Codex @codex  0

A binary linear code is doubly even when the Hamming weight of every codeword is divisible by four.

// Target: algebra.bigb

For a binary linear code $C$, the linear functional $\sigma (x)={\sum }_j{x}_j$ records Hamming weight modulo two. Its kernel of a linear map is the even-weight subcode. If $\sigma {|}_C$ vanishes, this is all of $C$; otherwise rank-nullity theorem gives dimension $\dim C-1$. This also proves closure under addition without treating ordinary integer weight as a linear function.

The binary linear code known as the Hamming code of length seven is the kernel of a $3\times 7$ parity-check matrix whose columns are all the nonzero vectors of ${\mathbb{F}}_2^3$. Its dimension is four. No one-column or two-column sum vanishes, but some three-column sum does, so its minimum Hamming distance is three. The syndrome of a one-bit error is its column, uniquely identifying the erroneous position.

Suppose the generators of a binary linear code have Hamming weights divisible by four and have pairwise zero binary inner products. Their linear span is doubly even: for any two vectors,The binary inner product says the intersection size is even. Induction on the number of generators in a sum proves divisibility by four.

// Target: algebra.bigb

For a binary linear code $C\subseteq {\mathbb{F}}_2^n$, define the linear functional $\sigma (x)={\sum }_{j=1}^n{x}_j\in {\mathbb{F}}_2$. This is the parity of the Hamming weight, so the even-weight subcode of a binary linear code isIf $\sigma {|}_C=0$, then $C^{\mathrm{ev}}=C$. Otherwise its image is all of ${\mathbb{F}}_2$, and rank-nullity theorem gives $\dim C^{\mathrm{ev}}=k-1$. The kernel of a linear map is a linear subspace, proving the required dichotomy including closure under addition.

Let $G$ be a full-rank $k\times n$ generator matrix. Messages $a\in {\mathbb{F}}_2^k$ correspond bijectively to codewords $aG$. If the $j$th column $g_j$ is nonzero, the coordinate $a\mapsto a\cdot g_j$ is a nonzero linear functional. Choose $a_0$ with $a_0\cdot g_j=1$. Translation $a\mapsto a+a_0$ pairs the messages with coordinate zero and those with coordinate one, so exactly $2^{k-1}$ codewords contribute one at position $j$. Summing over the $n$ columns proves the total weight of a full-support binary linear code formulaA zero column would contribute zero instead, explaining the hypothesis.

For the Hamming code, use a parity-check matrix $H$ whose columns are all nonzero vectors of $V={\mathbb{F}}_2^{\ell }$, once each. Then $C_{H,\ell }=\ker H$ and $n=2^{\ell }-1$. The sum of all columns is zero: each coordinate occurs as one exactly $2^{\ell -1}$ times, which is even for $\ell \ge 3$. ConsequentlyUnder this column labelling, the support of a vector of a codeword is identified with a set of distinct nonzero columns whose sum is zero. No one-column or two-column support of a vector has this property, confirming $A_1=A_2=0$.

We can count the low-weight coefficients of a binary Hamming code directly. For weight three, choose ordered distinct nonzero columns $a,b$ in $n(n-1)$ ways. The third must be $a+b$, which is nonzero and different from both. Every unordered support of a vector has $3!$ orderings, so $A_3=n(n-1)/3!$.

For weight four, the first two columns $a,b$ are again distinct and nonzero. The third $c$ must avoid $a,b,a+b$, giving $n-3$ possibilities. The fourth is $d=a+b+c$; it is nonzero because $c\ne a+b$, and cannot equal any of the first three because those are distinct. Conversely every ordered zero-sum four-tuple arises in this way. Dividing by $4!$ gives $A_4=n(n-1)(n-3)/4!$.

For weight five, the first three columns $a,b,c$ must be linearly independent. If $a+b+c=0$, the sum condition would force the fourth and fifth to coincide. Thus there are $n(n-1)(n-3)$ choices for the first three. Put $t=a+b+c$. The fourth column $d$ must avoid all seven nonzero vectors of their linear span: $a,b,c$ would repeat a column; $t$ would force the fifth column to be zero; and $a+b,a+c,b+c$ would force the fifth column to be $c,b,a$, respectively. Conversely, if $d$ avoids these seven vectors, $e=t+d$ is nonzero and distinct from $a,b,c,d$. This gives $n-7$ choices for $d$ and determines $e$. Dividing by $5!$ yieldsAt $\ell =3$, the last count is zero, as the argument also shows.

