Past exam of the mathematics course of the University of Cambridge / 2009 / iii / Paper 72 (Numerical Solution of Differential Equations)

Multiply by a test function vanishing at the endpoints and apply integration by parts. The positive operator is $-u^{\prime \prime }+xu$, so the forcing changes sign. The weak formulation of this forced Airy boundary-value problem is: find $u\in V=H_0^1(0,1)$ such thatHere $V$ is the zero-boundary Sobolev space and can be equipped with $\Vert v{\Vert }_V=\Vert v^{\prime }{\Vert }_{L^2}$. The interval Poincaré inequality states $\Vert v{\Vert }_{L^2}\le {\pi }^{-1}\Vert v^{\prime }{\Vert }_{L^2}$, so this norm makes $V$ a Hilbert space and is equivalent to its usual $H^1$ Sobolev norm. The form is symmetric and satisfiesThe Lax-Milgram theorem says that a bounded coercive bilinear form on a Hilbert space and a bounded linear functional determine exactly one weak solution. All its hypotheses have just been verified. Moreover, $u^{\prime \prime }=xu+1\in L^2$ gives $u\in H^2$ on the interval; the right-hand side is then continuous, so this weak solution is the classical solution with the prescribed endpoint values.

Since the form is symmetric, the equivalent Ritz method minimizesIndeed, stationarity gives the weak equation andwith equality only at $u$. This proves the minimization equivalence, including the sign of the linear term.

Restricting to a conforming finite element space $V_h\subset V$ gives a unique discrete solution and Galerkin orthogonality, $a(u-u_h,v_h)=0$ for every $v_h\in V_h$. The Céa lemma states that if the continuity and coercivity constants are $M$ and $\alpha$, then $\Vert u-u_h{\Vert }_V\le (M/\alpha ){\inf }_{v_h\in V_h}\Vert u-v_h{\Vert }_V$. For this symmetric problem the exact best-approximation constant in the energy norm is one. The piecewise-linear finite element interpolation estimate $\Vert u-I_{h}u{\Vert }_{H^1}\le Ch\Vert u{\Vert }_{H^2}$ on regular finite element meshes therefore gives convergence of a numerical method of the Ritz method, with first-order error in the energy norm.

Take a uniform finite element mesh $x_i=ih$, $0\le i\le J$, $h=1/J$, and interior piecewise-linear hat functions ${\varphi }_i$. Write $u_h={\sum }_{i=1}^{J-1}{U}_i{\varphi }_i$. The Ritz method gives $KU=F$ withThe derivative part has diagonal $2/h$, neighboring entries $-1/h$ and zero other entries. For the affine-weighted hat mass matrix, symmetry around $x_i$ givesOn $[x_i,x_{i+1}]$, the product of the two hats is symmetric about the midpoint, integrates to $h/6$, and therefore has weighted integralConsequently the explicit tridiagonal equations arefor $1\le i\le J-1$, with $U_0=U_J=0$. The reaction coefficient has been integrated exactly; replacing it by an unweighted constant mass matrix would give a different system. For a nonzero coefficient vector, the represented function is nonzero and $U^{T}KU=a(u_h,u_h)>0$, so the matrix is symmetric positive-definite and the algebraic solution is unique.

Uniform spacing is a choice, not a requirement of the method. On a general finite element mesh with $h_i=x_i-x_{i-1}$, the corresponding entries arewith symmetric lower entries. These reduce to the displayed uniform-grid system.

For this two-step family with a third-order member, use the exponential-symbol order criterion for a multistep method. Expanding the exact-solution residual symbol givesThe constant, linear and quadratic coefficients vanish for every parameter. ThusAt the exceptional value the fourth-degree coefficient is $-1/30$, so there is no further increase. This means an unscaled exact-solution residual of order $h^{p+1}$, or a residual divided by the step of order $h^p$.

The first characteristic polynomial factors as $\rho (w)=(w-1)(w-a)$. The root condition for a multistep method requires every root to have modulus at most one and each unit-modulus root to be simple. The root $a=-1$ is allowed because it is distinct from the simple root one; $a=1$ is not, since it makes one a double root. Hence zero-stability holds exactly forOn this interval, $\rho (1)=0$ and ${\rho }^{\prime }(1)=\sigma (1)=1-a\ne 0$ establish consistency of a numerical method. The Dahlquist equivalence theorem, for a fixed-step consistent linear multistep method applied to an ordinary differential equation with a Lipschitz continuous right-hand side with convergent starting values and well-defined small-step updates, says that convergence of a numerical method is equivalent to zero-stability. Therefore the same interval is exactly the convergence of a numerical method range. With sufficiently accurate starting values the convergent members have the orders just computed.

