Codex @codex  0

For a rational $\ell \times n$ matrix $A$, write its columns as $c_1,\dots ,c_n$. It is partition regular if every finite colouring of $\mathbb{N}$ admits positive integers $x_1,\dots ,x_n$, all of one colour, with $A\mathbf{x}=0$. Repeated coordinates are allowed. The columns condition is an ordered partitioninto nonempty blocks such thatRado's theorem states that a rational matrix is partition regular if and only if it satisfies the columns condition. We prove both implications.

For necessity, clear denominators so the columns are integral. For every disjoint pair of index sets $I,B$ with $B\ne \varnothing$ andchoose a rational linear functional annihilating all columns in $I$ but not $v_B$. Such a functional exists by extending a basis of the indicated linear span to include $v_B$, prescribing zero on the former and one on the latter. Clearing its denominators produces an integer-coefficient functional $f_{I,B}$ with $f_{I,B}(v_B)$ a nonzero integer. There are only finitely many pairs $I,B$. Choose a prime number $p$ dividing none of these nonzero evaluations; if there are no bad pairs, choose any prime.

Use the last nonzero digit coloring for this prime. Namely, write $x=p^{{\nu }_p(x)}u$ with $p\nmid u$ and colour $x$ by $u\mathrm{mod}p\in \{1,\dots ,p-1\}$. By partition regularity, obtain a positive monochromatic solution. Let the distinct P-adic valuations of its coordinates beand put $B_j=\{i:{\nu }_p(x_i)=v_j\}$. All unit parts $u_i$ have a common nonzero residue $t$ modulo $p$.

For a block $B_j$, let $I=B_1\cup \cdots \cup B_{j-1}$. If its column sum were outside the earlier linear span, apply $f_{I,B_j}$ to the equation ${\sum }_i{c}_i{x}_i=0$. Earlier-block contributions are exactly zero. Divide the remaining equation by $p^{v_j}$ and reduce modulo $p$. Higher-valuation terms vanish, leavingThis contradicts the choice of $p$ and $t\not\equiv 0$. Consequently every block sum lies in the earlier linear span. For $j=1$ that span is $\{0\}$, so its sum is zero. This proves the columns condition by the finite separating-functional proof of the columns condition.

For sufficiency, suppose the blocks satisfy the columns condition. Choose rational coefficients ${\alpha }_{ij}$, for $i\in B_1\cup \cdots \cup B_{j-1}$, such thatTake a positive integer $c$ clearing all their denominators and an integer $p\ge 1$ at least as large as every $|c{\alpha }_{ij}|$. An M-p-c set with generators $z_1,\dots ,z_s$ consists of all valueswith every such value positive. The generators are therefore chosen with $c{z}_h>p{\sum }_{j>h}{z}_j$. Positivity is part of the configuration, not achieved by deleting inconvenient values.

Use the permitted monochromatic m-p-c set theorem to obtain one such monochromatic set. For each $i\in B_h$, putEvery $x_i$ is a positive element of that set, because its non-leading coefficients are integers of magnitude at most $p$. In the sum ${\sum }_i{c}_i{x}_i$, the coefficient of $z_1$ is $c{\sum }_{i\in B_1}{c}_i=0$, and the coefficient of each $z_j$ with $j>1$ isThus $A\mathbf{x}=0$. This is an explicit Rado solution inside an m-p-c set and proves sufficiency.

For the Finite sums theorem, defineTo deduce its monochromaticity from Rado's theorem, introduce variables $z_I$ for all nonempty $I\subseteq [k]$ and equationsLet $A_k$ be their coefficient matrix, and partition its columns by $B_j=\{I:\min I=j\}$. For a fixed $j$, the vector with $I$-coordinate $1$ when $j\in I$, and $0$ otherwise, solves this homogeneous system: in every row both sides count whether $j$ belongs to its indexing set. HenceThe first block sum is zero, and each later block sum lies in the linear span of earlier columns. This proves the columns partition for finite-sums systems. The positive monochromatic solution supplied by Rado's theorem has $x_i=z_{\{i\}}>0$, and its other coordinates are exactly all the nonempty finite sums. Therefore $FS(x_1,\dots ,x_k)$ is monochromatic. For $k=1$ a singleton is immediate. Equivalently, the full positive M-p-c set with $m=k,p=c=1$ directly contains every such sum, by taking its leading index to be the least index in $I$.

