residual code of with respect to . If only the weight is of importance, we will denote it by .
Proposition 2.2**.**
Let be an -code. If , then has the parameters
[TABLE]
Some authors call the result for the special case the one-step Griesmer bound.
Proposition 2.3**.**
*([9], MacWilliams Identities)
Let be an -code and be the dual code of . Let and be the number of codewords of weight in and , respectively. With this, we have*
[TABLE]
where
[TABLE]
are the binary Krawtchouk polynomials. We will simplify the notation to and whenever is clear from the context.
Whenever we speak of the first MacWilliams identities, we mean Equation (2) for . Adding the non-negativity constraints we obtain a linear program where we can maximize or minimize certain quantities, which is called the linear programming method for linear codes. Adding additional equations or inequalities strengthens the formulation.
Proposition 2.4**.**
*([5, Proposition 5], cf. [10])
Let be an -code with all weights divisible by and let be the weight distribution of . Put*
[TABLE]
Then the integer
[TABLE]
satisfies the following conditions.
- (1)
* is divisible by .* 2. (2)
If , then
[TABLE]
for some integer satisfying . Moreover, if , then has an -subcode and if , it has an -subcode. 3. (3)
If , then
[TABLE]
for some integer satisfying . Moreover, if , then has an -subcode. If , then has an -subcode unless , in which case it has an -subcode.
A special and well-known subcase is that the number of even weight codewords in a code is either or .
3. Results
Lemma 3.1**.**
Each code contains a codeword of weight .
- Proof. Let be an code with and . From the first two MacWilliams identities we conclude
[TABLE]
so that
[TABLE]
Thus, the number of even weight codewords is at most . Since at least half the codewords have to be of even weight, we obtain . In the remaining case we use the linear programming method with the first four MacWilliams identities, , , and the fact that there are exactly even weight codewords to conclude , i.e., and for all . With this and rounding to integers we obtain the bounds , which then gives the unique solution , , , and . Computing the full dual weight distribution unveils , which is negative.
Lemma 3.2**.**
Each even code is isomorphic to a code with generator matrix
[TABLE]
- Proof. Applying Proposition 2.2 with on a code would give a code, which does not exist. Thus, has effective length , i.e., . Since no code exists, is projective, i.e., . Since no code exists, Proposition 2.2 yields that cannot contain a codeword of weight . Assume for a moment that contains a codeword of weight and let be the corresponding residual code. Let be another codeword of and and be the weights of and . Then the weight of the corresponding residual codeword is given by , so that weight is impossible in ( does not contain a codeword of weight ). Since has to contain a subcode, Lemma 3.1 shows the non-existence of , so that . With this, the first three MacWilliams Identities are given by
[TABLE]
Minimizing gives , so that Proposition 2.4.(3) gives , i.e., all weights are divisible by . A further application of the linear programming method gives that , so that has to contain a subcode. Next, we have used Q-Extension and LinCode to classify the codes for and , see Table 1. Starting from the doubly-even codes, Q-Extension and LinCode give 424207 doubly-even codes and no doubly-even code (as the maximum minimum distance of a code is .) Indeed, a codeword of weight or can occur in a doubly-even code. We remark that largest occurring order of the automorphism group is . Finally, an application of Q-Extension and LinCode on the 424207 doubly-even codes results in the unique code as stated. (Note that there may be also doubly-even codes with two or more codewords of a weight . However, these are not relevant for our conclusion.)
We remark that the code of Lemma 3.2 has a trivial automorphism group and weight enumerator , i.e., all weights are divisible by four. The dual minimum distance is (, ), i.e., the code is projective. Since the Griesmer bound, see Inequality (1), gives a lower bound of for the length of a binary linear code with dimension and minimum distance , the code has the optimum minimum distance. The linear programming method could also be used to exclude the weights and directly (and to show ). While the maximum distance was proven using the Griesmer bound directly, the code is not a Griesmer code, i.e., where Inequality (1) is satisfied with equality. For the latter codes the -divisibility would follow from [13, Theorem 9] stating that for Griesmer codes over , where is a divisor of the minimum distance, all weights are divisible by .
