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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 average reproduces constants but need not reproduce a linear regression function near an endpoint. For $x_i=i/n$, $K=1_{[-1,1]}$, $m(x)=x$, and $nh\to \infty$, its deterministic estimate at zero is $(\lfloor nh\rfloor +1)/(2n)\sim h/2$. A local linear fit removes this first-order boundary bias of an estimator.

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.

A left monoid action assigns each $m\in M$ a map $a\mapsto m\cdot a$ with $1\cdot a=a$ and $(mn)\cdot a=m\cdot (n\cdot a)$. It need not be invertible or injective. A left M-set is equivalently a covariant functor from the one-object category determined by $M$.

The distinct radicals in a minimal primary decomposition are exactly the associated primes of a module $R/I$, and hence are invariant. The injection into the sum of primary quotients proves one inclusion; an element supported on one component, supplied by irredundancy, proves the other. This does not imply uniqueness of embedded primary components.

For a positive-definite Hermitian operator $A={\sum }_j{t}_j|u_j⟩⟨u_j|$ and a unit vector $v$, the weights $|⟨v|u_j⟩{|}^2$ form a probability distribution. Scalar logarithmic concavity bounds their average of $\log t_j$ by the logarithm of their average of $t_j$. Positive semidefinite operators follow by a limiting convention. Using the eigenbasis of a density operator $\rho$ gives $S(\rho \Vert \sigma )\ge D(r\Vert q)$, where $r$ is the spectrum of $\rho$ and $q$ the diagonal of $\sigma$ in that basis. This yields nonnegativity of quantum relative entropy without commutativity.

On complex symmetric matrices with positive-definite real part, the determinant has a nonzero analytic square root normalized positively on real positive matrices. It is continued within this accretive domain. For $A=G+iB$, put $C=G^{-1/2}B{G}^{-1/2}$ with real eigenvalues $b_j$; then $d(A)=\sqrt{\det G}{\prod }_j\sqrt{1+i{b}_j}$, using positive-real-part roots for each factor. The principal scalar square root of the product determinant need not equal this continued root: accumulated determinant arguments can cross the scalar branch cut. The principal matrix logarithm instead tracks all factors consistently.

A martingale problem is well-posed for a specified test domain and class of paths if it admits a solution for every specified starting point and that solution is unique in law. Existence on one fixed probability space is not required. Test-domain and true-versus-local conventions must be stated. For bounded diffusion coefficients and $C_b^2$ test functions, boundedness on every finite horizon upgrades the local identities to true martingale identities.

When the knot polynomial is normalized as ${\psi }_i(x)={\prod }_{\ell =1}^{k-1}(x-t_{i+\ell })/(k-1)!$, the displayed formula has no additional factorial prefactor. Applied to Marsden's identity, it extracts the coefficient of the corresponding B-spline in any polynomial of degree at most $k-1$. Differentiating the finite sum makes adjacent terms cancel, proving independence of $x$. For linear polynomials the coefficient is their value at the Greville abscissa.

For a diffusion with generator $({\sigma }^2/2){\partial }_{xx}+b{\partial }_x$ and increasing scale function of a one-dimensional diffusion $s$, its speed density is $m=2/({\sigma }^2{s}^{\prime })$. Scale records hitting probabilities; speed records expected occupation and exit times. Multiplying scale by a positive constant divides speed by the same constant, leaving their Green-kernel product unchanged.

A positive normalized continuous local martingale deflator requires a locally square-integrable Brownian diffusion coefficient. If the forced market price of risk is $-t^{-1/2}$, the coefficient has magnitude $Z_t/\sqrt{t}$. Since $Z_0>0$, continuity makes its squared integral diverge near zero. The bank account can nevertheless exist because $t^{-1/2}$ itself is integrable; finite integrated interest is weaker than square-integrability of the required risk compensation.

If a compact H-hull lies in a disc of radius $r$ centred at $\xi \in \mathbb{R}$, the displayed estimate holds for $|z-\xi |>2r$, with an absolute constant $C$. It refines the Laurent expansion uniformly in the hull and supplies the first-order increment needed to derive the Chordal Loewner equation from the Loewner local growth property.

For steady axisymmetric ideal flow with only poloidal components, induction implies $\mathbf{u}\times \mathbf{B}=C{\mathbf{e}}_{\varphi }/R$. Axis regularity, or zero toroidal electromotive force, sets $C=0$. Continuity then gives $\rho \mathbf{u}=k(\psi )\mathbf{B}$, locally on connected magnetic surfaces of the poloidal magnetic flux function. Without the zero-circulation condition, annular domains can support steady cross-field flow.

In aligned steady ideal flow with $p=c_s^2\rho$ and constant isothermal sound speed, the Lorentz force density has no field-parallel component. Projecting momentum along the magnetic field makes $u^2/2+\Phi +c_s^2\ln (\rho /{\rho }_0)$ constant on each connected magnetic surface. The arbitrary reference density changes only the additive constant.

For a cold corotating field line anchored in a circular point-mass orbit, the effective potential has meridional Hessian ${\omega }^2\mathrm{diag}(-3,1)$. Along a straight line inclined by $\alpha$ to the vertical its quadratic curvature is ${\omega }^2(1-4{\sin }^2\alpha )$. Negative curvature gives the strict local launching criterion. Equality is marginal and global escape requires additional information.

For a logarithmically homogeneous barrier on a proper cone, the convex conjugate with reversed argument defines a barrier on the interior dual cone. The inverse gradient relation makes $y=-\mu \nabla F(s)$ equivalent to $s=-\mu \nabla F_{†}(y)$. This gives a joint primal-dual central path without assuming the barrier is identical on a self-dual cone.

The probability chain rule makes a sum of sequential predictive log scores equal the log Bayesian model evidence when the forecasts are coherent Bayesian one-step predictions under proper priors. Differences of total scores are therefore log Bayes factors.