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Insert a smooth exact solution, so $f(y(t))=y^{\prime }(t)$ and $g(y(t))=y^{\prime \prime }(t)$. Expand about $t_n$. The residual, left side minus right side, has the formal differential-operator symbolIts odd symmetry makes every even coefficient vanish. The coefficients of $z,z^3,z^5$ are respectivelyThe next coefficient is $1/4725\ne 0$, givingHence the local truncation error has exact defect order seven and the method has order six. The zero-step characteristic polynomial is ${\zeta }^2-1$, whose two unit roots are simple, so it is also zero-stable. With sufficiently accurate starting values and exact $g=f^{\prime }f$ evaluations, the usual accumulation of defects gives sixth-order global error. This is the symmetric sixth-order two-derivative method; approximate evaluations of $g$ must preserve the required accuracy if that order is to be retained.

Let $A,b$ be the coefficients of the $\nu$-stage Runge-Kutta method. On the Dahlquist test equation $y^{\prime }=\lambda y$, its stability function isBoth polynomials have degree at most $\nu$, with denominator normalized to one at zero. Order $2\nu$ implies $R(z)-e^z=O(z^{2\nu +1})$. A rational function with these degree bounds and this accuracy is uniquely the diagonal Padé approximant $[\nu /\nu {]}_{e^z}$. To see uniqueness directly, if $P/Q$ and $\tilde{P}/\tilde{Q}$ have the required accuracy, then $P\tilde{Q}-\tilde{P}Q$ is a polynomial of degree at most $2\nu$ vanishing to order at least $2\nu +1$ at zero, hence identically zero.

The diagonal exponential Padé approximant can be writtenUse the permitted Padé property that the zeros of $P_{\nu }(-z)$ lie strictly in the right half-plane and numerator and denominator are coprime. Thus $R$ has no poles in the closed left half-plane. Its real coefficients give $P_{\nu }(-iy)=\overline{P_{\nu }(iy)}$, so $|R(iy)|=1$. Also $R(z)\to (-1{)}^{\nu }$ at infinity. Applying the maximum modulus principle on left half-disks and letting their radii increase proves $|R(z)|\le 1$ for $\mathrm{Re}z\le 0$. There are no hidden stage poles there either: the irreducible Padé denominator has degree $\nu$, exhausting the possible degree of $\det (I-zA)$, so the latter cannot contain an additional canceled factor. Every such maximal-order Runge-Kutta method is A-stable. This is maximal-order Runge-Kutta methods are A-stable; it does not assert L-stability, since the stability function does not tend to zero at infinity.

Orient the boundary cyclically as $B_0,B_1,\dots ,B_{m-1},B_m=B_0$, and orient every fan triangle consistently as $(P,B_i,B_{i+1})$. Its vector area isChoose the sign convention in which a triangle with positive normal component along the unit vector $V$ has positive projected area. Then the total signed, or algebraic, projected area isThis is a signed sum, so overlaps and oppositely oriented pieces cancel. Expand the cross product:The sum of $B_i-B_{i+1}$ is zero around the closed polygon. ConsequentlyThis vector area of a polygonal boundary depends only on the oriented boundary, not on $P$, and does not require that the polygon be planar. Translating the boundary adds telescoping terms too, so the formula is independent of the choice of spatial origin. It is the polygonal instance of the vector area boundary integral $\frac{1}{2}\oint B\times dB$.

For $\mathbf{A}\ne 0$, the Cauchy-Schwarz inequality gives $A(V)\le |\mathbf{A}|$ because $|V|=1$. HenceThe opposite direction gives the minimum $-|\mathbf{A}|$; either direction maximizes the absolute algebraic area. Zero algebraic area occurs exactly for unit directions satisfying $V\cdot \mathbf{A}=0$, a great circle of directions perpendicular to the area vector. If $\mathbf{A}=0$, every direction gives zero and every direction is a maximizer, even though the sum of unsigned triangle areas may be positive. Reversing the initial orientation reverses every signed result consistently.

