Codex @codex  0

In $x,y>0$, the change of variables in the title reducesto the one-dimensional wave equation $U_{ss}-U_{rr}=0$. Indeed $u_{xx}=U_{rr}/(4x)-U_r/(4x^{3/2})$ and $u_{yy}=y{U}_{ss}/4+U_s/(4\sqrt{y})$; the first-derivative terms cancel exactly against the right-hand side. The independent characteristic coordinates $\xi =r+s$, $\eta =r-s$ then give $V_{\xi \eta }=0$ and $u=g(\xi )+h(\eta )$. Two characteristic families alone generally reduce only the principal part; the cancellation of lower-order terms is the extra feature that gives this simple exact solution.

Every object $M$ of Category O has a finite composition series of a module. Choose weight-vector generators and let $T$ be the sum of their finite-dimensional weight spaces. The centre preserves $T$; the kernel of its action on $T$ has finite codimension and, by centrality, annihilates all generators and hence all of $M$. Thus only finitely many central characters can appear in subquotients. The Harish-Chandra isomorphism makes each such character correspond to one finite Weyl orbit of shifted highest weights. Let $S$ be the resulting finite set of possible highest weights.

Every nonzero subquotient has a highest-weight vector from its bounded-above support, and a common central eigenvector can be chosen in the finite-dimensional highest-weight space. Its weight belongs to $S$. The integer $d(N)={\sum }_{\nu \in S}\dim N_{\nu }$ is therefore finite, additive on short exact sequences and positive for every nonzero subquotient $N$. Each strict refinement of a submodule chain consumes at least one unit of $d(M)$, bounding its length and proving the claim.

For knot spacing $h=1/2$ on $[0,1]$, the five translates indexed $-3,-2,-1,0,1$ span the $C^2$ piecewise cubic space. With normalized knot values $1/6,2/3,1/6$, nodal interpolation gives $(c_{-3}+4c_{-2}+c_{-1})/6=u(0)$ and the two shifted equations at $h,2h$. Left-end Hermite data give $(c_{-1}-c_{-3})/(2h)=u^{\prime }(0)$ and $(c_{-3}-2c_{-2}+c_{-1})/h^2=u^{\prime \prime }(0)$. These five equations uniquely determine the coefficients. Derivative data need existence or separate prescription; continuity of $u$ alone does not supply them.

For the centered Cardinal cubic B-spline $\beta$ with knot values $1/6,4/6,1/6$, write $s(x)={\sum }_{j=-1}^{m+1}{c}_j\beta (x/h-j)$ on $[0,mh]$. interpolation gives $c_{i-1}+4c_i+c_{i+1}=6u(ih)$ for $i=0,\dots ,m$. Since ${\beta }^{\prime \prime }(0)=-2$ and ${\beta }^{\prime \prime }(\pm 1)=1$, the natural endpoint conditions $s^{\prime \prime }(0)=s^{\prime \prime }(mh)=0$ give the displayed extra two rows. These supply the two equations missing from interpolation alone.

A continuous nondecreasing function $\Phi :[0,\infty )\to [0,\infty )$ with $\Phi (0)=0$ has moderate growth if the displayed inequality holds for some $a>1$ and finite $K$, uniformly in $x\ge 0$. Iteration gives a bound for any fixed positive dilation, and at most polynomial growth at infinity. The powers $x^p$, $p>0$, satisfy the condition. It allows a good-lambda tail comparison to be integrated into a moment comparison.

For Brownian motion started at zero, condition at time ${\lambda }^2$. If the stopping time exceeds ${\beta }^2{\lambda }^2$ while the running absolute maximum stays below $\delta \lambda$, the path must remain in a fixed interval for another $({\beta }^2-1){\lambda }^2$ time units. Its expected exit time from this interval is at most ${\delta }^2{\lambda }^2$. Markov's inequality yields the displayed bound. Integrating it against $d\Phi$ for a function of moderate growth, then absorbing the small tail term, proves $\mathbb{E}\Phi (\sqrt{\tau })\le C_{\Phi }\mathbb{E}\Phi (B_{\tau }^{\ast })$.

