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Every finitely generated torsion-free module over a principal ideal domain is free. In the structure theorem, torsion-freeness removes every cyclic torsion summand and leaves only the free summand.

The Bessel potential of order $s$ is the Fourier multiplier $(1+|\xi {|}^2{)}^{-s/2}$. Sufficiently high order turns a compactly supported finite-order distribution into a bounded continuous function.

Let $a={\mathbb{P}}_i(T_j<T_i)$ and $b={\mathbb{P}}_j(T_i<T_j)$. Before returning to $i$, the chain visits $j$ zero times with probability $1-a$ and, for $k\ge 1$, exactly $k$ times with probabilityWhen $b>0$, the expected number of visits is $a/b$.

If $H>0$ at one time, the inequality $d(H^{-1})/dt\ge 1$ forces $H\to +\infty$ and $a\to 0$ within finite time to the past. If $H<0$, it forces $H\to -\infty$ and $a\to 0$ within finite time to the future.

A machine computes the characteristic function of a language $X$ when it halts on every input with the one-letter output $1$ for inputs in $X$ and $0$ for inputs outside $X$.

The induced worldsheet metric is the pullback of the target-space metric:

A strictly convex function has at most one minimizer: if distinct points attained the same minimum, every strict convex combination of them would have a smaller value.

On the positive orthant,is strictly concave. Its unique maximizer $x^{\ast }$ on a compact convex feasible set satisfies the first-order inequalityequivalently

A stratum is a subgroup assigned its own nuisance baseline.

For bounded stopping times $\sigma \le \tau$, a supermartingale satisfies $\mathbb{E}[V_{\tau }\mid {\mathcal{F}}_{\sigma }]\le V_{\sigma }$; equality holds for a martingale.

For a discrete martingale and stopping time $T$,The indicator is measurable at time $n$, so the stopped process is a martingale. If it is uniformly bounded and $T<\infty$ almost surely, bounded convergence gives $\mathbb{E}{M}_T=\mathbb{E}{M}_0$ directly.

A stochastic differential equation specifies infinitesimal drift and random diffusion through a stochastic integral equation.

The stochastic integral $\int HdX$ integrates a predictable process against a semimartingale. It extends pathwise integration against finite-variation processes and the Itô integral against local martingales.

The quadratic variation of a continuous semimartingale is the limit in probabilityFinite-variation processes have zero quadratic variation, while a Brownian motion satisfies $[B{]}_t=t$.

A semimartingale is the sum of a local martingale and an adapted finite-variation process. This is the broad class of integrators for which the Itô stochastic integral is defined.

Two-body relaxation is the cumulative randomization of stellar velocities by many weak gravitational encounters. Its timescale in a system of $N$ comparable stars is of order $N/\log N$ crossing times.

A body is in radiative equilibrium when absorbed radiative power equals emitted radiative power. A surface absorbing flux $F$ and emitting as a grey body has equilibrium temperature $T=(F/(ϵ\sigma ){)}^{1/4}$.

Emissivity compares thermal emission from a surface with emission from an ideal blackbody at the same absolute temperature, using the same wavelength and angular averaging convention. For ordinary passive surfaces in the far field at local thermal equilibrium, the ratio lies between zero and one. A blackbody has emissivity one. Unless a spectral or directional qualification is given, the $ϵ$ in $M=ϵ\sigma T^4$ is total hemispherical emissivity: a ratio of emitted radiant exitances, not of all outgoing radiant flux including reflected radiation.^[ref]

Emissivity belongs to a specified surface state, not just a bulk material name. Coating, oxidation and surface finish can change it; wavelength, viewing direction and temperature also matter. A surface that looks white in visible light need not have low infrared emissivity. This distinction is useful when choosing a radiator coating or interpreting a radiation-based temperature measurement: a narrow-band detector needs the appropriate spectral emissivity, and its signal can include reflected surrounding radiation. The grey body and diffuse thermal emitter assumptions are separate simplifications, addressing wavelength and direction respectively.

Writing $A=D+L+U$, where $D$ is diagonal and $L,U$ are strictly triangular, the Jacobi iteration is

The Jacobi method solves a linear system by updating every coordinate from the same old vector. It requires every diagonal entry $a_{ii}$ to be nonzero. Isolating coordinate $i$ in row $i$ gives the simultaneous ruleHere $B$ is the iteration matrix. A sweep computes a complete new vector before replacing the old one, so its coordinate updates can run independently.^[ref]

For a sparse matrix, a sweep needs work proportional to the stored nonzero entries, while Gaussian elimination may create fill-in. The Jacobi method is therefore a simple starting point for large linear systems, and the weighted Jacobi method supplies a useful building block of a multigrid method. Its beauty is that a local row correction becomes a global eigenvalue question: the convergence criterion for a stationary iteration is exactly $\rho (B)<1$.

A splitting $A=M-N$ with invertible $M$ produces

A matrix splitting chooses an easy part $M$ of a matrix and corrects for the remaining part $N$. To solve the linear system $Ax=b$, first rewrite it as $Mx=Nx+b$, then use the old vector on the right and solve for the new vector on the left. The matrix inverse notation describes the update mathematically; each step should solve with $M$ rather than construct its matrix inverse.^[ref]

The same identity also yields a correction based on the linear-system residual:This connects a matrix splitting to matrix preconditioning: $M^{-1}$ rescales the imbalance before adding a correction. Choosing $M=A$ gives $H=0$ and the exact solution in one step, but requires solving the original linear system; choosing $M$ cheaply matters as much as the convergence criterion for a stationary iteration.

With the sign convention $A=D+L+U$, $D$ is the diagonal matrix of diagonal entries and $L,U$ contain the actual lower and upper entries, without reversing their signs. The Jacobi method uses $M=D$, $N=-(L+U)$; the Gauss-Seidel method uses $M=D+L$, $N=-U$. The first requires only division by diagonal entries; the second uses a lower triangular matrix and forward substitution. These choices explain their different information flows.