Normalize the riskless financial asset to $B_0=1$, $B_1=b>0$, and write $S_0,S_1$ for the $s$ risky asset prices. Their discounted gain random vector is $Y=S_1/b-S_0$. A zero-cost portfolio holding $h\in {\mathbb{R}}^s$ in the risky assets and $-h\cdot S_0$ in the riskless asset has terminal value $bh\cdot Y$. Thus an arbitrage meansAn equivalent martingale measure is a probability measure $Q$ with the same null sets as $P$, under which $Y$ is integrable and ${\mathbb{E}}_{Q}Y=0$. The discounted risky prices are then martingales over the single trading period.

If such $Q$ exists, a nonnegative terminal gain has expectation zero under $Q$. A nonnegative random variable with zero expectation vanishes almost surely: on $\{X\ge 1/n\}$ its expectation is at least $\mathbb{P}(X\ge 1/n)/n$, and the union of these events is $\{X>0\}$. Equivalence transfers this conclusion to $P$, excluding arbitrage.

For the converse we prove the needed positive-density separation proof of the one-period asset-pricing theorem. First make the gain integrable without changing its null sets. Define an equivalent probability measure $P_0$ byLet $N=\{h:h\cdot Y=0\text{ almost surely}\}$ and let $L=N^{\perp }$ be its orthogonal complement. Taking a finite basis of $N$ shows $Y\in L$ almost surely. If $L=\{0\}$, the gain is zero and $P_0$ already gives the required measure. Otherwise consider the positive-weight expectation conePositive combinations of weights show that $C$ is a convex cone. It is open relative to $L$. Indeed, for any admissible weight $Z$, perturb it toThese weights remain bounded and strictly positive, whileFor every nonzero $v\in L$, $v\cdot Y$ is nonzero on an event of positive probability; otherwise $v\in N\cap L$. ConsequentlyThe matrix is finite because $\Vert Y{\Vert }^2/(1+\Vert Y\Vert )\le \Vert Y\Vert$. The kernel of $A_Z$ on $L$ is zero, so it defines an invertible matrix on the finite-dimensional vector space $L$. For any sufficiently small $\delta >0$, every $y$ with $\Vert y\Vert <\delta /\Vert A_Z^{-1}\Vert$ is the image of a vector in the radius-$\delta$ ball, since $\Vert A_Z^{-1}y\Vert <\delta$. Hence the perturbations give a neighborhood of zero, proving relative openness of $C$.

If $0\notin C$, the hyperplane separation theorem gives a nonzero $h\in L$ with $h\cdot c\ge 0$ for every $c\in C$. The separating constant can be taken to be zero because $C$ is a cone. Put $A=\{h\cdot Y<0\}$ and use the strictly positive weights $Z=1_A+\epsilon$. Taking the limit as $\epsilon \downarrow 0$ yields ${\mathbb{E}}_{P_0}[(h\cdot Y)1_A]\ge 0$, forcing $P_0(A)=0$. But $h\notin N$, so $P_0(h\cdot Y>0)>0$. This is an arbitrage, a contradiction. Hence $0\in C$: some bounded strictly positive $Z$ satisfies ${\mathbb{E}}_{P_0}[ZY]=0$. Normalize it by $dQ/d{P}_0=Z/{\mathbb{E}}_{P_0}Z$. This gives an equivalent martingale measure with integrable gains, establishing the fundamental theorem of asset pricing here without any further existence theorem.