The dual code $C_{H,\ell }^{\perp }$ is the row space of $H$, so its codewords are $aH$ for $a\in {\mathbb{F}}_2^{\ell }$. For nonzero $a$, the linear functional $v\mapsto a\cdot v$ takes value one on exactly half the $2^{\ell }$ vectors of $V$, by the same translation-pairing argument used above. Removing the zero vector removes only a zero value, so the Hamming weight is still $m=2^{\ell -1}$. Since $H$ has matrix rank $\ell$, distinct $a$ give distinct codewords. This proves the constant weight of a binary simplex code and its full distribution:In particular, the ordinary weight enumerator of the binary simplex code is $1+n{z}^m$.

Apply the MacWilliams identity to this dual code, whose size is $2^{\ell }=n+1$. For $W_C(z)={\sum }_s{A}_s{z}^s$, the result isThe coefficient of $z^s$ in the second product is a value of the binary Kravchuk polynomialsThese bounds merely enforce $0\le j\le m$ and $0\le s-j\le n-m$. Since $n-m=2^{\ell -1}-1$, they agree with the bounds in the PDF. Therefore the requested formula isFor a further check on the low-weight counts, $n=2m-1$ givesThus $K_3(m)=m-1$, $K_4(m)=\left(\genfrac{}{}{0ex}{}{m-1}{2}\right)$, and $K_5(m)=-\left(\genfrac{}{}{0ex}{}{m-1}{2}\right)$. Substituting these into the boxed formula and using $m=(n+1)/2$ reproduces the three direct combinatorial counts above.

Finally, at $s=n$ the only possible term in the defining sum has $j=m$, so $K_n(m)=(-1{)}^m=1$, because $m=2^{\ell -1}$ is even. ConsequentlyThe unique length-$n$ binary word of weight $n$ is the all-ones word, exactly as proved by the parity-check matrix calculation.

A binary block code of length $N$ is a nonempty subset $C\subseteq {\mathbb{F}}_2^N$. Its size is $|C|$, the number of distinct codewords. The Hamming distance $d(x,y)$ counts coordinates at which two words differ. The code's minimum Hamming distance is ${\min }_{x\ne y\in C}d(x,y)$ when there are at least two words. No pairwise minimum exists for a singleton code; it can be assigned $+\infty$ as a convention when needed.

A binary linear code is a vector subspace of ${\mathbb{F}}_2^N$. If its dimension is $k$, a generator matrix $G$ has a basis of $C$ as its $k$ rows, givingTo match the coordinate-row convention, take a parity-check matrix $P$ of size $N\times (N-k)$, with independent columns, and defineThe transpose $H=P^T$ is the usual parity-check matrix with one column per coordinate. By rank-nullity theorem, the displayed kernel has dimension $k$. The two constructions agree when $GP=0$: the generator's row space lies in the parity-check kernel and has the same dimension, hence equals it. Conversely, for a given $C$, choose the columns of $P$ as a basis of its dual code $C^{\perp }$; orthogonality and the dimension formula give exactly $C$ as the kernel.

For a nonzero linear code, $x-y$ is a nonzero codeword whenever $x,y$ are distinct codewords, and $d(x,y)=\mathrm{wt}(x-y)$. Conversely compare any nonzero codeword with zero. Thus the minimum Hamming distance of a linear code is the least nonzero Hamming weight.

Write $P_i$ for the row associated with coordinate $i$. A nonzero binary vector $x$ is a codeword exactly whenIts nonzero coordinate positions therefore index a linearly dependent set of rows. Conversely, a dependence among a set of rows has coefficients in ${\mathbb{F}}_2$, not all zero; putting these coefficients in the corresponding coordinates gives a nonzero codeword of weight no greater than the number of rows in that set. Taking minima in both directions proves the parity-check dependencies determine minimum distance criterion:Rows here are indexed by positions: equal row vectors at different positions are two distinct members of the indexed family. In the usual matrix convention, the same result refers to columns of $H=P^T$. For the zero code, neither a nonzero codeword nor a dependent coordinate set exists, so both minima can consistently be interpreted as $+\infty$.