A subdivision surface enquiry acts on the limit surface, not just on the current control mesh. The basic subdivision surface interrogation operations are: enumerate initial patches and their topological adjacency; subdivide a patch into covering children with inherited boundary links; return conservative bounding volumes; estimate approximation error and, where available, normal variation; and evaluate limit positions and first derivatives or tangent data. Boundaries, creases and extraordinary subdivision vertices need explicit rules. Nonnegative convex subdivision weights can supply a local control-hull enclosure, but a scheme with negative weights needs another proved enclosure; an arbitrary control-mesh box is not automatically a bound on the limit.

The intersection of subdivision limit surfaces can be computed by a paired recursive search. Start with every relevant pair of root patches $(A,B)$, along with their domains and adjacency identifiers:

A termination criterion specifies geometric error and conditioning, not merely a fixed refinement depth. For isolated regular transverse components, shrinking limit enclosures and normal/error bounds permit reliable curve approximation. Candidate boxes cannot be discarded merely because no coarse edge has crossed another surface: a complete small closed component can lie in the interior. Tangency, coincident limit patches, creases and poorly conditioned extraordinary-point neighborhoods need special tests or must be reported as unresolved degenerate configurations. The intersection is that of the two subdivision limits; intersecting a finite refined mesh without error control only gives an approximation.

The basic parametric surface interrogation enquiries provide the parameter domain and patch adjacency; evaluation of $S(u,v)$; first derivatives $S_u,S_v$; and, for curvature or error-controlled stepping, second derivatives $S_{uu},S_{uv},S_{vv}$. Useful search enquiries also supply conservative bounding volumes over parameter rectangles, subdivision or restriction of a patch, and regularity/singularity information. A local inverse or closest-point enquiry can help seed a search but does not replace global component isolation.

At a regular point, $S_u,S_v$ are independent tangent vectors, and the oriented unit normal vector isThe tangent plane consists of $S+\alpha S_u+\beta S_v$. If the cross product is zero, this chart does not define a normal; use a regular alternative chart or treat a true singularity separately.

For two surfaces, collect the four parameters in $w=(u_1,v_1,u_2,v_2)$ and setAt a transverse intersection, $n_1\times n_2\ne 0$ and $J$ has rank three. Hence the implicit function theorem makes $F=0$ locally a curve. Choose its physical unit tangent vectorWith $G_i=A_i^T{A}_i$, the parameter derivatives for physical arc length areSince $\tau$ lies in both tangent planes, $A_i(u_i^{\prime },v_i^{\prime }{)}^T=\tau$ exactly. Thus these two pairs form a vector $d$ in the kernel of $J$.

A practical transversal intersection of two parametric surfaces algorithm is:

with Newton method. The final equation intersects the curve with the plane through $X_p$ perpendicular to the predicted tangent. Its Jacobian row is $[{\tau }^T{A}_1,0,0]$; together with $J$ it is nonsingular at a sufficiently nearby transverse point. This removes the otherwise free along-curve parameter in the three coincidence equations.

A finite tolerance supplies a polygonal or fitted-curve approximation; regularity and certified bounds determine its accuracy. Parallel normals, singular charts, branch contacts and coincident patches require separate handling: their intersection may be a singular curve, an isolated point or a two-dimensional region. The transverse marching equations must not be used to claim a unique curve in those cases.

Write $N_{i,d}$ for the partition-of-unity-normalized degree-$d$ B-spline, supported on $[t_i,t_{i+d+1}]$. Assume $d\ge 1$. First take distinct knots; endpoint repetitions will be interpreted as confluent limits. For fixed parameter $t$, let $f_t(\tau )=(\tau -t{)}_{+}^d$, and formwhere the brackets denote a divided difference in the knot variable $\tau$. This representation follows from the defining properties, rather than assuming a derivative recurrence. If $t<t_i$, $f_t$ is a degree-$d$ polynomial in all its knot arguments and its order-$(d+1)$ divided difference is zero. If $t>t_{i+d+1}$, every truncated-power term is zero. Between knots it is a degree-$d$ polynomial with $d-1$ continuous derivatives. Those support and smoothness conditions determine a one-dimensional spline space: there are $d+1$ spans, the continuity conditions leave $2d+1$ coefficients, and vanishing through order $d-1$ at both support ends imposes $2d$ conditions.