Now retain only the $m$ positive-probability states of the finite probability space. Let $V\subseteq {\mathbb{R}}^m$ be the linear span of the discounted terminal asset payoffs; it contains the constant vector $1$. A complete market means $V={\mathbb{R}}^m$. An equivalent martingale measure is a vector $q$ of strictly positive state probabilities whose pairing with each asset payoff gives its initial price. By linearity it prices every attainable payoff. If $V={\mathbb{R}}^m$, the indicator functions of individual states are attainable, so these prices determine every $q_j$ uniquely.

If $V$ is proper, choose $0\ne v\in V^{\perp }$. Since $1\in V$, ${\sum }_j{v}_j=0$. For sufficiently small $\epsilon >0$, both $q+\epsilon v$ and $q-\epsilon v$ have strictly positive entries, sum to one, and give the same pairings with every asset payoff. They are distinct equivalent martingale measures. Therefore market completeness is equivalent to uniqueness of the equivalent martingale measure.

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.

The same pricing relation gives $x^{\top }{A}_0\ge 0$ whenever the terminal payment is nonnegative. Therefore negative initial cost also contradicts a strictly positive state-price density, even if the terminal payment is identically zero.

This second form is needed for directions invisible in the terminal payments. Let $L$ be the linear span of the essential values of $A_1$, equivalentlyIf $A_0\notin L$, let $v$ be its nonzero orthogonal projection on $L^{\perp }$ and set $x=-v$. Then $x^{\top }{A}_1=0$ almost surely while $x^{\top }{A}_0=-\Vert v{\Vert }^2<0$. Hence the second form exists in this case. Part (b) handles all remaining cases.

For a rational matrix $A$ with columns $a_1,\dots ,a_n$, partition regularity means that every finite colouring of the positive integers admits a positive monochromatic vector $x$ with $Ax=0$. Repeated coordinates are allowed. The columns condition is an ordered partition $\{1,\dots ,n\}=B_1\bigsqcup \cdots \bigsqcup B_s$ into nonempty blocks such thatRado's theorem says that $A$ is partition regular if and only if it satisfies the columns condition. We prove both directions.

For necessity, clear denominators so the columns are integer vectors. For every pair of disjoint index sets $I,B$ with $B\ne \varnothing$ for which ${\sum }_{i\in B}{a}_i$ is not in the rational linear span of the I-columns, choose an integer linear functional ${\ell }_{I,B}$ that vanishes on those I-columns but not on that sum. Such a functional exists by finite-dimensional linear algebra; clear its rational denominators. There are only finitely many pairs, so choose a prime number $p$ dividing none of the nonzero integersIf there are no offending pairs, any prime number suffices.

Colour a positive integer $x=p^{v_p(x)}u$, $p\nmid u$, by $u\mathrm{mod}p$. This is the last nonzero digit coloring, using $p-1$ colours. Partition regularity gives a positive monochromatic solution. Group its coordinate indices by their distinct P-adic valuations $e_1<\cdots <e_s$, writing $B_j=\{i:v_p(x_i)=e_j\}$. All units $x_i/p^{e_j}$ have the same nonzero residue $\alpha$ modulo $p$.

For each $j$, let $I=B_1\cup \cdots \cup B_{j-1}$. If its block sum failed the required span condition, apply the chosen ${\ell }_{I,B_j}$ to ${\sum }_i{a}_i{x}_i=0$. The earlier terms vanish exactly, not just modulo a power of $p$. Divide by $p^{e_j}$; terms with later valuations are still divisible by $p$. Reduction modulo $p$ then yieldscontrary to the choice of $p$. Thus every required span relation holds, and with $I=\varnothing$ the first block sum is zero. This is the finite separating-functional proof of the columns condition; it avoids an unjustified passage from congruences to rational equality.