At $a=1$ the Taylor expansion still gives a formal order-two residual, but ${\rho }^{\prime }(1)=\sigma (1)=0$ and the double root prevents a convergent method. Canceling the common factor $w-1$ produces the common-factor cancellation defect in a multistep recurrence and changes the starting relations and cannot repair convergence of a numerical method of the original recurrence. At $a=0$, cancellation of a zero root instead reveals the ordinary trapezoidal rule; it is consistent with the order-two result.

Apply the method to the Dahlquist test equation, put $z=h\lambda$, and retain every root of its amplification polynomial of a multistep method:An A-stable convergent method must satisfy the root condition for a multistep method for every $\mathrm{Re}z\le 0$.

First take $-1<a<0$. As negative real $z$ tends to minus infinity, one root tends to the nonzero root of $\sigma$,It follows by root continuity that some sufficiently negative finite $z$ is unstable. At $a=-1$, the recurrence has polynomial $w^2-2zw-1$; for any negative real $z$, its root $z-\sqrt{z^2+1}$ is below $-1$. Thus all negative convergent parameters are excluded.

For $0\le a<1$, use the boundary-locus test for multistep A-stability. A unit root $w=e^{i\theta }$ with $\sigma (w)\ne 0$ would require $z=\rho (w)/\sigma (w)$. Direct multiplication by the conjugate denominator givesThere is therefore no unit-root crossing in the open left half-plane. The leading coefficient of $P_z$ never vanishes there, since its only zero is the positive real value $2/(1+a)$. For a small negative real $z$, the simple root initially at one moves to $1+z+O(z^2)$, inside the disk, and the other root is close to $a$, also inside. Continuity of polynomial roots and connectedness of the open left half-plane then keep both roots strictly inside throughout that half-plane. Possible zeros of $\sigma$ cause no omitted crossing: for $a>0$ its other root lies strictly inside the unit disk, while at $a=0$ its unit-circle zero $w=-1$ has $\rho (-1)\ne 0$.

On the imaginary axis, if $a>0$ the displayed real part vanishes only at $w=1$, which corresponds to $z=0$ and is simple. Other imaginary-axis parameters retain strictly interior roots. If $a=0$, the two roots are zero and the stability function $(1+z/2)/(1-z/2)$, whose modulus is one on that axis; the unit root is simple. Thus the required boundary root condition for a multistep method holds as well.

Combining necessity and sufficiency givesIn particular, the third-order member $a=-1/5$ is convergent but not A-stable, in agreement with the Second Dahlquist barrier.

Let $h=\Delta x$, $k=\Delta t$ and $\mu =k/h$. For the exact solution $u(x,t)=F(x+t)$ of the advection equation, the three left-hand samples are translations by $(\mu -1)h$, $\mu h$ and $(\mu +1)h$ relative to $(x_m,t_n)$; the right-hand samples are translations by zero and $h$.

Denote their coefficients by $a=\mu (1+\mu )/6$, $b=(2-\mu )(1+\mu )/3$, $c=(2-\mu )(1-\mu )/6$, $d=(2-\mu )/3$ and $e=(1+\mu )/3$. Taylor expansion compares momentswhere $0^0=1$ in the zeroth moment. Direct calculation givesThus the unscaled exact-solution residual isFor fixed nonzero $\mu$, dividing by the time step yieldsThe scheme is therefore third order under fixed-Courant refinement, with an unscaled one-step defect of order four. Stability then gives third-order accumulated error for smooth data with periodic boundary conditions. Calling the unscaled defect fourth order is a different convention, not fourth-order approximation of the differential equation.

At $\mu =-1,1,2$, the amplification factor in (b) becomes exactly $e^{i\mu \theta }$: the numerical step translates the grid data by that integer number of cells and is exact for this constant-speed advection equation. At $\mu =0$ it is the identity, a zero-time-step limit. These are exact special shifts, rather than merely a cancellation of one term of the Taylor expansion. This is the implicit advection scheme with exact integer shifts.

