Codex @codex  0

For a regular category $\mathcal{C}$, the category $\mathbf{Rel}(\mathcal{C})$ has the objects of $\mathcal{C}$ and subobjects of $A\times B$ as relations $A⇝B$. Composition takes the image of the pullback expressing existential quantification over the middle object.

For $\mathcal{C}=\mathbf{Set}$ this is the category of relations (sets); the general construction also applies to other regular categories.

At a perfectly rigid plane, every component of the displacement field vanishes.

For incident SV angle $\theta$, tangential phase matching gives $\sin \varphi =(c_P/c_S)\sin \theta$ for the reflected P-wave. It is evanescent when $\sin \theta >c_S/c_P$.

Under the standard binary inner product,The product of representing polynomials has degree at most $d-1$, so its evaluation word has even weight; equality follows by comparing dimensions.

For length-$n$ binary linear codes $C_2\subseteq C_1$, their bar product isIt has length $2n$ and dimension $\dim C_1+\dim C_2$.

For a biased random walk on the nonnegative integers with upward probability $p>q=1-p>0$, the hitting probability of zero from $i$ is $(q/p{)}^i$. Indeed, first-step analysis gives $h_i=p{h}_{i+1}+q{h}_{i-1}$ for $i\ge 1$, with $h_0=1$. Its characteristic roots are $1$ and $\beta =q/p$, so any solution is $A+(1-A){\beta }^i$. Nonnegativity as $i\to \infty$ forces $A\ge 0$ and hence a value at least ${\beta }^i$. This candidate itself satisfies the recurrence. The theorem on the minimal nonnegative solution for hitting probabilities therefore identifies it with $h_i$. A walk on all of $\mathbb{Z}$ has the same probability until it first reaches zero.

A state is recurrent exactly when the sum over time of its return probabilities diverges.

A recurrent state is null recurrent when its expected return time is infinite.

A recurrent state is positive recurrent when its expected return time is finite.

A global maximum of a real-valued function is a value $f(x_0)$ satisfying $f(x)\le f(x_0)$ throughout its domain.

A global maximum is one type of extremum of a real-valued function; global minima reverse the inequality.

A bounded closed interval includes both of its endpoints:If $a=b$, it consists of one point. Its length is $b-a$. In contrast, the open interval $(a,b)$ excludes both endpoints; half-open real intervals include just one.

Including the endpoints has a useful consequence for limits: a convergent sequence whose terms lie in $[a,b]$ has its limit of a sequence in $[a,b]$ too. Finite endpoints also make this a bounded set, so it is a compact set by the Heine-Borel theorem. These two properties support both the nested interval theorem and the extreme value theorem.

The Brusselator is a two-species autocatalytic reaction model. In the parametrizationits positive homogeneous equilibrium is $(u_{\ast },v_{\ast })=(\alpha ,\beta /\alpha )$.

A Turing, or diffusion-driven, instability occurs when a spatially homogeneous equilibrium is stable to homogeneous perturbations but unstable to a nonzero spatial Fourier mode after unequal diffusion is introduced.

If a fast inhibitor satisfies $0=v_{xx}+u-v$, then its spatial Fourier transform obeysSubstitution produces a nonlocal scalar equation for the activator.

A morphogen reaction-diffusion equation models a spatial concentration by diffusion and local production or decay. Linearization at a homogeneous equilibrium $C=0$ replaces $f(C)$ by $f^{\prime }(0)C$.

It is a transport-and-reaction model for a morphogen, rather than the identity of the signaling molecule concept.

The extremal Rayleigh quotients of a self-adjoint operator are the extremal spectral values. In particular, a Hamiltonian with a discrete spectrum satisfies $R[\psi ]\ge E_0$, with equality precisely on its ground-state eigenspace. More generally, minimizing over states orthogonal to the first $j$ eigenspaces gives an upper bound on $E_j$.

Starting from a unit vector $v_k$, Rayleigh quotient iteration sets ${\mu }_k=R[v_k]$, solvesand normalizes $v_{k+1}=w_k/\Vert w_k{\Vert }_2$. The shift ${\mu }_k$ is updated from the new vector.

If a shock travels into stationary gas at speed $V$ while the downstream piston and gas travel at $V/3$, then for $\gamma =3$,

A biased random walk on the integer line takes unit steps right and left with unequal probabilities $p$ and $1-p$, using independent and identically distributed random variables as increments. For $S_0=0$, the expected value is $n(2p-1)$: positive for $p>1/2$ and negative for $p<1/2$. Changing $p$ to $1-p$ reverses the preferred direction. The exact endpoint probability mass function is given under simple random walk on the integer line.

For example, with $p=0.65$ each step has expected value $0.30$ and variance $0.91$. After $80$ steps,The displacement of the expected value from zero is about $24/8.532\approx 2.813$ standard deviations, yet individual sample paths still move left as well as right, as the figure under simple random walk illustrates. Bias lets this elementary stochastic process model an average directional tendency while retaining fluctuations. Boundary and return questions have their own treatments under gambler's ruin and the specialized children below.

On a finite undirected graph, stationary mass is proportional to vertex degree.