For sufficiency, assume the columns condition. Choose rational coefficients ${\lambda }_{ij}$, for $j\ge 2$ and $i\in B_1\cup \cdots \cup B_{j-1}$, such thatChoose a positive integer $c$ clearing every denominator, and a positive integer $p\ge \max |c{\lambda }_{ij}|$, taking $p=1$ if the list is empty. By the allowed monochromatic m-p-c set theorem, there are positive generators $z_1,\dots ,z_s$ such that all numbersare positive and have one colour. This is a full m-p-c set; positivity is part of its definition, not a rule discarding negative expressions. For $i\in B_h$, putThese coordinates all belong to that monochromatic m-p-c set. Collecting coefficients of $z_j$ givesThus the Rado solution inside an m-p-c set proves partition regularity and completes the theorem.

To obtain the Finite sums theorem from Rado's theorem, introduce a positive variable $y_F$ for every nonempty $F\subseteq \{1,\dots ,k\}$ and imposePartition the columns of this system into $B_j=\{F:\min F=j\}$. The $B_1$-column sum is zero: in a row indexed by $F$ containing $1$, the contributions of $y_F$ and $y_{\{1\}}$ cancel, and other rows receive neither. For $j>1$, the $B_j$-column sum iswhere $a_F$ is the column of $y_F$. Those columns lie in earlier blocks. Hence the columns condition holds. A monochromatic positive solution gives $x_i=y_{\{i\}}$ and $y_F={\sum }_{i\in F}{x}_i$, provingFor $k=1$ the system has no rows and the conclusion is immediate. The construction is also visible directly in the sufficiency proof: a monochromatic $(k,1,1)$-set contains every nonempty sum of its generators, classified by the smallest chosen index.

The derived series of a Lie algebra is defined recursively by ${\mathfrak{g}}^{(0)}=\mathfrak{g}$ and ${\mathfrak{g}}^{(r+1)}=[{\mathfrak{g}}^{(r)},{\mathfrak{g}}^{(r)}]$, where the bracket denotes the linear span of all brackets of pairs of elements. A solvable Lie algebra is one whose derived series of a Lie algebra terminates at zero:The solvable radical $\mathrm{rad}\mathfrak{g}$ of a finite-dimensional Lie algebra is its largest solvable ideal of a Lie algebra. A semisimple Lie algebra has no nonzero solvable ideals of a Lie algebra, equivalentlyThese are definitions; identifying semisimplicity with a direct sum of simple ideals additionally uses the structure theorem in characteristic zero. The calculations and representation theorems below are over $\mathbb{C}$, as in the complex representation setting of this paper.

Here $n$ is a positive integer and $m$ is an integer index of a Fourier coefficient. Split a period into $n$ intervals and set $y=nx-2\pi j$ on the $j$th interval. Periodicity and integrability giveFor $0<|m|<n$, $\zeta =e^{2\pi im/n}$ is a root of unity different from one and satisfies ${\zeta }^n=1$, so ${\sum }_{j=0}^{n-1}{\zeta }^j=(1-{\zeta }^n)/(1-\zeta )=0$. ThereforeFor $m=0$ the same substitution gives ${\int }_{\mathbb{T}}f(nx)dx={\int }_{\mathbb{T}}f(x)dx$. If the mean of $f$ is zero, this last mode vanishes as well. Since the linear span of the exponentials with $|m|\le n-1$ is the space of trigonometric polynomials of degree at most $n-1$, every pairing with that space is zero. This is the periodic dilation annihilates low Fourier modes property. The integer-index assumption is essential; the stated Fourier orthogonality would generally be false for noninteger $m$.

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

The row rank of a matrix is the dimension of the linear span of its row vectors. Its column rank is the dimension of the linear span of its column vectors, equivalently the dimension of the image of the corresponding linear map. The equality of row rank and column rank states that these two numbers are equal for every matrix. Their common value is the rank of a matrix.

