For the out-cluster $C_x$ in directed percolation, define $p_H=\inf \{p:{\mathbb{P}}_p(|C_x|=\infty )>0\}$ and $p_T=\inf \{p:{\mathbb{E}}_p|C_x|=\infty \}$, with value $1$ when the relevant set is empty below $1$. The first is a survival threshold; the second is a percolation susceptibility threshold. Both depend on the root in a general directed graph. Exploring a site percolation cluster and using each fresh vertex coin for its first tested incoming bond gives a coupling of probability distributions with bond percolation such that ${\theta }_s(p)\le p{\theta }_b(p)$ and ${\chi }_s(p)\le p{\chi }_b(p)$. Consequently $p_H^b\le p_H^s$ and $p_T^b\le p_T^s$.

Suppose every vertex of a directed graph has at most $\Delta$ incoming bonds. In bond percolation with parameter $q$, open a site whenever at least one incoming bond is open. Incoming bond sets have different heads and hence are disjoint, so these site indicators are independent random variables. Supplement their probabilities to $r=1-(1-q{)}^{\Delta }$. Every vertex reached by a nontrivial open directed path is now open. Conditioning the root to be open gives ${\theta }_s(r)\ge r{\theta }_b(q)$, since a simple directed path from the root never uses a bond entering the root. Thus $p_H^s\le 1-(1-p_H^b{)}^{\Delta }$.

Take a directed graph with vertices $v_0,v_1,\dots$ and $n+1$ parallel bonds from $v_n$ to $v_{n+1}$. This is a locally finite graph, although the incoming degrees are unbounded. For bond percolation, ${\theta }_b(p)={\prod }_{n\ge 0}(1-(1-p{)}^{n+1})>0$ whenever $p>0$: the failure probabilities are summable, so the infinite product is positive. For site percolation, survival requires every vertex of the ray to be open, which has probability zero for $p<1$. Thus the survival and susceptibility thresholds for directed percolation satisfy $p_H^b=0$ and $p_H^s=1$.

Independently open the bonds of a directed graph with a common probability $p$, and ask whether an infinite open directed path starts at a specified root. On the oriented square lattice, edges run from $(x,n)$ to $(x\pm 1,n+1)$ with $x+n$ even. The critical probability is the infimum of parameters with positive survival probability. The incoming-edge coupling of site and bond percolation compares this threshold with that of oriented site percolation.

Independently open vertices of a directed graph with common probability $p$, and require every vertex of an infinite directed path to be open, including its root. Conditioning the root to be open changes the survival probability by a factor $p$ and does not change the critical probability. This model gives an independent comparison process in the independent oriented-percolation comparison for the contact process.

A spanning circuit family is a collection of directed cycles whose union contains every vertex of the directed graph. Different circuits may overlap. This is distinct from a cycle cover of a directed graph, which requires vertex-disjoint cycles.

If $V=A_1\oplus A_2=A_2\oplus A_3=A_1\oplus A_3$ with nonzero subspaces, choose $0\ne v\in A_3$ and write $v=a_1+a_2$ using the first direct sum of vector spaces. Both components are nonzero. The plane spanned by them meets $A_1$ and $A_2$ in their component lines and meets $A_3$ in the line through $v$. Its intersection with $A_3$ cannot be the whole plane, since $a_1\notin A_3$.

In mostly-minus signature and the Weyl representation, take $p^0=E=\sqrt{|\mathbf{p}{|}^2+m^2}>0$. The Hermitian matrices $A=p\cdot \sigma =E-\mathbf{p}\cdot \mathbf{\sigma }$ and $B=p\cdot \bar{\sigma }=E+\mathbf{p}\cdot \mathbf{\sigma }$ commute and obey $AB=m^{2}I$. Their positive square roots of a matrix therefore obey $\sqrt{A}\sqrt{B}=mI$. It follows that $u=(\sqrt{A}\xi ,\sqrt{B}\xi {)}^T$ solves $(/p-m)u=0$ and $v=(\sqrt{A}\zeta ,-\sqrt{B}\zeta {)}^T$ solves $(/p+m)v=0$. They multiply $e^{-ipx}$ and $e^{ipx}$ respectively. For normalized two-spinors, $u^{†}u=v^{†}v=2E$. The formula extends by positive-semidefinite square roots to the massless limit.