Choose a spatial interval with periodic boundary conditions and a uniform grid, so no separate inflow boundary closure of a difference scheme is required. The discrete Fourier transform is a unitary operator in the discrete L2 norm. Every circulant stencil is diagonal in that transform, and a one-step Fourier multiplier is a contraction exactly when its modulus is at most one for every grid frequency. For a mesh-uniform statement, use all $\theta \in [-\pi ,\pi ]$, the limiting frequency range.

A Fourier mode $e^{im\theta }$ giveswith the coefficients from (a). Squaring moduli and putting $v=\cos \theta$ yieldsThis has the required nonnegative sign for every frequency exactly whenOne must also check that the implicit equation is solvable, rather than divide by a vanishing symbol. HereFor $\mu \ne 1/2$, a zero would have to occur at zero or $\pi$; the latter occurs only at $(1\pm \sqrt{3})/2$, both outside the displayed stable intervals. At $\mu =1/2$, $D=3/4+(1/4)\cos \theta \ge 1/2$. Thus there is no denominator zero in the stable range. For fixed such $\mu$, continuity on the compact frequency interval gives a uniformly bounded inverse, and $|G|\le 1$ proves $\Vert U^n{\Vert }_{2,h}\le \Vert U^0{\Vert }_{2,h}$ for all steps.

Outside these intervals the displayed difference is negative at every nonzero frequency where $D\ne 0$, so $|G|>1$. On periodic refining meshes there are frequencies approaching any selected such value. Their amplification is bounded away from one and is repeated $O(h^{-1})$ times over a fixed physical interval when $k=\mu h$, violating a mesh-uniform stability bound. At the two singular parameters the implicit operator itself fails on meshes containing the Nyquist mode. These observations prove necessity as well as sufficiency.

For the physical convention $k>0$, $h>0$, the result is $0<\mu \le 1$ or $\mu \ge 2$, with zero admitted as the identity limit. The negative intervals describe the algebraic contraction result for signed Courant numbers. In particular, the additional large positive interval must not be discarded by imposing the usual explicit-scheme Courant–Friedrichs–Lewy condition: this scheme is implicit. Stability at large fixed Courant number is not a guarantee of a small error constant.

For an $s$-stage Runge-Kutta method with coefficient matrix $A=(a_{ij})$ and weights $b=(b_i)$, put $B=\mathrm{diag}(b_i)$. It is algebraically stable whenEquivalently, $m_{ij}=b_i{a}_{ij}+b_j{a}_{ji}-b_i{b}_j$ defines a positive semidefinite symmetric matrix. The condition concerns the full coefficient tableau and nonlinear contractivity, not merely the scalar stability function. It is normally used with a consistent Runge-Kutta method and stage equations satisfying the necessary stage solvability of an implicit Runge-Kutta method.

The Butcher contractivity theorem states that an algebraically stable Runge-Kutta method is B-stable: for a dissipative vector field and any positive step for which the stage problems are solved, its step does not increase the distance between two numerical solutions. The required condition on the dissipative vector field isfor all compared states at each common time. The inner product can be real Euclidean or complex Hermitian. The stage solvability of an implicit Runge-Kutta method is assumed in this comparison, not a conclusion from a distance estimate alone.

Let $d$ be the difference of starting values, $D_i$ the differences of corresponding stages and $F_i$ the differences of their vector-field values. ThenExpanding the squared norm of the update givesSubstitute $d=D_i-h{\sum }_j{a}_{ij}{F}_j$ in the linear term. Symmetrizing the double sum proves the Runge-Kutta contractivity identityEach inner product in the first sum is nonpositive by the defining inequality for a dissipative vector field, and its weight is nonnegative. The final quadratic form is nonnegative: resolve the vectors $F_i$ into components and apply positive semidefiniteness of $M$ to each component vector. Hence $\boxed{\Vert d_{+}\Vert \le \Vert d\Vert }$, which is exactly B-stability. No linearization of the vector field has been used.

