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Rosenbrock and linearly implicit methods — Examples

1 · Prerequisites

2 · Summary

These examples compute the scalar Rosenbrock–Euler factor and its backward-Euler match, show that a bounded zero Jacobian approximation can turn the selected two-stage method into an undamped explicit midpoint step on a stiff mode, and verify that omitting the second-stage correction destroys cubic local accuracy. A diagonal two-dimensional example counts one shifted QR factorization reused for two right-hand sides. The damping counterexample fixes hλ=4 and makes no order or uniform-convergence claim.

3 · Logical flowchart

4 · Definitions, theorems and proofs

None yet.

5 · Examples, counterexamples and false statements

CounterexampleConstruction: AI-adaptedVerification: AI-adaptedOpen item page →

Omitting the second-stage correction destroys cubic local accuracy

Statement

On y′=y from y=1, with J=1, T~=0, and α=1−1/2, consider the incorrect variant that retains k1=(1−αh)−1y and the midpoint evaluation F1=f(y+hk1/2) but replaces the corrected second stage by (1−αh)ℓ2=F1 and returns y+hℓ2. Its one-step expansion is y+=y+hy+(α+12)h2y+O(h3), whereas the exact flow is exp⁡(h)=1+h+12h2+O(h3). Since α>0, the altered method has a nonzero O(h2) local defect and fails the cubic local-error condition for order two. The altered formula does not satisfy the proper method's stage equations.

Facts & Assumptions

Given: f(y)=y, initial y=1, J=1, h>0 small, and α=1−1/2.

[F1]

Two-stage second-order Rosenbrock W-method with shared factorization: the correct stages use the second right-hand side F1−k1 and then set k2=q2+k1.

[F2]

If f:[a,b]→Rm is differentiable with integrable f′ then ∫abf′=f(b)−f(a); and a bounded derivative makes f Lipschitz: a differentiable vector curve with integrable derivative has endpoint difference equal to the integral of its derivative.

[F3]

The exponential is the unique solution of y′=y with y(0)=1: any differentiable solution on R of Y′=Y, Y(0)=1 equals exp⁡.

[F4]

The exponential function is smooth and (exp⁡)′=exp⁡: every derivative of exp⁡ equals exp⁡.

[F5]

The exponential function is strictly increasing: exp⁡ is increasing on R.

[F6]

The real exponential function and the number e by a power series: exp⁡(s)=∑n=0∞sn/n!, so exp⁡(0)=1.

[F7]

Proof

Given: The data in the Statement.

1.1givenalgebra

Define the altered variant as stated. Since W=1−αh, its first stage is k1=y/(1−αh). The midpoint value is F1=y+(h/2)k1, and the altered second solve gives ℓ2=F1/(1−αh).

1.2givenF3F4F6

Define Y(s)=exp⁡(s) for s∈R. By [F4], Y′=Y, and [F6] gives Y(0)=1; thus this is an exact solution of the IVP (unique on R by [F3]).

2.1step 1.1algebra

Put r=(1−αh)−1. The exact identity (1−x)−1=1+x+x2+x3/(1−x) shows r=1+αh+α2h2+O(h3); for 0<h≤1/(2α) the remainder is bounded by 2α3h3. Squaring this finite expansion gives r2=1+2αh+3α2h2+O(h3), with a bounded remainder on the same interval. Since F1=y+(h/2)k1=y(1+hr/2), the altered second stage is ℓ2=F1r=y(r+hr2/2)=y(1+(α+1/2)h+(α2+α)h2+O(h3)). Multiplying by h and adding y gives the displayed one-step expansion.

2.2step 1.2F4F5

By [F4], Y′′′(s)=exp⁡(s); since 0≤s≤h≤1, [F5] gives Y′′′(s)≤exp⁡(1).

3.1F2F4F7step 2.1step 2.2algebra

The derivatives of Y are continuous by [F4] and integrable on each compact subinterval by [F7]. Three applications of [F2] give exp⁡(h)=1+h+h2/2+R, where R=∫0h(h−s)2Y′′′(s)/2 ds and ∣R∣≤exp⁡(1)h3/6. Subtracting this expansion from the altered update in step 2.1 leaves αh2+O(h3), which is not bounded by Ch3 for any fixed C as h↓0 because α>0. Thus the altered scheme fails the cubic local-error condition for order two.

4.1F1step 3.1∎

The proper method's second right-hand side and corrected output are exactly the two terms changed in this variant by [F1]. Step 3.1 proves the altered formula has no cubic local defect, and this item makes no global-rate claim for it.

Finite check

For h=0.01 and y=1, the leading local-defect coefficient is αh2≈2.9289×10−5; the exact Taylor coefficient at h2 is 1/2, while the altered coefficient is 1/2+α.