Boost a massive rest Dirac spinor labelled by $\xi =(1,1{)}^T/\sqrt{2}$ along the third axis and take its massless limit. In the Weyl representation the result is the displayed equal superposition of helicities, not an eigenvector of the fixed transverse rotation generator ${\Sigma }_1/2$. For momentum $(E,0,0,E)$ the positive-energy Hamiltonian is $E{\gamma }^0{\gamma }^3$, which anticommutes with ${\Sigma }_1=\mathrm{diag}({\sigma }_1,{\sigma }_1)$. A nonzero positive-energy solution cannot also be a ${\Sigma }_1$ eigenvector. Thus a transported canonical spin label must be distinguished from literal transverse spin at zero mass; longitudinal helicity remains a good quantum number.

For $g=\mathrm{diag}(1,-1,-1,-1)$ and the block-diagonal standard representation of the gamma matrices, write a positive-energy Dirac spinor as $(\xi ,\eta {)}^T$. The Dirac equation gives $\eta =(\mathbf{\sigma }\cdot \mathbf{p})\xi /(E+m)$. Choosing two orthonormal two-spinors ${\chi }_s$ yields the displayed positive-frequency solutions. Negative-frequency solutions are $v_s=\sqrt{E+m}((\mathbf{\sigma }\cdot \mathbf{p}){\eta }_s/(E+m),{\eta }_s{)}^T$ multiplying $e^{ip\cdot x}$. They obey $(/p+m)v_s=0$. Both have norm $2E$; their Dirac adjoints give ${\overline{u}}_r{u}_s=2m{\delta }_{rs}$ and ${\overline{v}}_r{v}_s=-2m{\delta }_{rs}$. Spatial rotations act on each two-spinor with generators ${\sigma }_j/2$, explaining spin one-half.

For action $-\int \bar{\psi }(\gamma \cdot \partial +m)\psi$, choose gamma matrices with $\{{\gamma }^{\mu },{\gamma }^{\nu }\}=2{\eta }^{\mu \nu }$ and signature $(-,+,\dots ,+)$. The Fourier kernel of $D=\gamma \cdot \partial +m$ is $i\gamma \cdot p+m$. Its algebraic inverse is $(-i\gamma \cdot p+m)/(p^2+m^2)$. The Grassmann Gaussian integral for $e^{-i\bar{\psi }D\psi }$ has covariance $-i{D}_F^{-1}$, giving the displayed Dirac propagator. Its denominator has positive-energy poles below and negative-energy poles above the real energy axis by the Feynman i-epsilon prescription. Both the metric and the factor of $i$ in the derivative kernel must be specified when comparing propagator formulas.

With $\{{\gamma }^{\mu },{\gamma }^{\nu }\}=2g^{\mu \nu }$ and spin curvature $[{\nabla }_{\mu },{\nabla }_{\nu }]=R_{\mu \nu ab}{\gamma }^{ab}/4$, the Dirac equation operator squares to the rough Laplacian minus $R/4$. In the curvature contraction the four-gamma term vanishes by the first Bianchi identity, the two-gamma terms vanish by Ricci symmetry, and the scalar term is $-2R$. In Riemannian signature $i/\nabla$ is the self-adjoint convention and its square is $-{\nabla }^2+R/4$.

The gamma matrix Clifford algebra and commuting partial derivatives give $(/\partial {)}^2=\square$. Thus every free Dirac equation solution also solves the Klein-Gordon equation componentwise. The converse is false without the first-order spinor constraint: the second-order equation alone allows twice as much independent initial data.

For a commuting spinor in mostly-plus signature, orient its Dirac current to the future and write $V^i={ϵ}^{†}{\alpha }^iϵ$, with Hermitian ${\alpha }^i=-{\gamma }^0{\gamma }^i$. For every spatial unit vector $u$, $(u\cdot \alpha {)}^2=1$ implies $|u\cdot V|\le {ϵ}^{†}ϵ=V^0$. Choosing $u$ along the spatial current proves $V^2\le 0$. Thus spinor bilinears supply causal vectors suitable for energy-condition estimates. A Grassmann-valued spinor does not have this ordinary positivity interpretation.