For the Dahlquist test equation $y^{\prime }=\lambda y$ with $\mathrm{Re}\lambda \le 0$, this also bounds the amplification factor by one wherever the stages are well defined. With strictly positive weights, no stage pole can occur in the open left half-plane. Indeed, if $(I-zA)v=0$ for nonzero $v$, thenwhen $\mathrm{Re}z<0$, contradicting $M⪰0$. This proves the usual A-stability corollary for the scalar stability function; boundedness from the open half-plane makes any boundary pole in that rational function removable. Zero-weight redundant stages require their own solvability treatment. The nonlinear B-stability statement is stronger than A-stability, and the two notions should not be conflated.

For the first Butcher tableau, $b=(1/2,1/2{)}^T$ and the off-diagonal entries sum to $1/2$, while each diagonal entry is $1/4$. ThusThe positive weights and zero matrix prove that method 1 is algebraically stable. It is the two-stage Gauss collocation method.

The second method is explicit and has $b=(1/4,3/8,3/8{)}^T$. Its first diagonal algebraic-stability entry isA positive semidefinite matrix cannot have a negative diagonal quadratic form, so method 2 is not algebraically stable. The negative value supplies a direct test; its order is irrelevant to this conclusion.

For the third method, $A=\left(\begin{array}{cc}1/4& -1/4\\ 1/4& 5/12\end{array}\right)$ and $b=(1/4,3/4{)}^T$. Direct computation givesBoth weights are positive, so method 3 is algebraically stable.

Interpret the square as $[0,1{]}^2$, use equal spacing $h=1/J$ in both directions and retain interior indices $1\le m,n\le J-1$. Boundary values contribute to the right-hand side and do not change the interior coefficient matrix. Products of the one-dimensional sine modes used in the discrete sine transformform a basis, and substitution into the five-point Laplacian givesThe interior matrix is ${\Delta }_h+\lambda I$, so it is nonsingular exactly whenThese are the discrete Helmholtz resonance values, including their possible multiplicities. A sufficient condition is $\lambda <{\Lambda }_{11}=8h^{-2}{\sin }^2(\pi /(2J))$, so in particular every nonpositive $\lambda$ is allowed. That is not a necessary restriction: positive values between, or above, the finitely many discrete eigenvalues are nonsingular as well. With homogeneous Dirichlet boundary conditions a nonresonant system has only the zero solution; at a resonance the corresponding modes of the discrete sine transform give nonzero homogeneous solutions.

For a sufficiently smooth function, Taylor expansion of the four neighboring values givesThe reaction term $\lambda u$ is sampled exactly at the grid point. On an exact solution, therefore,The method has second-order local accuracy for the partial differential equation. The original printed equations multiply this expression by $h^2$, so their unscaled residual is $O(h^4)$; that scaling does not make the Laplacian approximation fourth order. Resonance and inverse-matrix bounds are separate issues from this local truncation calculation.

Let $S_{1}u$ be the sum of the four axial neighbors and $S_{2}u$ the sum of the four diagonal neighbors. The two PDF stencils defineThe reaction stencil has four axial weights $1/12$ and center weight $2/3$, so its weights sum to one. Taylor expansion givesFor example, the diagonal neighbors supply the $u_{xxyy}$ term needed to turn the fourth derivatives into the full squared Laplacian. Adding the reaction contribution producesOn a smooth solution of the constant-coefficient equation, both displayed leading terms vanish. Thus the compact fourth-order Helmholtz stencil hasThe cancellation uses the differential equation and constant $\lambda$; the nine-point finite-difference stencil alone still has an $O(h^2)$ operator error on an arbitrary smooth function. It also does not establish a uniform global fourth-order error without an appropriate inverse bound and compatible treatment of boundaries and resonances.

The eigenvalue stability analysis of a finite difference method reduces a linear evolution discretization to its modal behavior, but it must retain the norm and the dependence on the mesh. For a homogeneous system obtained by the method of lines, $U^{\prime }=A_{h}U$, the solution is $e^{t{A}_h}U(0)$. A one-step time discretization gives $U^{n+1}=G_{h,k}{U}^n$; for a Runge-Kutta method, where its stage matrices are invertible, $G_{h,k}=R(k{A}_h)$. With a multistep method one instead uses an amplification matrix on the augmented vector of several time levels.