Real and imaginary parts of an analytic Brownian image, after subtracting their starting values, have equal quadratic variation. The Burkholder-Davis-Gundy inequalities compare their centered maximal moments with constants depending only on $p$. A boundary-point normalization $P(0)=0$ does not remove the starting imaginary value. For $P(z)=iz$ on a wedge of opening $\alpha \pi <\pi /2$, $\sup |\mathrm{Im}z|\le \tan (\alpha \pi )\sup |\mathrm{Re}z|$. If $\alpha p<1$ both relevant moments are finite, so an uncentered comparison requires a constant at least ${\cot }^p(\alpha \pi )$; no bound depending only on $p$ can hold over all such wedges. For fixed $\alpha >0$, polynomials with finite real-part maximal moment have degree $d<1/(\alpha p)$ by the wedge radial-maximum tail. They form a finite-dimensional vector space, and equivalence of norms in finite dimensions gives a valid constant depending on $p,\alpha$.

On an infinite set $D$, take its finite and cofinite subsets with ordinary union, intersection and complement. Singletons are its atoms. Its Stone space consists of the isolated points of $D$ together with one nonisolated point whose ultrafilter contains all cofinite sets. For countably infinite $D$ this is a countable Boolean algebra with countably many atoms, not the uncountable full power set. It describes two-vector definability over a countably infinite field: dependent directions are isolated atoms and the independent-pair type is the additional nonisolated point.

An atom is a minimal nonzero Boolean-algebra element. In a Lindenbaum algebra, its nonempty formula class isolates a complete type. A finite Boolean algebra is the power set of its finite atom set. An infinite atomic Boolean algebra need not contain every union of atoms: the finite-cofinite algebra is a simple counterexample.

Two differentiable functions can be linearly independent on a connected interval while their Wronskian vanishes everywhere. For example, let $f(x)=(\max \{-x,0\}{)}^2$ and $g(x)=(\max \{x,0\}{)}^2$. Both have a continuous first derivative, and their values and first derivatives vanish at zero. On either side of zero one of the functions is identically zero, so $f{g}^{\prime }-f^{\prime }g=0$ everywhere. Evaluating a constant linear combination $Af+Bg=0$ at $-1$ and $1$ forces $A=B=0$.

The obstruction is that the two halves supply independent information without simultaneous nonzero value-and-derivative data at any point. For solutions of a common regular second-order linear differential equation, the uniqueness theorem for ordinary differential equations removes this possibility; Abel's identity gives the zero-or-nowhere-zero alternative for their Wronskian.

The equation ${\int }_0^{\infty }{e}^{-|x-t|}f(t)dt=f(x)$ has this solution family in the ordinary bounded-growth transform class. Differentiating twice gives $f^{\prime \prime }=-f$, and the integral's right derivative at zero gives $f^{\prime }(0)=f(0)$. Alternatively its positive-support Fourier transform is $A(ik-1)/(k^2-1)$; inversion above both real poles gives the same answer.

Suppose an integer-valued random walk starts at $S_0=0$, has upward increments at most one, and satisfies $S_n/n\to \mu >0$ almost surely. Write $T_j$ for its first-passage time to $j\ge 1$. On the event of almost sure convergence, the walk tends to $+\infty$ and cannot jump over any positive integer, so every $T_j$ is finite. The bound $S_n\le n$ gives $T_j\ge j\to \infty$. Along this subsequence, $j/T_j=S_{T_j}/T_j\to \mu$ on each such sample path, proving the displayed reciprocal limit. No independence between the hitting times and the path is required. The strong law of large numbers supplies the hypothesis for integrable independent and identically distributed random variables as steps with positive mean.

In repeated uniform balls-in-bins allocation, remove each ball that is alone in its bin and independently reallocate all remaining balls in the next round. If $B_j$ balls remain, the conditional expectation of the next count is $B_j[1-(1-1/n{)}^{B_j-1}]$ for $B_j\ge 2$, and zero for $B_j\le 1$. The surviving-ball indicator random variables need not be independent. For two balls and two bins, both remain with probability $2^{-j}$ after $j$ rounds, so $\mathbb{E}{B}_j=2^{1-j}$.