Subtract the constant term of a modular form times a same-weight monomial in $E_4,E_6$. The remainder is a cusp form, hence divisible by the modular discriminant. Induction on weight and $1728\Delta =E_4^3-E_6^2$ express every form as a weighted homogeneous polynomial in these two Eisenstein series. The number of monomials of a given weight equals the dimension of level-one modular forms, so they form a basis and the graded ring has no polynomial relation.

For a constant ambient buoyancy frequency, entrained liquid lags the decreasing ambient density by ${\rho }_0{N}^2[(a_0+\alpha z{)}^4-a_0^4]/[4\alpha g(a_0+\alpha z{)}^3]$. Balancing this negative buoyancy with expanding-bubble buoyancy gives the displayed fifth-degree equation. The first physically admissible root is the neutral height in the quasi-steady rise-speed model. In a very deep release, bubble expansion is negligible and $z_n=[(a_0^4+4\alpha g{\varphi }_0{a}_0^3/N^2{)}^{1/4}-a_0]/\alpha$. Actual momentum can cause overshoot, while bubble slip becomes important as the liquid stops rising.

Consider a two-dimensional dike of full opening width $b(z,t)$ carrying magma upward at mean velocity $w(z,t)$. Let $Q_0>0$ be the imposed magma volumetric flow rate per unit strike length. Assume equal solid/liquid mass densities $\rho$, constant specific heat capacity $c_p$, latent heat $L$, melting temperature $T_m$, and fixed inlet temperature $T_{\mathrm{in}}>T_m$.

If opening is entirely supplied by wall melting, volume conservation gives $bw=Q_0$. Prescribe the heat transfer to each wall as ${\alpha }_T\rho c_{p}w(T-T_m)$ with constant dimensionless ${\alpha }_T>0$. The two walls and the warming of newly melted material then giveThe second equation is the melt-dilution heat balance in a dike; discarding its sensible-heat dilution term is an additional approximation.

Set $\Delta T=T_{\mathrm{in}}-T_m$, $S=L/(c_p\Delta T)$ and $\vartheta =(T-T_m)/\Delta T$. The similarity solution ansatzreduces the two equations toThus both opening and vertical similarity length grow as $\sqrt{t}$. The fixed-temperature inlet requires $\vartheta (0)=1$ and, on the positive-width branch, $f(0)=1$. The ratio $S$ and these boundary data must accompany the shape equations. A finite initial aperture or unequal densities can change the early-time model; the zero-width start is an idealized similarity limit.

A shortest-path predecessor $\pi (v)$ records the previous vertex on a chosen route from source $s$ to $v$. When edge relaxation strictly improves $d(v)$ through $u\to v$, set $\pi (v)=u$. For a multigraph, store the actual directed edge, since two edges with the same endpoints can have different weights. Initialize all vertices, including the source, without a shortest-path predecessor. The authored implementation reconstructs a route by following these incoming edges backwards, then reversing their order.^[ref]

At the end of the edge relaxation trace, $\pi (t)=a$, $\pi (a)=b$, and $\pi (b)=s$. Following them gives $t,a,b,s$, so the forward directed path is $s\to b\to a\to t$, of cost $2-1+2=3$. This makes the distance useful: a number tells us the best cost, whereas the shortest-path predecessors tell us which connections to take. In the Dijkstra algorithm, reconstruct a destination's shortest route after its label becomes permanent. In a label-correcting shortest-path algorithm, wait for convergence and the appropriate negative-cost directed cycle check. During correction, a predecessor's label may have changed since its child's last update: immediately after $b\to a$ in the trace, the current chain to $t$ costs $3$ although $d(t)$ still records the earlier discovered route of cost $8$.