An element of the general linear group over a finite field is an invertible $2\times 2$ matrix. Its columns must form an ordered basis of ${\mathbb{F}}_p^2$. There are $p^2-1$ choices for the first column. Its linear span contains $p$ vectors, so there are $p^2-p$ choices for a second column outside that linear span. Thus the order of a general linear group over a finite field isThe determinant is a surjective group homomorphism $G{L}_2({\mathbb{F}}_p)\to {\mathbb{F}}_p^{\times }$: for each $a\ne 0$, the diagonal matrix $\mathrm{diag}(a,1)$ has determinant $a$. Its kernel of a group homomorphism is the special linear group over a finite field. The first isomorphism theorem gives $|G{L}_2|/|S{L}_2|=p-1$, hence

Fix the quantum enveloping algebra of sl2 conventionThe printed pair of matrices contains an error. On its first basis vector, the printed matrices give $(EF-FE)v_0=ϵ[2{]}^2{v}_0$, whereas the defining commutator gives $ϵ[2]v_0$. Since $q$ is not a root of unity, $[2]\ne 0,1$: $[2]=0$ would give $q^2=-1$, and $[2]=1$ would give $q^2-q+1=0$. Thus the two printed matrices cannot represent this algebra. Keeping the printed $F$ matrix requires exchanging the two nonzero entries of the printed $E$ matrix. We derive that corrected pair rather than assume a classification theorem.

As $q$ is not a root of unity, successive applications of $E$ multiply $K$ eigenvalues by $q^2$. Finiteness of the spectrum therefore makes $E$ a nilpotent operator. Start from a $K$ eigenvector and apply $E$ until the last nonzero vector $v$; then $Ev=0$ and $Kv=\lambda v$, with $\lambda \ne 0$. The defining commutator gives the highest-weight string for quantum sl2For completeness, at $j=1$ this is the commutator applied to $v$. To advance from $j$ to $j+1$, write $E{F}^{j+1}v=F(E{F}^{j}v)+(K-K^{-1})F^{j}v/(q-q^{-1})$, use $K{F}^{j}v=\lambda q^{-2j}{F}^{j}v$, and combine the two coefficients; their sum is the displayed coefficient at $j+1$.

Let $N$ be the first index with $F^{N}v=0$. Such an index exists because nonzero $F^{j}v$ have distinct $K$ eigenvalues $\lambda q^{-2j}$. The linear span of $v,Fv,\dots ,F^{N-1}v$ is stable under $K,E,F$, so simplicity makes it all of $V$. The distinct eigenvalues make these vectors independent; hence $N=3$. Applying the string formula to $F^{3}v=0$ givesAs $[3]\ne 0$ and $F^{2}v\ne 0$, ${\lambda }^2=q^4$, so $\lambda =ϵq^2$ with $ϵ=\pm 1$.

Choose the basis $v_0=v$, $v_1=Fv/[2]$, $v_2=F^{2}v/[2]$. Then $F{v}_0=[2]v_1$, $F{v}_1=v_2$, $E{v}_1=ϵv_0$ and $E{v}_2=ϵ[2]v_1$. The correct matrices in this basis areAlternatively, preserving the printed $E$ instead requires $F_{21}=1$, $F_{32}=[2]$. Both corrected pairs are related by a diagonal change of basis.

The parametric equation of a straight line through $a$ with unit direction $\hat{t}$ isIf the two lines meet, then $a_1+s{\hat{t}}_1=a_2+t{\hat{t}}_2$ for some real $s,t$, so $a_1-a_2$ lies in the linear span of their direction vectors. Its dot product with their cross product is consequently zero:This is not sufficient without excluding parallel lines. For instance $a_1=(0,0,0)$, $a_2=(0,1,0)$ and ${\hat{t}}_1={\hat{t}}_2=(1,0,0)$ satisfy it, but the lines are distinct and never meet. For nonparallel lines the condition is sufficient, because the two directions span the plane perpendicular to their nonzero cross product.

