The source-type similarity solution of $h_t=D(h^2{h}_x{)}_x$ with whole-line conserved area $\mathcal{V}$ is $h=H(t)[1-x^2/L(t{)}^2{]}_{+}^{1/2}$, where $L=2(\mathcal{V}/\pi {)}^{1/2}(Dt{)}^{1/4}$ and $H=(\mathcal{V}/\pi {)}^{1/2}(Dt{)}^{-1/4}$. Its volume flux vanishes at the finite front, while the velocity tends to $\dot{L}=L/(4t)$. A reflecting half-line uses twice its prescribed area in these formulas.

For the simplified quadrupole equation ${\sigma }_{\gamma }^{\prime }+4k^2{\theta }_{\gamma }/15=-{\sigma }_{\gamma }/{\tau }_c$, neglecting its time derivative and gravitational driving gives ${\sigma }_{\gamma }=-{\tau }_c{\delta }_{\gamma }^{\prime }/5$. A radiation-dominated inertia then obeys ${\delta }_{\gamma }^{\prime \prime }+4{\tau }_c{k}^2{\delta }_{\gamma }^{\prime }/15+k^2{\delta }_{\gamma }/3=0$. On slowly varying subhorizon scales the amplitude is damped by $\exp [-(2/15)k^2\int {\tau }_{c}d\tau ]$. This coefficient belongs to that truncated collision model, rather than the polarization-inclusive Silk damping equation.

The magnitude of the phase velocity of a real-frequency plane Fourier mode is $|\omega |/|\mathbf{k}|$. In an anisotropic medium it depends on the direction of the wave vector and need not equal the group velocity magnitude.

For weak quadratic coupling between frequencies one and two, the first oscillator's complex amplitude can obey $2i{A}_T+\alpha A+b\overline{A}=0$ with a constant real second-oscillator amplitude $b$. In real coordinates this is a linear slow system with squared growth rate $(b^2-{\alpha }^2)/4$. Exponential amplification occurs when the modulation amplitude exceeds detuning, $|b|>|\alpha |$. The method of multiple scales is valid over bounded slow time while the physical amplitudes remain in the weak-coupling regime. At equality the slow matrix can have a Jordan block and produce linear growth for some initial phases.

For a nonzero closed subspace of a Hilbert space $V$ and a closed subspace of a Hilbert space $W$, this angle measures the worst loss under projection from $V$ to $W$. Its positive cosine is a uniform lower bound for $P_W{|}_V$. The order of the subspaces matters in general. This is distinct from the smallest angle between two subspaces, whose cosine is a supremum. For equal finite dimensions the cosine is the smallest singular value of the cross Gram matrix of orthonormal bases. When $V$ is zero, the infimum is over an empty unit sphere and does not define an angle in $[0,\pi /2]$; use a lower-bound formulation instead.

For $w(x)=x^a{e}^{-x}$ on $[0,\infty )$, $a>-1$, the monic Generalized Laguerre polynomials have squared norms $h_j=j!\Gamma (j+a+1)$. The corresponding orthogonal polynomial projection kernel is $(xy{)}^{a/2}{e}^{-(x+y)/2}{\sum }_{j=0}^{m-1}j!L_j^{(a)}(x)L_j^{(a)}(y)/\Gamma (j+a+1)$ for nonnegative $x,y$, and zero otherwise.

At a nonzero optimum, the derivative of expected utility of a proportional transaction cost payoff is zero. Normalized marginal utility therefore prices $X$ at $\epsilon$ for a positive optimum or $-\epsilon$ for a negative optimum. At a zero optimum the one-sided derivative inequalities put $\mathbb{E}X$ inside that interval, so density one works. The competing one-sided-payoff alternative prevents the coercive-maximizer argument from being assumed when a net gain has only one sign.

If two parties share a pure resource of Schmidt rank $R$, a fully refined branch of their local operations has Kraus operator $K_{\mu \nu }={\sum }_{j=1}^R\sqrt{p_j}{A}_{\mu j}\otimes B_{\nu j}$. Hence its operator Schmidt rank is at most $R$. Local ancillas, locally adaptive readouts and refined discarded environments are included in the branch operators. Shared classical randomness only mixes such branches. Classical comparison of records does not increase their operator Schmidt rank.

Over the reals or a finite field with more than two elements, the normalizer of all invertible diagonal matrices in $G{L}_2$ consists of monomial matrices and has quotient $C_2$ over that diagonal subgroup. Over ${\mathbb{F}}_p$ its size is $2(p-1{)}^2$ for $p>2$. For $p=2$ the diagonal subgroup is trivial, so its normalizer is all of $G{L}_2({\mathbb{F}}_2)$, of order six.