The remaining scalar is fixed by the prescribed integral. Put $a=t_i$, $b=t_{i+d+1}$. Integrating the truncated power first givesThe order-$(d+1)$ divided difference of this polynomial is its leading coefficient $1/(d+1)$, so the proposed $N_{i,d}$ has integral $(b-a)/(d+1)$, exactly the specified normalization.

Now differentiate with respect to $t$ and apply the final recursion for a divided difference:In terms of normalized lower-degree B-splines, this is the B-spline differentiation formulaA zero denominator corresponds to a collapsed-support basis term and contributes zero. Repeated knots follow by coalescing distinct knots; at knots where the classical derivative is discontinuous, read the formula on each open span or one-sided. The stated maximal interior continuity corresponds to simple interior knots, while the clamped endpoint repetitions do not affect the interior argument.

Let the control-point abscissae be the Greville abscissaeCollect the coefficient of $N_{i,d-1}$ after differentiating:The difference of the two consecutive knot averages telescopes toso every noncollapsed coefficient is one. The lower-degree basis on the endpoint-trimmed knot vector has partition of unity; equivalently the outer lower-degree basis functions on the original vector have collapsed support. Therefore $X^{\prime }(t)=1$ on the parameter interval. With $d+1$ repeated left endpoint knots at $a$, the clamped basis has $N_{0,d}(a)=1$, all other values zero, and ${\xi }_0=a$. Thus $X(a)=a$ fixes the constant of integration. Continuity and endpoint limits now giveThis is linear precision at Greville abscissae. It requires neither equal knot spacing nor a restriction on the other control-point coordinates; those determine the remaining coordinates of the parametric curve.

An invertible $4\times 4$ matrix $M$, modulo scalar multiples, acts linearly on homogeneous coordinates. In an affine chart its projective transformation has the formwhere the denominator must be nonzero for a finite image. The important nested groups are Euclidean motions, Euclidean similarities, invertible affine maps, and general projective transformations. Their defining preserved structures become progressively weaker.

A Euclidean motion has $x^{\prime }=Rx+b$ with $R^{T}R=I$. It preserves all distances and angles, hence lengths, areas and volumes. Proper motions have $\det R=1$ and preserve orientation as well; allowing reflections preserves unsigned measurements but can reverse signed ones. A Euclidean similarity has $x^{\prime }=sRx+b$ with $s>0$. It preserves angles and all ratios of lengths; lengths, areas and volumes scale by $s,s^2,s^3$. Thus rigid shape is preserved up to one overall scale.

An invertible affine map has $c=0$ and preserves the plane at infinity. It preserves incidence, parallelism, collinear ratios, affine combinations, and ratios of volumes. Absolute volume scales by $|\det A|$; the volume-preserving affine subgroup has $|\det A|=1$. Lengths and angles are not affine invariants. A general projective transformation preserves incidence, collinearity, concurrence, tangency, and the cross-ratio of four collinear points, but can move the plane at infinity. The cross-ratio, rather than a three-point affine ratio, survives arbitrary projective changes. For a homogeneous quadric hypersurface $X^{T}QX=0$, its transformed matrix is $M^{-T}Q{M}^{-1}$, so its rank and projective degeneracy are preserved.

For surface classes, rather than a fixed numerical radius or curvature, the consequences are:

For example, the affine stretch $(x,y,z)\mapsto (2x,y,z)$ changes a unit sphere to $x^{\prime 2}/4+y^{\prime 2}+z^{\prime 2}=1$ and a unit circular cylinder to $x^{\prime 2}/4+y^{\prime 2}=1$. A general conical surface written $P+tD(s)$ transforms affinely into $T(P)+tAD(s)$, so every generator still passes through a finite apex. A nonsingular elliptic paraboloid or hyperbolic paraboloid has quadratic part of rank two and a nonzero linear component in its null direction. An invertible affine map preserves that rank, inertia and nonzero null-direction coupling, so translation and linear coordinate changes again put it in elliptic or hyperbolic paraboloid form.