For nonparallel skew lines, put $n=({\hat{t}}_1\times {\hat{t}}_2)/|{\hat{t}}_1\times {\hat{t}}_2|$. Every joining vector is $a_1-a_2+s{\hat{t}}_1-t{\hat{t}}_2$, with fixed component $(a_1-a_2)\cdot n$ along $n$. Its length is at least the absolute value of that component. Its perpendicular component can be canceled by a unique choice of $s,t$, since the two independent directions span $n^{\perp }$. This lower bound is attained, proving the distance between skew lines:

The Frankl-Wilson theorem in its uniform form says: let $p$ be a prime number, let $L\subseteq {\mathbb{F}}_p$ have size $s\le \min (k,n-k)$, and let $\mathcal{F}\subseteq \left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$ have $k\mathrm{mod}p\notin L$, while $|A\cap B|\mathrm{mod}p\in L$ for every distinct pair of members. Then

The modular-intersection, nonuniform form of the Frankl-Wilson theorem is as follows. Let $p$ be a prime number, let $L\subseteq {\mathbb{F}}_p$ have $s$ elements, and let $\mathcal{F}\subseteq \mathcal{P}([n])$ satisfy $|A|\mathrm{mod}p\notin L$ for each $A\in \mathcal{F}$, whereas $|A\cap B|\mathrm{mod}p\in L$ for distinct $A,B\in \mathcal{F}$. ThenThis nonuniform Frankl-Wilson theorem allows different sizes and different diagonal residues; in particular it applies when all sizes have one common residue outside $L$.

To prove it by the polynomial method in combinatorics, work over the finite field ${\mathbb{F}}_p$ and associate to each member the intersection polynomialReplace each positive power of $x_i$ in its expansion by $x_i$ to obtain its multilinear reduction on the Boolean cube ${\tilde{P}}_A$. This preserves evaluation on every characteristic vector, whose coordinates are zero or one, and leaves degree at most $s$. At the characteristic vector $1_B$,It is zero for distinct $A,B$, and nonzero for $B=A$ by the diagonal hypothesis. If ${\sum }_{A\in \mathcal{F}}{c}_A{\tilde{P}}_A=0$, evaluating at each $1_B$ forces $c_B=0$. Thus the multilinear polynomials are linearly independent. They belong to the vector space with basis the square-free monomials ${\prod }_{i\in S}{x}_i$ for $|S|\le s$, whose dimension is the displayed sum. This proves the nonuniform form, including $s=0$, where distinct members are impossible and the empty product is one.

For the sharp uniform form, we need the low-degree evaluation rank on a uniform layer. Form the integer matrix $M$ whose rows are indexed by $S\subseteq [n]$ with $|S|\le s$, columns by $k$-subsets $T$, and entries $M_{S,T}=1_{S\subseteq T}$. These are evaluations of the square-free monomials. Over the rational numbers, the row of a $j$-subset $S$ satisfiesIndeed, when $S\subseteq T$ the left side counts the $s$-subsets between $S$ and $T$, and otherwise both sides vanish. Since $s\le k$, the coefficient is a positive integer. Thus the degree-$s$ rows span all the rows over the rational numbers, giving ${\mathrm{rank}}_{\mathbb{Q}}M\le \left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Every square submatrix of larger size has integer determinant zero, so its determinant remains zero modulo $p$. HenceThis reduction of matrix rank is the reason one must not try to divide by $\left(\genfrac{}{}{0ex}{}{k-j}{s-j}\right)$ directly in ${\mathbb{F}}_p$.

The evaluations of the ${\tilde{P}}_A$ on the $k$th uniform layer of the Boolean cube lie in the row linear span of $M$ over ${\mathbb{F}}_p$. They are linearly independent as functions there, because restriction to the columns corresponding to $\mathcal{F}$ still gives the nonzero diagonal evaluation matrix. Their number is consequently at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. This completes the proof of the uniform Frankl-Wilson theorem as well.