The relevant finite-time stability estimate iswith $C_T$ independent of the refining spatial mesh and allowed time steps. Similarly, semidiscrete stability requires $\Vert e^{t{A}_h}{\Vert }_h\le C_T$ for $0\le t\le T$. The norm should represent the continuum problem, such as the discrete L2 norm $\Vert U{\Vert }_{2,h}^2=h{\sum }_j|U_j{|}^2$ in one dimension. A fixed finite matrix having decaying solutions as $t\to \infty$ is not by itself a mesh-uniform result for the partial differential equation.

If $G=X\Lambda X^{-1}$, thenThus eigenvalues of modulus at most one give a uniform bound when the diagonalizing bases have uniformly bounded condition numbers. For a normal matrix in the chosen inner product, the spectral theorem for normal operators supplies an orthonormal basis, so the condition number is one and the norm of $G^n$ is exactly ${\max }_j|{\lambda }_j{|}^n$. For a self-adjoint or skew-adjoint spatial matrix, applying a scalar stability function preserves this favorable modal structure. This is the setting where eigenvalue calculations provide especially clean, reliable step restrictions.

A precise fixed-matrix statement is also useful: a matrix is power bounded for all nonnegative integers $n$ if and only if all its eigenvalues lie in the closed unit disk and every Jordan block at a unit-modulus eigenvalue has size one. Indeed, powers of an interior Jordan block contain a polynomial in $n$ times a decaying geometric factor and are bounded; a larger block on the unit circle produces unbounded polynomial growth. For a family of matrices, the resulting bounds must still be uniform. Moreover, finite-time stability versus power boundedness distinguishes this all-time condition from the physical-time estimate above. For example $G_k=I+kN$, $N^2=0$, has $G_k^n=I+nkN$, bounded for $nk\le T$ despite a nontrivial Jordan block at one. Even the scalar update $1+k$ is stable on fixed intervals, since $(1+k{)}^n\le e^T$, although it represents a growing equation rather than a contraction.

For a constant-coefficient periodic difference operator, Fourier modes diagonalize the spatial matrix. The discrete Fourier transform is a unitary operator, so Parseval's identity identifies its modal maximum with the operator norm. The eigenvalue stability analysis of a finite difference method then becomes von Neumann stability analysis. For the Dirichlet discrete Laplacian, a discrete sine transform plays the same role. These facts explain its advantages: a large matrix calculation becomes a scalar symbol or a known spectral interval, and the extremal eigenvalue directly determines a safe time step.

As a first example, centered space discretization of the heat equation $u_t=u_{xx}$ has eigenvaluesForward Euler method gives $R(k\lambda )=1-4r{\sin }^2(\theta /2)$ with $r=k/h^2$. Requiring all factors in $[-1,1]$ yields $0\le r\le 1/2$ for all periodic meshes. In two dimensions the corresponding uniform bound is $r\le 1/4$. Because these are normal matrices, these scalar bounds prove contraction rather than merely suggest it.

Backward Euler method has $R(z)=1/(1-z)$, so the centered discretization of the heat equation is contractive for every $k>0$. The Crank-Nicolson method has $R(z)=(1+z/2)/(1-z/2)$ and is likewise contractive for these negative real modes. However, its factor tends to $-1$ for very stiff modes, leaving high-frequency oscillations weakly damped; Backward Euler method tends to zero. The eigenvalues therefore expose the presence of stiff differential equations and damping as well as binary stability. Unconditional stability does not remove accuracy requirements.

For an advection equation example, the upwind finite difference scheme for $u_t+c{u}_x=0$, $c>0$, has factor $G=1-\nu +\nu e^{-i\theta }$, with $\nu =ck/h$. Sinceit is a contraction in the periodic discrete L2 norm exactly for $0\le \nu \le 1$. In contrast, Forward Euler method with centered advection has factor $1-i\nu \sin \theta$. Its modulus exceeds one at nonzero active frequencies, and repeated amplification makes it unstable under fixed nonzero Courant refinement. A much smaller scaling $k=O(h^2)$ can bound its finite-time growth, demonstrating again that exact contraction and the most general stability estimate are different claims.

The major limitation is the possible presence of a nonnormal matrix. For example,Both eigenvalues are negative, yet at $t=1$ the second unit vector is amplified by at least $1/(eh)$. This proves that negative spectra do not imply uniform semidiscrete stability. Ill-conditioned eigenvectors, Jordan normal form and transient amplification can invalidate a spectral-only argument. Direct energy estimates or estimates for the resolvent can supply the missing norm control.