For the Hamming code, let $l\ge 1$, set $N=2^l-1$, and let the rows of $P$ be all nonzero vectors of ${\mathbb{F}}_2^l$, each once. These rows include the standard basis, so $P$ has rank $l$ and the code has dimension $N-l$ and size $2^{N-l}$. There is no dependence involving one row, because no row is zero, or two rows, because no rows are equal. For $l\ge 2$, any two distinct nonzero vectors $a,b$ have a distinct nonzero sum $a+b$, which is another row. These three rows sum to zero. Thus $d(C)=3$ for $l\ge 2$.

The perfectness of a Hamming code also has a direct decoding proof. Given any received word $y$, its syndrome is $yP\in {\mathbb{F}}_2^l$. A zero syndrome means $y\in C$. Otherwise it equals exactly one row $P_i$. The vector $y+e_i$ then has syndrome zero and is the unique codeword obtainable by changing one coordinate. There is no other codeword at distance at most one: its necessary correction would have to have the same syndrome, and among zero and all single-coordinate errors the syndromes are precisely the distinct $2^l$ vectors of ${\mathbb{F}}_2^l$. Therefore the radius-one Hamming balls about codewords partition ${\mathbb{F}}_2^N$.

Equivalently, each ball contains $1+N=2^l$ words; disjointness and $2^{N-l}{2}^l=2^N$ show that none are uncovered. Hence every nondegenerate binary Hamming code is a perfect single-error-correcting code. For $l=1$, the construction is the singleton code $\{0\}\subseteq {\mathbb{F}}_2$, whose one radius-one ball is also the whole ambient space. This deals with the degenerate boundary without falsely claiming its minimum distance is three.

A binary linear code of length $n$ is a vector subspace $C\subseteq {\mathbb{F}}_2^n$. The Hamming weight of a word counts its nonzero coordinates, and the code weight is $w(C)={\min }_{0\ne c\in C}\mathrm{wt}(c)$; for a linear code this equals its minimum distance. One may assign infinite minimum weight to the zero code when needed.

For length-$n$ codes with $C_2\subseteq C_1$, the bar product of binary linear codes isIt is a linear code because this map from $C_1\oplus C_2$ is linear and injective. For a nonzero codeword, if $v=0$ then $u\ne 0$ and its weight is $2\mathrm{wt}(u)\ge 2w(C_1)$. If $v\ne 0$, every nonzero coordinate of $v$ makes exactly one of the corresponding coordinates of $u,u+v$ nonzero; henceTaking the minimum givesIn fact equality holds when the codes are nonzero: minimum-weight words $(u,u)$ and $(0,v)$ attain the two candidate weights.

Work over the finite field ${\mathbb{F}}_2$. The given factorization shows that $g$ divides $X^{23}-1$, so it generates a length-$23$ cyclic code $C$. Since $\mathrm{deg}g=11$, the vectors represented byform a basis: their distinct leading degrees prove their linear independence, and every multiple of $g$ representing a polynomial of degree below $23$ has a multiplier of degree at most $11$. Hence $C$ is a binary linear code of dimension $12$, containing $2^{12}$ codewords. The generator has Hamming weight seven, so the minimum Hamming distance of a linear code satisfies $d(C)\le 7$.

The BCH bound used here is the following consecutive-root theorem. If a length-$n$ cyclic code over ${\mathbb{F}}_q$, with $n$ relatively prime to $q$, has a generator polynomial of a cyclic code vanishing at ${\beta }^b,{\beta }^{b+1},\dots ,{\beta }^{b+\Delta -2}$ for a primitive $n$th root $\beta$, then its minimum Hamming distance is at least $\Delta$.

Choose a root $\beta$ of $g$ in an algebraic closure of ${\mathbb{F}}_2$. The factorization implies ${\beta }^{23}=1$, while $g(1)=1$ in ${\mathbb{F}}_2$, so $\beta \ne 1$. As $23$ is prime, $\beta$ has order $23$. The Frobenius endomorphism preserves the roots of a polynomial with coefficients in ${\mathbb{F}}_2$: $g(z^2)=g(z{)}^2$. Thus every ${\beta }^{2^j}$ is a root. In particular, $2^8=256\equiv 3(\mathrm{mod}23)$ shows thatare roots of $g$. These four consecutive roots give $d(C)\ge 5$ by the BCH bound. A further argument is needed to exclude weights five and six.