Theorem 3.3**.**
Each code is isomorphic to a code with the generator matrix given in Lemma 3.2.
- Proof. Let be a with generator matrix which is not even. Removing a column from and adding a parity check bit gives an even code. So, we start from the generator matrix of Lemma 3.2 and replace a column by all possible column vectors. Checking all cases gives either linear codes with a codeword of weight or the generator matrix of Lemma 3.2 again.
Lemma 3.4**.**
No code exists.
- Proof. Assume that is a code. Since no and no code exists, we have and , respectively. Let be a systematic generator matrix of . Since removing the th unit vector and the corresponding column (with the -entry) from gives a code, there are at least codewords in whose weight is divisible by . Thus, Proposition 2.4.(3) yields that is doubly-even. By Theorem 3.3 we have . Adding this extra inequality to the linear inequality system of the first four MacWilliams identities gives, after rounding down to integers, , , , and . (We could also conclude directly from the non-existence of a -code.) The unique remaining weight enumerator is given by . Let be such a code and be the code generated by the nine codewords of weight . We eventually add codewords from to till has dimension exactly nine and denote the corresponding code by . Now the existence of contradicts Theorem 3.3.
So, the unique code is strongly optimal in the sense of [11, Definition 1], i.e., no and no code exists. The strongly optimal binary linear codes with dimension at most seven have been completely classified, except the codes, in [3]. The next open case is the existence question for a code, which is equivalent to the existence of a code. The technique of Lemma 3.2 to conclude the -divisibility of an optimal even code can also be applied in further cases and we given an example for codes, whose existence is unknown.
Lemma 3.5**.**
Each code contains a codeword of weight .
- Proof. We verify this statement computationally using Q-Extension and LinCode.
We remark that a direct proof is possible too. However, the one that we found is too involved to be presented here. Moreover, there are exactly codes without a codeword of weight .
Lemma 3.6**.**
If an even code exists, then it has to be doubly-even.
- Proof. Since no and no code exists, we have and . Proposition 2.2 yields that does not contain a codeword of weight . Assume for a moment that contains a codeword of weight and let be the corresponding residual code. Let be another codeword of and and be the weights of and . Then the weight of the corresponding residual codeword is given by , so that weight is impossible in ( does not contain a codeword of weight ). Since has to contain a subcode, Lemma 3.5 shows the non-existence of , so that . We use the linear programming method with the first four MacWilliams identities. Minimizing the number of doubly-even codewords gives , so that Proposition 2.4.(3) gives , i.e., all weights are divisible by .
Two cases where -divisibility can be concluded for optimal even codes are given below.
Theorem 3.7**.**
No code exists.
- Proof. Assume that is a code. Since no and no code exists, we have and , respectively. Considering the residual code, Proposition 2.2 yields that contains no codewords with weight . With this, we use the first four MacWilliams identities and minimize . Since , so that Proposition 2.4.(3) gives , all weights are divisible by . Minimizing gives , so that Proposition 2.4.(3) gives , i.e., all weights are divisible by . The residual code of each codeword of weight is a projective -divisible code of length . Since no such codes of lengths and exist, does not contain codewords of weight or , respectively.111We remark that a -divisible non-projective binary linear code of length exists. The residual code of a codeword of weight is a projective -divisible -dimensional code of length . Note that cannot contain a codeword of weight since no even code of length exists. Thus we have . Now we look at the two-dimensional subcodes of the unique codeword of weight and two other codewords. Denoting their weights by , , and the weight of the corresponding codeword in by we use the notation . W.l.o.g. we assume , and obtain the following possibilities: , , , and . Note that and are impossible. By , , , and we denote the corresponding counts. Setting , we have that is the number of codewords of weight in . Assuming the unique (theoretically) possible weight enumerator is . Double-counting gives , , and . Solving this equation system gives and . Using the first four MacWilliams identities for we obtain the unique solution , , and , so that is negative – contradiction. Thus, and the unique (theoretically) possible weight enumerator is given by (). Using Q-Extension and LinCode we classify all codes for and , see Table 2. For dimension , there is no code and exactly 106322 codes. The latter codes have weight enumerators
[TABLE]
(), where . The corresponding counts are given in Table 3. Since the next step would need a huge amount of computation time we derive some extra information on a -subcode of . Each of the codewords of weight of hits of the columns of a generator matrix of , so that there exists a column which is hit by at most such codewords. Thus, by shortening of we obtain a -subcode with at least codewords of weight . Extending the corresponding cases with Q-Extension and LinCode results in no code. (Each extension took between a few minutes and a few hours.)