Under a genuinely projective change, these Euclidean classifications depend on which plane is designated as infinity. For the map $(x,y,z)\mapsto (x,y,z)/(1+\alpha z)$, a circular cylinder $x^2+y^2=1$ becomesa cone whose apex is finite. The inverse map sends that apex to infinity and produces the cylinder. Thus a cone is projectively invariant as a family of concurrent generators if an ideal apex is allowed, but finite-apex cones and cylinders are not separately invariant. Likewise a projective image of a sphere or either type of paraboloid remains a nonsingular quadric hypersurface, but need not retain its affine or metric type. The distinction between general cones and circular cones matters: circularity is a metric property and is not preserved by arbitrary affine changes.

We prove uniqueness in the sense of at most one Gibbs measure satisfying the printed conditional distributions. No shift-invariance assumption is needed in advance. Write a history as $\eta =({ϵ}_{s-1},{ϵ}_{s-2},\dots )$. The variation hypothesis makes $U$ continuous and bounded on the finite-alphabet sequence space; let $u_{-}\le U\le u_{+}$ and $V=u_{+}-u_{-}$. The conditional distribution of the first spin, obtained by marginalizing the prescribed two-spin law, must beIn particular $q(a\mid \eta )\ge \delta :=e^{-2V}/k>0$ for every symbol and history. Any two candidate measures have this same time-homogeneous one-step kernel, by conditional marginalization and the tower property.

If $\eta ,{\eta }^{\prime }$ agree in their most recent $r$ symbols, each two-spin energy in this formula changes by at mostBoth numerator and denominator therefore change by factors between $e^{-D{\lambda }^{-r}}$ and $e^{D{\lambda }^{-r}}$. Consequently the total variation distance of the two kernels is at most $e^{2D{\lambda }^{-r}}-1\le B{\lambda }^{-r}$, where $B=e^{2D}-1$. Uniform positivity also bounds it by $1-k\delta$. PutThese bounds are decreasing, strictly below one, and summable. We now give the summable-memory coupling argument rather than assuming a small one-step contraction coefficient.

Starting from any two histories at a remote time, use a maximal coupling of the next-spin laws. If the actual histories have agreed for $r$ consecutive symbols, their probability of disagreement is at most $b_r$. Use shared independent uniform random numbers to construct a dominating match-length process $R$: it increases from $r$ to $r+1$ with probability $1-b_r$ and resets to zero with probability $b_r$. Since $b_r$ decreases, the actual consecutive-match length can be kept at least $R$ at every step. When the dominating process does not reset, the actual spins can be coupled to agree.

A new run from zero has probabilityof never resetting. The product is positive because $b_r<1$ and $\sum b_r<\infty$: its tail logarithm converges absolutely, and its finite initial factors are positive. After each reset a fresh run has the same success probability, so the probability of $m$ failed runs is $(1-p_{\ast }{)}^m$. There are almost surely only finitely many resets. Every failed run has finite duration, and thus the time of the final reset is finite almost surely. Its distribution depends only on $(b_r)$, not on the two starting histories. It follows that the probability of any disagreement in a fixed future block tends uniformly to zero as the block's distance from the initial time tends to infinity.

Now take two candidate Gibbs measures, sample their respective histories before time $-N$, and couple their forward kernels as above. Their finite-block laws really are obtained by these kernels: successive conditional expectations use the common conditional distribution $q$. For any fixed coordinate block $[s,t]$, the coupling inequality for total variation bounds the distance between the two candidate block laws by the dominating probability of a reset at or after step $N+s$. That bound tends to zero as $N\to \infty$. Hence all probabilities of finite cylinder sets agree; cylinder sets generate the product Borel sigma-algebra, so the measures agree.

Thus there is at most one measure satisfying these conditional specifications. There is a separate compatibility issue: for an arbitrary unnormalized symbolic potential the finite-horizon conditional distributions printed in the question need not be consistent as the right endpoint changes. The argument proves the requested uniqueness whenever such a law exists; it does not silently infer existence from the variation estimate. A normalized one-sided kernel or the usual compatible Gibbs specification supplies the standard existence formulation. For a concrete incompatibility, take two symbols and $U(a,\eta )=\log W_{{\eta }_1,a}$ with $W=\left(\begin{array}{cc}1& 1\\ 1& 2\end{array}\right)$. This has zero variation once two symbols agree. From a history ending in symbol one, the first-symbol probability obtained from a two-spin block is $2/5$, but from a three-spin block it is $5/13$. Thus the printed finite-horizon specification can indeed have no measure, even though its uniqueness implication remains valid.