Put $S(X)=1+X+\cdots +X^{22}$. Cancelling $X+1$ in the supplied factorization gives $g(X)g^{\mathrm{rev}}(X)=S(X)$. In the quotient ring ${\mathbb{F}}_2[X]/(X^{23}-1)$, $X$ is invertible and every cyclic shift of $S$ equals $S$. Since $g^{\mathrm{rev}}(X)=X^{11}g(X^{-1})$,The coefficient of $X^s$ in this circular product is ${\sum }_{i-j\equiv s(23)}{g}_i{g}_j$, the binary inner product of the coefficient vector of $g$ with a cyclic shift of itself. Every coefficient of $S$ is one. The circular autocorrelation of the binary Golay generator therefore implies that all pairs of basis rows $u_i=X^{i}g$, including equal rows, satisfy

Each $u_i$ has Hamming weight seven. Append its parity bit, which is one, and write ${\hat{u}}_i=(u_i,1)$. The extended rows have Hamming weight eight and satisfy ${\hat{u}}_i\cdot {\hat{u}}_j=1+1=0$. Their linear span is precisely the parity extension $\hat{C}$ of $C$, because the appended coordinate is the linear functional equal to the sum of all coordinates.

These extended generators produce a doubly even code. To prove this rather than assume it, useAll the extended generators have weights divisible by four. A sum of previous generators is orthogonal to the next generator, so the intersection size in this identity is even. Induction proves that every word in $\hat{C}$ has Hamming weight divisible by four; this is the orthogonal generators of doubly even codes argument.

An original word of weight five acquires a parity bit and has extended weight six, while a word of weight six acquires a zero parity bit and still has extended weight six. Both contradict divisibility by four. Together with the BCH bound $d(C)\ge 5$ and the weight-seven generator, this completes the BCH and parity-extension proof of the binary Golay distance:

Its radius-three Hamming balls are disjoint, and their volume isThere are $2^{12}$ such balls, so they contain $2^{12}{2}^{11}=2^{23}$ words, exactly the size of the ambient binary cube. They therefore cover the cube, with every word in exactly one ball. Hence

For a binary linear code $C\le {\mathbb{F}}_2^n$, its rank is its dimension of a vector space $r$. Use the weight enumerator conventionwhere $w(c)$ is the Hamming weight. This convention is essential here: $W_C(1,0)$ counts words of weight $n$, rather than zero words. There are $2^r$ codewords, so $\boxed{W_C(1,1)=2^r}$.

There is only one binary word of weight $n$, namely $1=(1,\dots ,1)$. Thus $W_C(1,0)=1$ precisely when $1\in C$. If this holds, the linear map $c\mapsto c+1$ is a bijection of $C$ and replaces weight $w$ by $n-w$; hence $W_C(s,t)=W_C(t,s)$. Conversely this symmetry gives $W_C(1,0)=W_C(0,1)=1$, because the zero word is the unique word of weight zero.

The repetition code consists of $0,1$, givingThe single parity-check code consists of all words of even weight. Exactly $\left(\genfrac{}{}{0ex}{}{n}{j}\right)$ words have weight $j$, so the binomial theorem gives

A linear subspace $W\subseteq {\mathbb{F}}_2^n$ is self-orthogonal if $u\cdot v=0$ for every $u,v\in W$. The standard bilinear form is nondegenerate, whence $\dim W+\dim W^{\perp }=n$ and $\dim W\le \lfloor n/2\rfloor$. The condition with $u=v$ means every vector has even Hamming weight. Such a binary linear code gives the linear algebra behind the Eventown theorem.

A length-$n$, dimension-$k$ binary linear code has full support when no coordinate vanishes on every codeword, equivalently when the union of the supports of a vector of all codewords covers every coordinate. Every coordinate is then a nonzero linear functional on the message vector space ${\mathbb{F}}_2^k$. Translation by a vector on which it is one pairs the zero and one fibers, so exactly $2^{k-1}$ codewords contribute one in that coordinate. Summing these contributions proves the formula. If there are $r$ nonzero columns in a full-rank generator matrix, the same argument gives $r{2}^{k-1}$ instead.