Lemma 3.8**.**
Each code satisfies .
- Proof. We verify this statement computationally using Q-Extension and LinCode.
We remark that there a 1 , 3 , and 9 codes without codewords of a weight in .
Lemma 3.9**.**
Each even code contains a codeword of weight ..
- Proof. We verify this statement computationally using Q-Extension and and LinCode.
We remark that there a 2 and 6 codes that are even and do not contain a codeword of weight .
Theorem 3.10**.**
If an even code exist, then the weights of all codewords are divisible by .
- Proof. From the known non-existence results we conclude and does not contain codewords with a weight in . If would contain a codeword of weight then its corresponding residual code is a code without codewords with a weight in , which contradicts Lemma 3.8. Thus, . Minimizing the number of doubly-even codewords using the first four MacWilliams identities gives , so that Proposition 2.4.(3) gives , i.e., all weights are divisible by . If contains no codeword of weight , then the number of codewords whose weight is divisible by is at least , so that Proposition 2.4.(3) gives , i.e., all weights are divisible by . So, let us assume that contains a codeword of weight and consider the corresponding residual code . Note that is even and does not contain a codeword of weight , which contradicts Lemma 3.9. Thus, all weights are divisible by .
Proposition 3.11**.**
If an even code exist, then its weight enumerator is either or .
- Proof. Assume that is an even code. Since no and no code exists we have and , respectively. Using the known upper bounds on the minimum distance for -dimensional codes we can conclude that no codeword as a weight . Maximizing gives , so that is -divisible, see Proposition 2.4.(3). Maximizing gives , so that is -divisible, Proposition 2.4.(3). Maximizing for gives a value strictly less than , so that the only non-zero weights can be , , , and . Maximizing gives an upper bound of , so that or . The remaining values are then uniquely determined by the first four MacWilliams identities.
The exhaustive enumeration of all codes remains a computational challenge. While we have constructed a few thousand non-isomorphic codes, we still do not know whether a code exists.
The reference list from the paper itself. Each links out to its DOI / PubMed record.
- 1[1] L. Baumert and R. Mc Eliece. A note on the Griesmer bound. IEEE Transactions on Information Theory , pages 134–135, 1973.
- 2[2] I. Bouyukliev. What is Q-extension? Serdica Journal of Computing , 1(2):115–130, 2007.
- 3[3] I. Bouyukliev and D. B. Jaffe. Optimal binary linear codes of dimension at most seven. Discrete Mathematics , 226(1-3):51–70, 2001.
- 4[4] I. Bouyukliev, D. B. Jaffe, and V. Vavrek. The smallest length of eight-dimensional binary linear codes with prescribed minimum distance. IEEE Transactions on Information Theory , 46(4):1539–1544, 2000.
- 5[5] S. Dodunekov, S. Guritman, and J. Simonis. Some new results on the minimum length of binary linear codes of dimension nine. IEEE Transactions on Information Theory , 45(7):2543–2546, 1999.
- 6[6] M. Grassl. Bounds on the minimum distance of linear codes and quantum codes. Online available at http://www.codetables.de , 2007. Accessed on 2019-04-04.
- 7[7] J. H. Griesmer. A bound for error-correcting codes. IBM Journal of Research and Development , 4(5):532–542, 1960.
- 8[8] S. Kurz. Lincode – computer classification of linear codes. ar Xiv preprint 1912.09357 , 12 pages, 2019.