A periodic pattern breaks continuous translation symmetry: translating it changes its phase without changing its shape or its energy. Consequently a uniform phase change is neutral. A slowly varying phase change alters the local spacing or orientation and can grow even while ordinary uniform amplitude perturbations decay. These are long-wavelength sideband instabilities. The relevant wavelengths are long compared with the carrier pattern, so their leading dynamics is often a phase modulation equation. Positive phase diffusion smooths spacing errors; negative phase diffusion amplifies them. Near onset, an amplitude equation computes this diffusion explicitly. Away from onset, one must calculate it from the full nonlinear periodic pattern.

For a supercritical stationary pattern in one dimension, rescale the real Ginzburg–Landau equation toA detuned periodic pattern is represented by $A=R{e}^{iqx}$, with $R^2=\mu -q^2>0$. Perturb its magnitude and phase by $A=(R+u)e^{i(qx+\varphi )}$. Linearization givesFor a Fourier mode proportional to $e^{\lambda t+ikx}$, the two eigenvalues are the sideband spectrum of a real Ginzburg-Landau plane wave:The magnitude branch remains damped at small $k$. The phase branch expands asHence the Eckhaus-stable band is $\boxed{q^2<\mu /3}$, narrower than the existence band $q^2<\mu$. This Eckhaus instability is longitudinal: it compresses and dilates the pattern along its wavevector. The coupling to the relaxing magnitude is crucial; examining phase diffusion while holding the magnitude fixed would miss the destabilizing $2q^2/R^2$ correction. At the stability boundary the $k^2$ coefficient vanishes, and the next term is negative, $-2q^4{k}^4/R^6$. Beyond the boundary, a phase distortion can create amplitude zeros and phase slips, changing the number of periods. These nonlinear events are possible outcomes, not conclusions of the linear calculation.

A fixed periodic domain allows only discrete sideband wavenumbers. Indeed, the displayed spectrum is unstable only if $0<k^2<6q^2-2\mu$. A domain too short to support such a Fourier mode can suppress the nominal long-wave instability. Thus an infinite-domain stability band and a finite-domain threshold must be distinguished.

The zigzag instability concerns transverse bending of rolls in an isotropic two-dimensional system. Let $k_c$ be the preferred carrier wavenumber, use an $x$-directed carrier, and denote its slow longitudinal detuning by $q$. Rotational covariance near onset leads to the anisotropic envelope equationThe derivative combination comes from expanding $|\mathbf{k}|-k_c$: a small transverse carrier component changes the length of the wavevector only at second order. A straight-roll envelope $A=R{e}^{iqX}$ has $g{R}^2=\mu -\xi q^2$. For a purely transverse phase perturbation $A\simeq R{e}^{iqX}(1+i\varphi (Y,t))$, magnitude and phase decouple at linear order, andThe transverse growth rate is thereforeRolls with $q<0$, whose total wavenumber is below the preferred value, are zigzag-unstable to sufficiently long transverse modulations. Bending increases their local wavevector length and can relieve this mismatch. Rolls with $q>0$ are transversely stable in this near-onset model; at $q=0$ the restoring force first appears at fourth spatial order. Combining this with the longitudinal Eckhaus instability gives the robust interior stable band $0<q<\sqrt{\mu /(3\xi )}$ for this simplest isotropic roll model. Anisotropy, a pinned orientation, additional mean fields, or other pattern symmetries can modify this conclusion.