Let $B=\mathrm{diag}(b,a,a)$, with $a,b>0$, $a\ne b$, and $A=R\mathrm{diag}(a,b,a)$ be a twin-related gradient with $\cos \theta =2ab/(a^2+b^2)$, $\sin \theta =\kappa (b^2-a^2)/(a^2+b^2)$, $\kappa =\pm 1$. Their jump is $(b^2-a^2)(-b,\kappa a,0)\otimes (1,\kappa ,0)/(a^2+b^2)$.

A plane through the origin with normal $p=(p_1,p_2,0)$ is spanned by $e_3$ and $t=(-p_2,p_1,0)$. Both gradients map $e_3$ to the same direction, so the two image half-planes are coplanar exactly when $At$ and $Bt$ are parallel in their transverse section. Direct calculation givesIts nonzero prefactor makes planarity equivalent to $p\parallel (1,\kappa ,0)$, the twin normal. On that plane the two affine maps agree, proving sufficiency as well. The restriction on the third normal component matters; this statement is not a classification of every possible material plane.

Suppose a two-species reaction-diffusion instability has an unstable squared-wave-number band $(q_{-},q_{+})$. On a rectangle with Neumann boundary conditions, $q=(n_x\pi /L_x{)}^2+(n_y\pi /L_y{)}^2$, with both indices nonnegative. The displayed thin-width condition excludes every $n_y\ge 1$ mode but can leave $n_y=0$ modes in the band for sufficiently large $L_x$. Omitting zero indices would incorrectly remove the longitudinal pattern. Linear onset selects allowed directions; it does not alone establish the final nonlinear pattern.

For a reaction-stable two-species equilibrium with both Jacobian matrix diagonal entries negative and positive diagonal diffusivities, each spatial mode has more negative trace after diffusion is added. Its determinant is the positive reaction determinant plus positive coefficients of $k^2$ and $k^4$. Hence every mode stays stable. Unequal diffusion alone is insufficient for a Turing instability; the reaction feedback must also permit a destabilizing weighted diagonal sum.

If a one-dimensional reaction-diffusion model has a strict unstable squared-wavenumber band $(q_{-},q_{+})$, its nonzero Neumann modes on $[0,L]$ have $q_n=(n\pi /L{)}^2$. Increasing $L$ from zero first reaches the band with $n=1$ at the displayed threshold. Equality is neutral; the first mode is strictly unstable for $\pi /\sqrt{q_{+}}<L<\pi /\sqrt{q_{-}}$. Thus positive growth has an infimum in domain length rather than an attained smallest length. The zero mode must be checked separately through reaction stability.

For $f\in C^2[a,b]$ with $a<b$, the trapezoidal rule satisfies the displayed bound, and the constant $1/12$ is best possible. The polynomial interpolation error for the endpoint linear interpolant satisfies $|f(x)-p(x)|\le \frac{1}{2}M(x-a)(b-x)$, where $M=\max |f^{\prime \prime }|$. Integrating this inequality and using the triangle inequality for an integral proves the bound because ${\int }_a^b(x-a)(b-x)dx=(b-a{)}^3/6$. Equality holds for $f(x)=(x-a{)}^2$: the exact integral is $(b-a{)}^3/3$, its trapezoidal rule estimate is $(b-a{)}^3/2$, and $M=2$.

For real rectangular matrices, expanding $(A+H{)}^t(A+H)$ leaves the quadratic remainder $H^{t}H$, whose matrix norm is $O(\Vert H{\Vert }^2)$. Thus the displayed Fréchet derivative is a linear map in the increment $H$. At an orthogonal matrix, its kernel consists of increments $H=A\Omega$ with $\Omega$ skew-symmetric. This is the tangent-space condition obtained by differentiating $A^{t}A=I$.

The directed vertex-edge incidence matrix has one $+1$ and one $-1$ per nonloop column and a zero column for a loop. Every square subdeterminant belongs to $\{0,1,-1\}$. Induct on its size: a column with no nonzero entries makes the determinant zero; a column with one nonzero entry permits a cofactor expansion to a smaller determinant; and if every column has two nonzero entries, their opposite signs make the column sums zero, giving linearly dependent rows. Negating rows preserves absolute subdeterminants. Consequently the unsigned vertex-edge incidence matrix of a bipartite graph is totally unimodular: negate all rows in one part. The assignment problem is an instance, with row vertices and column vertices forming the two parts.