Boundary conditions are another limitation. A periodic symbol proves a periodic or whole-line result, not an arbitrary initial-boundary-value problem. Boundary closure of a difference scheme can introduce growing modes or mesh-dependent amplification. A spatial operator with variable coefficients can still be studied through its assembled matrix, but then whether it is a normal matrix, its condition number and the physical inner product must be checked rather than borrowed from a Fourier stability analysis. For the finite element method the natural mass matrix inner product often turns a generalized eigenvalue problem into a self-adjoint one.

For time-dependent or split updates, separate spectra are insufficient to control products. For instance, $G_1=\left(\begin{array}{cc}0& K\\ 0& 0\end{array}\right)$ and $G_2=\left(\begin{array}{cc}0& 0\\ K& 0\end{array}\right)$ each have only zero eigenvalues, while $G_2{G}_1$ has eigenvalue $K^2$. For $K>1$, alternating the two is unstable. A common contractive norm would control the products, but their individual eigenvalues do not.

Finally, stability and consistency of a numerical method have different roles. The Lax equivalence theorem states that, for a well-posed linear initial-value problem and a consistent finite difference method on compatible grid spaces, stability on every fixed time interval is equivalent to convergence of a numerical method as the admissible meshes refine. Bounded grid representation and the correct initial and boundary setup are part of that framework. An eigenvalue estimate can establish its stability hypothesis in the favorable settings described above; it neither proves consistency of a numerical method nor bypasses its uniformity requirements. The method is therefore a powerful modal tool, provided its spectral calculation is connected to a genuine norm estimate.

A fixed-step linear multistep method for $y^{\prime }=f(t,y)$ uses several previous solution and derivative values:The coefficients are fixed independently of the step size. It is explicit when ${\beta }_s=0$ and implicit otherwise. Its characteristic polynomials of a linear multistep method are $\rho (w)={\sum }_j{\alpha }_j{w}^j$ and $\sigma (w)={\sum }_j{\beta }_j{w}^j$. The contraction mapping theorem gives a uniquely solvable implicit step under a Lipschitz continuity bound $L$ whenever $k|{\beta }_s|L<|{\alpha }_s|$, by contraction of the new-value equation on the appropriate solution region. A suitable starting procedure must supply the first $s$ values; the method alone does not determine them.

Order is defined by inserting a smooth exact solution into the recurrence. If the residual is $O(k^{p+1})$ and its first nonzero coefficient occurs at that degree, the method has order $p$; dividing the residual by $k$ gives an $O(k^p)$ local truncation error. Taylor expansion gives the exact order conditions for a linear multistep methodwith the right side interpreted as zero at $q=0$, and failure at $q=p+1$ for exact order. Equivalently,The basic consistency of a numerical method conditions are $\rho (1)=0$, ${\rho }^{\prime }(1)=\sigma (1)$; for the ordinary nondegenerate consistent formulation this common value is nonzero. Exactness on constants and linear functions does not control propagation of numerical perturbations.

That propagation is the role of zero-stability. On $y^{\prime }=0$ the homogeneous recurrence is $\rho (E)y_n=0$. Its modes are $n^r{\xi }^n$, with powers of $n$ determined by root multiplicities. The theorem on the root condition for a multistep method states that zero-stability is equivalent to every root of $\rho$ having modulus at most one, with every unit-modulus root simple. Interior repeated roots are permitted, since geometric decay dominates their polynomial factors. An exterior root amplifies perturbations geometrically; a repeated unit root amplifies them polynomially and violates a step-independent perturbation bound.

The Dahlquist equivalence theorem states that a fixed-step consistent linear multistep method is convergent exactly when it is zero-stable. This concerns ordinary differential equations with a uniformly Lipschitz continuous right-hand side on the solution region over a fixed finite interval, convergent starting values, and the well-defined nearby branch of each small-step implicit solve. For order $p$ convergence of a numerical method, the solution must have the required smoothness and the starting errors must be $O(k^p)$.

