Let $C=A^{\ast }{B}_{\alpha }$ with one global dual ball. Its support function is $\alpha \Vert Au{\Vert }_2$, and the proximal operator of a support function gives $u=g-P_{C}g$. The removed component is the convex projection, while the reconstructed image signal is its residual. Projected gradient descent on $\frac{1}{2}\Vert g-A^{\ast }p{\Vert }_2^2$, $p\in B_{\alpha }$, converges for $0<\tau <2/\Vert A{\Vert }^2$.

For the Schmidt-basis Pauli correlation tensor, choose Alice's axes ${\mathbf{e}}_z,{\mathbf{e}}_x$ and Bob's $({\mathbf{e}}_z\pm s{\mathbf{e}}_x)/\sqrt{1+s^2}$. The CHSH inequality expression is $2\sqrt{1+s^2}$. It exceeds two exactly when both Schmidt coefficients are nonzero, reaching $2\sqrt{2}$ for maximal quantum entanglement. A product state gives $s=0$ and saturates rather than violates the inequality.

For a tuned multicritical even Landau potential with leading positive $M^{2n}$ term, $M^2\sim |t{|}^{1/(n-1)}$ and $\xi \sim |t{|}^{-1/2}$. Long-wavelength Gaussian fluctuations in a correlation volume have variance proportional to ${\xi }^{2-D}$. Their ratio to $M^2$ therefore scales as $|t{|}^{(D-2)/2-1/(n-1)}$, vanishing only for $D>2n/(n-1)$. At equality the criterion is marginal, requiring further fluctuation analysis.

At an upper critical dimension, the leading Ginzburg criterion ratio has zero thermal power rather than a negative one. It does not vanish asymptotically, but its magnitude can depend on the coupling. The criterion alone does not establish a power-law divergence or new critical power exponents. Running marginal interactions generally produce logarithmic corrections; the ordinary quartic and tricritical sextic scalar theories are examples.

For a scalar quartic interaction $g{\varphi }^4/4!$, the tadpole relation $R=r_{\Lambda }+(g/2)I_D(R)$ can be subtracted at the critical point. When $I_D(0)$ exists, $A(T-T_C)=R[1+(g/2)J_D(R)]$, with $J_D(R)=\int d^{D}p(2\pi {)}^{-D}/[p^2(p^2+R)]$. This integral is finite as $R\to 0$ only above four dimensions and is logarithmic at four. A self-consistent one-loop relation is not an exact interacting critical-exponent formula.

For every singular infinite cardinal number $\kappa$, the Gimel function has the smallest value permitted by König theorem for cardinal numbers and the exponent: ${\kappa }^{\mathrm{cf}(\kappa )}=\max \{{\kappa }^{+},2^{\mathrm{cf}(\kappa )}\}$. Where $2^{\mathrm{cf}(\kappa )}<\kappa$, this is the singular cardinals hypothesis. The hypothesis imposes no separate successor-power condition on regular cardinals.

The Gimel function determines all infinite cardinal powers. For a regular base, $2^{\kappa }=\gimel (\kappa )$. For a singular base, put $s={\sup }_{\rho <\kappa }{2}^{\rho }$ and $\theta =\mathrm{cf}(\kappa )$; then $2^{\kappa }=s^{\theta }$, which is $s$ if attained below $\kappa$ and $\gimel (s)$ otherwise. After these powers are known, fix an infinite exponent $\lambda$ and recurse on the base. Below $\lambda$ use $2^{\lambda }$; at successors use the Hausdorff formula for cardinal exponentiation. At a limit base greater than $\lambda$, put $a={\sup }_{\rho <\kappa }{\rho }^{\lambda }$. The answer is $a$ if $\mathrm{cf}(\kappa )>\lambda$ or the supremum is attained, and $\gimel (a)$ otherwise. In the nonattained cases, the cofinal-index argument identifies the cofinality of the supremum.

A blocked Gibbs sampler draws groups of coordinates jointly from their full conditional distributions. Each update preserves the joint probability distribution; dependence between blocks can still cause a long mixing time.

Every full-conditional coordinate update satisfies detailed balance with the target distribution. Their systematic ordered composition preserves the target but need not satisfy detailed balance, since compositions of reversible kernels need not be reversible. A fixed-probability random-scan mixture remains reversible.

