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For the three unit-grid edge vectors $v_1,v_2,v_3$ and a multivariate polynomial $f$ of total degree at most three, sampled regular-grid Loop subdivision generates the graph of $f+\frac{1}{12}{\sum }_{r=1}^3(v_r\cdot \nabla {)}^{2}f$. Thus affine polynomials are reproduced exactly and all polynomials through degree three retain their degree. Fourth-degree data constant along one edge direction reduce to a Cardinal cubic B-spline, whose limit need not be a polynomial.

Such a continuous projective unitary representation admits strongly continuous rotations whose self-adjoint generator is bounded below and whose adjoint action rotates the loops. In the basic fermionic construction, integer one-particle modes are filled below zero. The rotation energy counts nonnegative particle energies and positive hole energies. It has finite-dimensional integer eigenspaces and implements circle rotations. Tensor products give higher nonnegative integer levels.

Assume a nonzero invariant Loitsyansky integral, self-preserving large-scale correlations, and high-Reynolds number freely decaying isotropic turbulence. Then $I\propto u^2{\ell }^5$ and $-d(u^2)/dt\propto u^3/\ell$ imply the displayed decay and growth laws. The assumptions on correlation shape and the dissipation coefficient are needed in addition to conservation of the integral.

For a confined incompressible flow with no velocity flux across the boundary, an integrated divergence identity gives $⟨|\int \mathbf{x}\times \mathbf{u}dV{|}^2⟩=-\iint r^2⟨\mathbf{u}\cdot {\mathbf{u}}^{\prime }⟩dVd{V}^{\prime }$. In a large approximately homogeneous region with short correlations, the latter is approximately $VI$. Conservation of angular momentum in an isolated torque-free cloud then motivates conservation of the Loitsyansky integral. A fixed subvolume of infinite turbulence is not an isolated cloud: advective transport, pressure torques, wall stresses and long-range correlations can invalidate the argument.

For nonzero design entries the Bernoulli logistic-regression model has strictly concave log likelihood, since $-{\ell }^{\prime \prime }={\sum }_i{x}_i^2{\mu }_i(1-{\mu }_i)>0$. A unique finite estimate exists exactly when its score is positive at minus infinity and negative at plus infinity. Perfect agreement of binary outcomes with the signs of the design can instead give a supremum approached at an infinite coefficient. If every design entry is zero, the coefficient is unidentifiable. Newton method uses the score divided by this observed information, with damping when needed.

Complete separation means that a predictor direction $v$ strictly separates the binary outcomes as displayed. In logistic regression, replacing $\beta$ by $\beta +tv$ increases each observed-outcome probability as $t$ increases, so the log-likelihood has no finite maximizer. Weak inequalities with some equalities can similarly produce quasi-complete separation. Finite coefficient estimates and ordinary information-matrix inference therefore require an existence check rather than relying only on the formal score equations.

For ordered response categories, ordered thresholds ${\theta }_l$ and a common slope vector define this cumulative logistic regression. Increasing $x^T\beta$ shifts probability toward higher categories. A covariate contrast multiplies all corresponding cumulative odds by the same factor. This model is not a canonical baseline-category multinomial logistic regression, so the latter's design-weighted count margins are not generally sufficient for it.

The Forward Euler method applied to the logistic differential equation $y^{\prime }=ry(1-ay)$ becomes the logistic map after $u=a(\lambda -1)y/\lambda$, $\lambda =1+r\Delta t$. The positive equilibrium point of the differential equation has perturbations decaying as $e^{-rt}$. Its discrete fixed-point multiplier is $1-r\Delta t=2-\lambda$: for $r\Delta t>2$, the discretization has an artificial oscillatory instability, leading initially to alternation on successive steps. This contrasts with the positive perturbation multiplier $e^{-r\Delta t}$ of the exact time-step map.

Suppose $|a_n|\le (\log (2n){)}^k$ and $A(x)={\sum }_{n\le x}{a}_n\log (x/n)=O(x(\log x{)}^{\beta })$, with $0\le \beta \le k$. For $0<h\le 1$, direct subtraction givesThe last term has absolute value $O((x{h}^2+h)(\log x{)}^k)$. Choose $h=(\log x{)}^{(\beta -k)/2}$ to obtain the displayed bound, with an additional $O((\log x{)}^k)$ term harmless as $x\to \infty$ for fixed $k$. This elementary finite-difference argument converts a logarithmically smoothed Perron formula bound to cancellation in its original partial sum.

For a stock with observed dynamics $d{S}_t/S_t=\sigma d{\hat{W}}_t+\sigma m_{t}dt$ and constant riskless interest rate $r$, the finite-horizon pricing density for Gaussian drift learning with $k=r/\sigma$ gives ${\zeta }_t=e^{-rt}{D}_t$. The terminal logarithmic utility condition $1/w_T^{\ast }=\lambda {\zeta }_T$ and the state-price budget constraint give $\lambda =1/w_0$. Replication then gives $w_t^{\ast }=w_0/{\zeta }_t$ at every time. The Itô formula for $1/\zeta$ makes the optimal dollar stock fraction $(m_t-r/\sigma )/\sigma$. Its finite expected log return follows from finite posterior second moments on finite horizons.