The mechanism can be sketched explicitly. The root condition for a multistep method bounds the recurrence's discrete Green function. For an order-$p$ exact-solution defect $d_n=O(k^{p+1})$, the Lipschitz continuity error equation then gives an estimate of the formThe current-step term can be absorbed for small $k$. The discrete Gronwall inequality says that a bound $E_n\le A+Bk{\sum }_{j<n}{E}_j$ implies $E_n\le A(1+Bk{)}^n\le A{e}^{B{t}_n}$. There are $O(k^{-1})$ defects on a fixed interval, so their sum is $O(k^p)$ and this yields the claimed global order. Conversely, testing $y^{\prime }=0$ with vanishing starting perturbations in exterior-root or repeated-unit-root modes proves that convergence of a numerical method fails without the root condition for a multistep method.

For example, the two-step Adams-Bashforth method hasIt is explicit, order two and zero-stable, hence convergent with order-two starting values. By contrast, a recurrence with $\rho =(w-1{)}^2$ has solutions $y_n=y_0+n(y_1-y_0)$ on the zero differential equation. Starting differences $\sqrt{k}$ tend to zero yet become unbounded at a fixed positive time, since $n$ is of order $1/k$. A small local defect or formal high order cannot rescue this mode. Canceling a common factor may remove it only by changing the allowed starting relations, as the parameter endpoint in Question 2 illustrates.

Absolute stability addresses a different limit: apply the method to the Dahlquist test equation $y^{\prime }=\lambda y$ and put $z=k\lambda$. The amplification roots satisfyAll roots must satisfy the disk condition, with unit roots simple; one cannot inspect only the root that tends to one as $z\to 0$. The degree and solvability of the recurrence must also be preserved. The resulting set of $z$ is the domain of absolute stability. A-stability means that it includes the whole closed left half-plane, so the numerical solutions of every Dahlquist test equation with $\mathrm{Re}\lambda \le 0$ are stable for every positive step size. It is a linear test-equation property, not a proof of arbitrary nonlinear contractivity or high accuracy at large steps.

Forward Euler method has order one and factor $1+z$, so its stability region is the disk $|1+z|\le 1$. Backward Euler method has factor $(1-z{)}^{-1}$ and is A-stable. The trapezoidal rule is order two with factor $(1+z/2)/(1-z/2)$; its modulus is at most one throughout the left half-plane. These one-step methods are included as the simplest multistep examples. For the two-step Adams-Bashforth method, the negative-real stability interval is $-1\le z\le 0$: its polynomial is $w^2-(1+3z/2)w+z/2$, and the root reaches $-1$ at $z=-1$. It therefore imposes a step restriction when applied to strongly decaying modes.

The genuinely two-step BDF2 method isIt has order two, roots $1,1/3$ at zero step and is A-stable. For its normalized polynomials $\rho =3w^2/2-2w+1/2$, $\sigma =w^2$, the unit-circle boundary locus hasThe leading coefficient has no zero in the left half-plane, and the roots at a small negative $z$ are both inside the disk. Root continuity then proves open-half-plane stability; on the imaginary boundary the only possible unit root is the simple root one at $z=0$. This gives a direct proof of the stated A-stability rather than relying on a boundary plot.

Two sharp order barriers explain the tradeoff. The first Dahlquist barrier states that a consistent zero-stable real linear $s$-step method using only first derivatives has order at most $s+1$ for odd $s$ and $s+2$ for even $s$; an explicit method has order at most $s$. The Second Dahlquist barrier states that an irreducible A-stable real linear multistep method has order at most two. These statements concern fixed constant coefficients and the ordinary first-derivative multistep class, not multiderivative methods or arbitrary variable-step formulations. Thus higher order alone is not a route to unconditional stiff stability. Question 2 provides a concrete illustration: its third-order member is zero-stable but not A-stable.

No nontrivial consistent explicit multistep method is A-stable. Its amplification polynomial of a multistep method has a fixed highest coefficient, while at least one lower coefficient grows without bound as real $z\to -\infty$. Were all roots uniformly in the unit disk, their elementary symmetric polynomials would keep all the monic coefficients bounded, a contradiction. This is the coefficient argument behind explicit multistep methods cannot be A-stable.

In practice, selecting a method therefore involves accuracy, startup, the presence of a stiff differential equation, implicit-solve cost and step-size control. Zero-stability handles propagation as the step tends to zero; consistency of a numerical method supplies a vanishing defect; A-stability handles the numerical solutions of all decaying Dahlquist test equations at finite steps. None substitutes for the others, and variable steps or PDE discretizations require additional uniform stability arguments appropriate to their setting.