ExampleConstruction: AI-adaptedVerification: AI-adaptedOpen item page →

Two stage right-hand sides reuse one shifted-Jacobian factorization

Statement

Take J=diag⁡(−1,−2), f(y)=Jy, y=(1,2)T, h=1, and the W-method approximation J~=0, T~=0. Choose a stored finite system containing zero and the normal values ±12,±1,±2,±4,±8, with unit roundoff 0<u≤1/100. Let a terminating certificate evaluator return norm upper bounds 2 for ∥W∥F, 0 for the computed-residual norm ∥r^∥2, and 5 for each other requested vector norm, and return certified upper bounds at most 1/2 for the residual-bound expressions in both solves. Take stage tolerances ε1=ε2=1. Then W=I2 and both stage right-hand sides use its single factor object. The exact stages are k1=(−1,−4)T, midpoint state (1/2,0)T, F1=(−1/2,0)T, and q2=F1−k1=(1/2,4)T. The prescribed QR factorization succeeds and is reused for both stage solves. No stability or rounding-cost claim is made.

Facts & Assumptions

Given: J=diag⁡(−1,−2), y=(1,2)T, h=1, J~=T~=0, a stored finite system containing the displayed dyadic values in normal range with u≤1/100, the stated norm upper bounds, and residual tolerances one.

[F1]

Two-stage second-order Rosenbrock W-method with shared factorization: both stages use the same W and the method factors W once.

[F2]

Reusable Householder QR solve for a shifted matrix with residual status: the factor object stores one QR factorization and each solve uses one triangular substitution.

[F3]

Scaled Householder reflector formation and application: on each nonzero stored active column, the specified finite arithmetic forms a reflector, updates the trailing block, and returns either the stored reflector data or a declared failure.

[F4]

A computed shifted-system residual has a certified upper bound: the exact linear residual is bounded by the computed residual and the explicit dot-product and subtraction allowance.

[F5]

Operationwise real and complex floating point arithmetic with rounded square roots and fused multiply-add: a successful operation on an exact zero or normal result returns its round-to-nearest value, and exact representable results are unchanged.

Proof

Given: The data in the Statement.

1.1givenF3F5algebra

Use the autonomous extension f(t,y)=Jy on D=R×R2, with in_domain returning true and exact field evaluation and storage checks. The W approximation is J~=0, so W=I2. For the first active column x=(1,0)T, [F3] has s=1, r^=1, α^=−1, v=(2,0)T, w=4, and τ^=1/2. Its trailing-block dot is zero, so the output block is diag⁡(−1,1). The second one-dimensional active column similarly has v=2,w=4,τ^=1/2 and produces R=−I2. By [F5] every exact representable intermediate is unchanged; all nonzero intermediates are among the listed stored normal values, no range branch fails, and both pivots are nonzero. Thus the one factorization succeeds.

2.1givenF1F2F5step 1.1algebra

The first RHS is f(y)=(−1,−4)T. In the first reflector replay the dot is −2 and the active vector p=τ^v is (1,0)T, so the update sends (−1,−4)T to (1,−4)T. The second replay has dot −8 and scalar p=1, so it sends the second coordinate to −4−(−8)=4. Every replay product, accumulated sum and subtraction is zero or one of the stipulated normal values, hence is represented exactly by [F5]. Thus the transformed RHS is (1,4)T, and descending substitution with R=−I2 has zero off-diagonal products and exact divisions by −1, giving k1=(−1,−4)T. The midpoint is y+k1/2=(1/2,0)T, so F1=J(y+k1/2)=(−1/2,0)T; all these exact values are stored.

3.1givenF1F2F5step 2.1algebra

The second RHS is F1−k1=(1/2,4)T. The first stored reflector has dot 1 and active p=(1,0)T, so it sends the first coordinate to 1/2−1=−1/2 and leaves the second at 4. The second reflector has dot 8 and scalar p=1, so it sends the second coordinate to 4−8=−4. All replay products, sums and subtractions are again zero or stipulated normal represented values. Descending substitution with R=−I2 is exact as in step 2.1 and gives q2=(1/2,4)T. Hence k2=q2+k1=(−1/2,0)T.

4.1givenF1F2F4step 1.1step 2.1step 3.1algebra∎

For either solve, W=I2 makes v^=x and the computed residual zero, so the evaluator returns r‾=0. The other norm upper bounds and γ2=2u/(1−2u)<0.021 show the first certificate expression is at most 0.021⋅2⋅5+0.01⋅10=0.31<1/2, so the evaluator can soundly return W‾=1/2 and then ρ=1/2 for r‾+W‾. Thus both solves meet tolerance one. By [F1]–[F2], this successful call uses one factorization and two triangular solves.