Let a portfolio hold $x\in [0,T]$ in a risk-free asset with net return $r$ and $T-x$ in an asset returning $g$ or $b$, with probabilities $p$ and $1-p$. Assume $T>0$, $-1<b<r<g$, $0<p<1$, and $pg+(1-p)b>r$. Its state wealths are $w_g=T(1+g)-(g-r)x$ and $w_b=T(1+b)+(r-b)x$. The expected utility $p\log w_g+(1-p)\log w_b$ has strictly negative second derivative. Setting its first derivative to zero gives the displayed unconstrained root; the derivative at $x=T$ is negative, so only the lower constraint can bind. Thus the displayed formula is the unique optimum. All equity is optimal exactly when $p\ge (r-b)(1+g)/[(g-b)(1+r)]$. The positivity of both state wealths is part of the logarithmic utility domain.

A fixed positive excess over the Turan theorem threshold forces logarithmic balanced complete multipartite graphs. There is an elementary induction using common neighbourhood from bipartite density: first obtain a logarithmic $r$-partite subgraph from the induction hypothesis, and select disjoint transversal $r$-cliques in it. Each such clique has at least $r\epsilon n$ common neighbours, by the union bound on non-neighbour sets. Apply the bipartite common-neighbour count to these cliques as left-side objects. Their selected union and a large common neighbourhood form the extra part.

In an instantaneous-transition approximation, match the superhorizon adiabatic CDM mode in radiation domination and its first derivative to the radiation-era logarithmic solution at ${\tau }_h=1/k$. Its value is $A_R/k^2$ and its derivative is $2A_R/k$. The two matching equations give the displayed expression. Its coefficient two belongs to this approximation; a smooth radiation forcing through entry changes the constants while preserving the logarithmic CDM growth after radiation-era horizon entry scaling.

A scale whose coordinate is a logarithm of a positive quantity. Equal ratios occupy equal coordinate intervals; for example, $1,2,4,8$ are equally spaced under the natural logarithm because every successive difference is $\ln 2$.

For $0<u<1$, the power series for the logarithm gives $-\log (1-u)-u={\sum }_{n=2}^{\infty }{u}^n/n<\frac{1}{2}{\sum }_{n=2}^{\infty }{u}^n=u^2/[2(1-u)]$. The inequality is strict because $1/n<1/2$ for every $n\ge 3$.

Diagonalize the self-adjoint Jacobi curvature operator at the initial point and transport its orthonormal eigenbasis parallel along the geodesic. The Jacobi field equation reduces to $j_i^{\prime \prime }+{\lambda }_i{j}_i=0$. For zero initial position and initial derivative $b_i$, the solution is $b_i\sin (\sqrt{{\lambda }_i}t)/\sqrt{{\lambda }_i}$ when ${\lambda }_i>0$, $b_{i}t$ when ${\lambda }_i=0$, and $b_i\sinh (\sqrt{-{\lambda }_i}t)/\sqrt{-{\lambda }_i}$ when ${\lambda }_i<0$. Only positive eigenvalues contribute conjugate points, at $t=k\pi /\sqrt{{\lambda }_i}$. At a common zero the multiplicity is the sum of the contributing eigenspace dimensions. The displayed convention uses the curvature operator positive on a positively curved plane; reversing the tensor convention also reverses its tensor formula.

A rank-one subsheaf of a vector bundle on a smooth algebraic curve is saturated when the quotient is torsion-free. Intersecting a one-dimensional subspace of the generic fibre with each stalk constructs a saturated subsheaf. The local ring of a smooth algebraic curve is a discrete valuation ring, so the subsheaf and its torsion-free quotient are locally free. Saturating a nonzero map from a line bundle enlarges that line bundle by an effective divisor, recording the map's zeros.

A free sheaf of finite rank on a scheme is a sheaf of modules globally isomorphic to ${\mathcal{O}}_X^{\oplus r}$. A locally free sheaf only requires such an isomorphism on an open cover; its local frames need not glue to one global frame.

For $1\to N\to G\to Q\to 1$ with $N,Q$ locally finite, take a finitely generated subgroup $L\le G$. Its image in $Q$ is finite, so $L\cap N$ has finite index in $L$ and is finitely generated by Schreier's lemma. Local finiteness of $N$ makes that intersection finite. Thus $L$ itself is finite, proving local finiteness of $G$.

// Target: algebra.bigb

For a time update with one-step defect ${\delta }_{h,k}$ after exact solution values are inserted, the normalized defect divides by the time step $k$. If ${\delta }_{h,k}=O(k^{p+1})$, the normalized local truncation error is $O(k^p)$. A spatial mesh contributes its own powers: for a diffusion update, ${\mathcal{T}}_{h,k}=O(k+h^2)$ means ${\delta }_{h,k}=O(k^2+k{h}^2)$. Under $k=O(h^2)$ these are respectively second and fourth powers of $h$. Neither normalization changes the actual order of a numerical method; it changes the power used to express its residual.