Strict improvement is a useful guard against directed cycles in the recorded pointers. Consider $s\to a$ and $s\to b$ of cost zero, followed by $a\to b$ and $b\to a$, also of cost zero. After the first two edge relaxations, both labels are zero and both shortest-path predecessors are $s$. If equality also allows pointer replacement, the last two operations can set $\pi (b)=a$ and $\pi (a)=b$: following pointers loops forever despite correct distance labels. Keeping the old pointer on ties prevents this example; choosing among equal-cost routes requires a separate rule that preserves a route to the source. Strict improvement alone does not protect against reachable negative-cost directed cycles, which can force endless decreases. Equality on recorded edges plus the Bellman inequalities certifies route costs only when the pointer chains actually terminate at the source.

For all-to-one distances to $r$, record a next vertex $j$ whenever relaxing $i\to j$ improves $d_i$. Follow these successors forward in the original directed graph to reach $r$. They are shortest-path predecessors when viewed in the reversed directed graph; keeping the direction convention explicit prevents returning a route whose arrows point the wrong way.

Edge relaxation tests whether extending a recorded route by one directed edge improves a tentative distance. For a single source $s$ and an original directed edge $u\to v$ of weight $w_{uv}$, form the candidate $q=d(u)+w_{uv}$. If $d(u)$ is finite and $q<d(v)$, replace $d(v)$ by $q$ and record the shortest-path predecessor $\pi (v)=u$. Otherwise leave both unchanged. The minimum formula in the header gives the resulting label in either case. The authored implementation uses this strict comparison and records the incoming directed edge.^[ref]

The table lists labels after each specified operation, keeping $d(s)=0$ throughout this constructed acyclic graph. Relaxing $a\to t$ first offers $6+2=8$. Relaxing $b\to a$ then offers $2-1=1$, lowering $a$ from $6$ to $1$; revisiting $a\to t$ now offers $1+2=3$. Finally $b\to t$ offers $2+7=9$, so the label $3$ stays. Changing a label can make a previously checked outgoing directed edge worth checking again. This trace illustrates the operation itself, with a deliberately specified order, rather than a run of the Dijkstra algorithm.

A single edge relaxation does not require nonnegative weights: its candidate is the cost of a real extended directed walk, even when the last directed edge has negative weight. Nonnegative weights are needed by the Dijkstra algorithm to make its selected minimum tentative distance permanent. The Bellman-Ford algorithm permits negative weights and revisits labels; its authored implementation also checks for reachable negative-cost directed cycles.^[ref][ref]

For the all-to-one convention of the Bellman inequalities, relax the same original directed edge $i\to j$ by $d_i\leftarrow \min \{d_i,c_{ij}+d_j\}$. This prepends $i\to j$ to a discovered route from $j$ to $r$, and improves the tail label $d_i$, not the head label $d_j$. Skip the operation when $d_j=\infty$. In exact arithmetic, a full pass over all relevant directed edges with no improvement establishes all their Bellman inequalities; combined with the discovered-route upper bounds, this certifies finite distances. One unsuccessful operation establishes only that this particular candidate did not improve its label at that moment.

A tentative distance $d(v)$ is the cheapest cost so far recorded by a shortest path problem algorithm. For the single-source convention, initialize $d(s)=0$ and $d(v)=\infty$ elsewhere. Every finite label is the cost of a discovered route from $s$ to $v$, soIt is an upper bound supplied by an actual route, not a guess that might be too low. The loop invariant follows directly from edge relaxation: extending a discovered route to $u$ by $u\to v$ supplies another discovered route. The usual label initialization and this safety property are stated and proved in the authored MIT notes.^[ref]

In the trace under edge relaxation, $d(t)=10$ first records the direct directed path $s\to t$. Later $d(t)=8$ records $s\to a\to t$, and finally $d(t)=3$ records $s\to b\to a\to t$. Each label is a usable route cost, but the first two are not shortest. An infinite tentative distance means no route has yet been discovered; only a completed search establishes unreachability. The isolated vertex $z$ in that example remains at $\infty$.

In a directed graph, the discovered route is formally a directed walk, which may repeat vertices; in the pictured acyclic graph it is always a directed path. Negative-cost directed cycles can keep supplying cheaper directed walks without a finite optimum. In the all-to-one convention, the same meaning applies to discovered routes from $i$ to $r$. Do not confuse either version with the lower bounds supplied by feasible potentials in the Bellman inequalities: one records something achievable, the other rules out anything cheaper.