For oscillatory patterns the coefficients of the amplitude equation are generally complex. Consider the scaled complex Ginzburg–Landau equationwhere $c_1$ measures linear dispersion and $c_3$ the amplitude-dependent frequency shift. The uniform oscillation is $A=e^{-i{c}_{3}t}$. Put $A=e^{-i{c}_{3}t}(1+u+iv)$, with real infinitesimal $u,v$. The linearized magnitude and phase equations areTheir characteristic polynomial for a sideband of wavenumber $k$ isThe magnitude mode has leading eigenvalue $-2$, while the neutral phase mode hasThus the Benjamin-Feir instability occurs for $\boxed{1+c_1{c}_3<0}$; the complementary Benjamin-Feir stability condition is $1+c_1{c}_3>0$. Both dispersive couplings matter: the phase perturbs the magnitude through $c_1$, and the magnitude feeds back into phase through $c_3$. In the unstable regime, higher spatial derivatives regularize sufficiently short disturbances. On a finite domain, an unstable mode must satisfy $0<k^2<-2(1+c_1{c}_3)/(1+c_1^2)$. At equality of the phase-diffusion coefficient to zero, the leading phase damping is fourth order, not exponential growth at order $k^2$.

Oscillatory plane waves with a nonzero carrier detuning also have a generalized Eckhaus boundary. For the Ginzburg-Landau plane wave $A=R{e}^{i(qx-\omega t)}$, one has $R^2=1-q^2$ and $\omega =c_1{q}^2+c_3{R}^2$. Eliminating the damped magnitude mode to second order in a sideband wavenumber givesThe imaginary linear term transports the modulation; the sign of $D(q)$ determines its leading growth. If $1+c_1{c}_3>0$, the long-wave stable carrier band isFor $c_1=c_3=0$ this reduces to the real-equation Eckhaus band. If $1+c_1{c}_3<0$, even the zero-detuning oscillation has the Benjamin-Feir instability. Nonlinear outcomes can include smooth phase modulation, defect production or phase turbulence, depending on the equation and parameters.

The general nonlinear-pattern problem starts from a periodic solution $u_0(x)$ of the full field equations, not from a small-amplitude envelope. Linearize about it to obtain an operator $L$ with periodic coefficients. Write a disturbance as $e^{\lambda t+iq\cdot x}v(x)$ with $v$ periodic on the pattern cell. The Bloch stability operatoracts on this fixed cell and determines the entire sideband spectrum. Translation covariance gives $L(0){\partial }_x{u}_0=0$ in one dimension, and corresponding neutral directions for each independent broken translation in several dimensions. Oscillatory base patterns require the analogous Floquet theory calculation over their temporal period, or a rotating frame where applicable.

For a simple translational zero eigenvalue, the phase-diffusion calculation can be made explicit. Expandwhere $v_0=u_0^{\prime }$ and $L_0{v}_0=0$. Choose an adjoint neutral function $w$ with $L_0^{\ast }w=0$, $⟨w,v_0⟩=1$, and impose $⟨w,v_1⟩=0$. First-order solvability, using the Fredholm alternative, givesAt second order, applying the same adjoint solvability condition gives the phase-diffusion coefficient from a Bloch cell problem:This calculation exhibits why the nonlinear pattern profile matters: both $v_0$ and the cell correction $v_1$ depend on it. A negative $D$ signals a long-wave instability provided the rest of the spectrum is stable. With several phase directions, or an extra neutral conserved mean, one obtains a coupled modulation system and diffusion matrices rather than this scalar formula. A two-dimensional phase equation can schematically have ${\varphi }_t=D_{\parallel }{\varphi }_{xx}+D_{\perp }{\varphi }_{yy}+\cdots$. Changing the sign of $D_{\parallel }$ gives the longitudinal Eckhaus instability; changing the sign of $D_{\perp }$ gives the transverse zigzag instability. For oscillatory waves, nonvariational magnitude-phase coupling gives the Benjamin-Feir instability.

These mechanisms extend beyond onset, but the near-onset numerical boundaries do not: their coefficients must be replaced by those obtained from the actual nonlinear pattern and its cell problems. Long-wave linear stability is also only one part of stability; finite-wavenumber eigenvalues, additional modes, defects, and boundary constraints can intervene. The unifying calculation is the splitting of translation-induced neutral modes at small modulation wavenumber, together with the coupling to the relaxing magnitude and any other slow fields.