At an acoustic boundary, the surface acoustic impedance is the pressure amplitude divided by the velocity amplitude directed into that boundary. With upward surface velocity $-V$ and harmonic convention $e^{i\omega t}$, it is $Z=P/V$. Reflection compares this impedance with the incident fluid's normal acoustic impedance. The velocity orientation and harmonic convention must be fixed before interpreting signs of inertial, elastic, and radiation terms.

Compile each CNOT gate into a Hadamard gate, a Controlled-Z gate, and another Hadamard gate on its target. Every one-bit teleportation implementing a Hadamard gate uses a fixed angle-zero basis. A Pauli frame propagates by classical binary updates without changing a measurement basis. All internal measurements, and any fixed computational output measurements, therefore share one simultaneous layer. Resource preparation and classical processing are not included in this logical measurement depth.

For a coherent club set sequence, the limit points of $C_{\delta }$ form a club set whenever $\mathrm{cf}(\delta )>\omega$. Along these points, $\mathrm{otp}(C_{\gamma })=\mathrm{otp}(C_{\delta }\cap \gamma )$ strictly increases, so a fixed fiber $E_{\xi }$ meets this club set at most once and cannot reflect at $\delta$. Under the square principle order-type bound, the fibers with $\xi \le \kappa$ partition all limit ordinals below ${\kappa }^{+}$ into $\kappa$ non-reflecting pieces.

Decide whether a directed graph has a nonempty directed cycle; self-loops count. This problem is NL-complete. Membership guesses a returning walk of at most the vertex count. For hardness, layer a reachability instance into $N+1$ layers with wait arcs, then add only the backward edge from the target in the last layer to the source in the first. The otherwise acyclic layering has a cycle exactly when the original target is reachable. Zero-length paths must not be treated as cycles.

For a positive-support exponential family $f(x\mid \theta )=p(x)e^{-\theta x}/q(\theta )$, normalization gives $m(\theta )=-q^{\prime }(\theta )/q(\theta )$. The natural conjugate prior proportional to $q(\theta {)}^{-k}{e}^{-k\mu \theta }$ has logarithmic derivative $k(m(\theta )-\mu )$. If its endpoint density values vanish, integration gives prior expected value $\mathbb{E}[m(\Theta )]=\mu$. Observations update $k$ to $k+n$ and $\mu$ to $(k\mu +{\sum }_i{x}_i)/(k+n)$. The endpoint control for a Laplace-family conjugate posterior justifies the same mean identity after updating, giving an exact credibility estimate with credibility factor $n/(n+k)$.

For an equally spaced design with independent errors of variance at most ${\sigma }^2$ and a regression function with Lipschitz constant $L$, the unit-width box kernel estimator averages the $N_x$ observations in the window. Its bias is at most $Lh/2$, and its variance is at most ${\sigma }^2/N_x$. The window occupancy for an equally spaced regression design gives, whenever $nh\ge 2$,In the usual range $h\le 1$, this is $O(\sigma /\sqrt{nh}+Lh)$ uniformly up to the boundary. For arbitrarily large bandwidth, the stochastic error cannot continue to decrease as $1/\sqrt{nh}$: once all observations are included the estimator is simply their mean.

A local constant estimator reproduces constants but need not reproduce an affine regression function near a support boundary. Let $x_i=i/n$, take $0<h\le 1$ and the regression kernel $K=1_{[-1,1]}$, and suppose $m(x)=x$. At target zero, the noiseless Nadaraya–Watson estimator averages $i/n$ for $1\le i\le k=\lfloor nh\rfloor$. Whenever $k\ge 1$, its exact fitted value isSince $m(0)=0$, this is the bias of an estimator when observation errors have zero expected value. Under $h\to 0$ and $nh\to \infty$, it is asymptotic to $h/2$. The one-sided boundary window has a nonzero first coordinate moment, explaining the first-order bias. An invertible local linear regression fit reproduces $m(x)=x$ exactly at the target and removes this term. If $k=0$, the local constant average is undefined rather than zero, so a nonempty window is a necessary condition.

Routes with positive logarithmic utility weights choose rates inversely proportional to their total resource price. Each resource changes its price multiplicatively according to demand minus its increasing supply. For continuous strictly increasing $q_j$ with $q_j(0)=0$, the potential $V={\sum }_r{w}_r\log ({\sum }_{j\in r}{\mu }_j)-{\sum }_j{\int }_0^{{\mu }_j}{q}_j(u)du$ is strictly concave and has nonnegative derivative along the dynamics. Positive-price convergence for increasing resource supplies requires positivity on used resources; zero-price faces are invariant.

For counts with a multinomial distribution, this is twice the log likelihood ratio of the saturated cell-probability estimate to fixed probabilities $p_i$. Zero-count terms have limiting value zero. Simulation under the specified probabilities gives a direct reference distribution when asymptotic approximations are unsuitable.