For a strictly positive posterior on finitely many binary spins, a Random-scan Gibbs sampler uses the two local posterior weights to redraw one spin. Positive full conditional distribution probabilities allow every finite sequence of spin changes, giving an irreducible Markov chain with positive self-transition probabilities.

For Gaussian precision $\left(\begin{array}{cc}A& C\\ C& D\end{array}\right)$, a systematic update of the first coordinate followed by the second makes the centered second coordinate an AR(1) chain with coefficient $C^2/(AD)<1$. Independent conditional Gaussian noise supplies its stable innovation. This proves convergence of the Gaussian Gibbs sampler and explains slower mixing when posterior correlation has magnitude near one.

For two agents and a strategyproof social choice function, if an alternative is selected when one agent ranks it first and the other ranks it last, it is selected whenever the first agent ranks it first, whatever the other agent reports. Changing the first agent's order preserves its attainable top choice; changing the second agent's order cannot offer it an improvement over a selected bottom alternative.

For a random walk with independent and identically distributed increments and an exit time from $(0,r)$, suppose $p=\mathbb{P}(X_1>r/m)>0$ for some integer $m\ge 1$. A block of $m$ such positive increments forces an exit. Conditional on survival, every successive block has success probability at least $p^m$, so $\mathbb{P}(T>km)\le (1-p^m{)}^k$ and $\mathbb{E}T\le m/p^m$. This is an instance of the geometric tail bound from a uniform escape probability; centering of the increments is not required.

For the geometric-jump countdown Markov chain, regeneration at zero givesA cycle visits positive state $j$ precisely when its jump reaches at least $j$, with probability $q^{j-1}$. Dividing this expected occupation by the mean cycle length proves the formula. Mean recurrence times are $1/{\pi }_j$, and the limiting probability away from zero is $1/(1+p)$.

For an aperiodic positive Harris recurrent Markov chain with geometric ergodicity, an observable with stationary moment $\pi (|h{|}^{2+\delta })<\infty$ for some $\delta >0$ satisfies a central limit theorem. Its asymptotic variance is ${\gamma }_h(0)+2{\sum }_{k\ge 1}{\gamma }_h(k)$, with stationary covariances. Ergodicity alone does not guarantee this theorem.

The orbital variation of a planet's reflected stellar light as the visible illuminated area changes. In a simple spherical model, $F_p/F_{\ast }=A_g(R_p/a{)}^2\Phi (\alpha )$, where $\Phi (0)=1$ and $\alpha$ is the star-planet-observer angle. Exoplanet clouds can alter the geometric albedo and the angular scattering law; this observable differs from an exoplanet thermal phase curve.

The right lifting property for each sphere-to-disk map fills a prescribed cycle and a compatible target element. The degree-zero generator supplies initial surjectivity; all other fillers force an acyclic kernel and the remaining degreewise surjections.

A Gaussian linear mixed model combines fixed effects, random effects with a normal distribution, and independent Gaussian noise. For $b\sim N(0,D)$ and $\epsilon \sim N(0,R)$ independently, its marginal distribution is $Y\sim N(X\beta ,V)$ with $V=ZD{Z}^T+R$. The random effects induce dependence among observations sharing their design columns. A random-intercept linear mixed model and a random-slope linear mixed model are special cases.

The backfitting algorithm estimates each additive mean function by smoothing its partial residual, obtained after subtracting the current fits of all other terms, and cycling through terms until convergence. Centering each smooth separates its constant part from the intercept. For fixed quadratic smoothness penalties it is block coordinate minimization of a penalized least-squares objective; identifiability and positive definiteness of the constrained problem ensure a unique converged fit. Non-Gaussian generalized additive models can use weighted backfitting inside iteratively reweighted least squares.

For a nonzero finitely generated Weyl algebra module, choose a finite-dimensional generating subspace $M_0$. Under the Bernstein filtration, $\dim (F_j{A}_n{M}_0)$ is eventually polynomial because the graded module is finite over a polynomial ring. Its degree is independent of the generating subspace, since two generating filtrations bound each other after fixed shifts. It equals the Gelfand–Kirillov dimension of a module.