Finite check

The stored matrix is I2, the two exact RHS values are (−1,−4)T and (1/2,4)T, and both residual norms are zero. Each residual allowance is less than 0.31, below the chosen tolerance one.

ExampleConstruction: AI-adaptedVerification: AI-adaptedOpen item page →

Rosenbrock–Euler damps a stiff scalar decay with the backward-Euler factor

Statement

Consider y′=−100y with y0=1 and one step h=1. The named Rosenbrock–Euler method (γ=1, exact Jacobian) returns y1=1/101, exactly the backward-Euler value. Its scalar factor is 1/(1+100); it lies in (0,1) and tends to zero as the decay parameter times the step tends to infinity. This is a scalar exact-arithmetic calculation and makes no finite-precision forward-error claim.

Facts & Assumptions

Given: f(y)=−100y, y0=1, h=1, and exact arithmetic.

[F1]

The Rosenbrock–Euler step map with an exact or approximate Jacobian: at γ=1, (1−hJ)k=f(y) and y+=y+hk.

[F2]

The Rosenbrock–Euler scalar stability function is A-stable exactly for gamma at least one half: R1(z)=1/(1−z) and the γ=1 method is L-stable.

[F3]

Autonomous Rosenbrock–Euler is one Newton correction of backward Euler: for an autonomous field, the γ=1 step is one Newton correction of the backward-Euler residual from the starting value.

[F4]

Backward Euler step equation and algebraic residual: the scalar endpoint equation is z=y+hf(z).

Proof

Given: The data in the Statement.

1.1givenF1algebra

Here J=−100 and z=h(−100)=−100. By [F1], k=(−100)/(1+100)=−100/101, hence y1=1−100/101=1/101.

2.1F3F4step 1.1algebra

The backward-Euler equation [F4] is z=1−100z, so 101z=1 and its unique scalar root is z=1/101. This agrees with step 1.1; [F3] also identifies the Rosenbrock update as one Newton correction, which is exact here because the residual is affine.

3.1F2givenalgebra∎

Since z=−100<0, the factor in [F2] is 1/101∈(0,1). For general decay parameter λ>0, the factor is 1/(1+hλ), which tends to zero as hλ→∞.

Finite check

For hλ=100, the method factor and backward-Euler factor both equal 1/101; explicit Euler would have factor 1−100=−99, which grows in magnitude rather than damping this mode.

CounterexampleConstruction: AI-adaptedVerification: AI-adaptedOpen item page →

A bounded approximate Jacobian can destroy Rosenbrock stiff damping

Statement

For the scalar family y′=−λy with λ>0 and y0=1, take h=4/λ, J~=0, and T~=0 in the exact-arithmetic stage equations of the two-stage method. Then W=1, its amplification factor is 5, and this approximate-J method amplifies the mode even though the exact-J method at z=−4 has modulus less than one. The zero approximation is bounded. This calculation demonstrates loss of stiff damping and makes no order or uniform-convergence claim.

Facts & Assumptions

Given: The scalar equation, λ>0, h=4/λ, and the stated approximate derivatives.

[F1]

Two-stage second-order Rosenbrock W-method with shared factorization: the stages satisfy Wk1=f+αhT~, F1=f(y+hk1/2) for this autonomous equation, Wq2=F1−k1, k2=q2+k1, and y+=y+hk2.

[F2]

The selected exact-J two-stage Rosenbrock method is scalar L-stable: for the exact Jacobian, R(z)=[1+(1−2α)z]/(1−αz)2 and it is L-stable.

Proof

Given: The data in the Statement.

1.1givenF1algebra

Here W=1 and k1=f(y)=−λy. Since hk1/2=−2y, the midpoint state is −y and F1=f(−y)=λy.

2.1step 1.1F1algebra

The second right-hand side is F1−k1=2λy, so q2=2λy and k2=q2+k1=λy. Therefore y+=y+hλy=5y, giving amplification factor 5.

3.1givenF2step 2.1algebra

For the exact-J branch, [F2] gives R(−4)=[1−4(1−2α)]/(1+4α)2=(5−42)/(5−22)2, because α=1−1/2. Since 25<32<36, we have 5<42<6, so ∣5−42∣<1; also 22<4, so 5−22>1. Thus ∣R(−4)∣<1. This differs from step 2.1 and proves that boundedness of J~ alone does not preserve stiff damping.

4.1step 1.1step 2.1algebra∎

The approximate-J calculation in step 2.1 and the exact-J factor comparison establish the stated damping failure. No order or convergence conclusion follows from this stiff-scaled calculation.

Finite check

With λ=4 and h=1, the approximate-J output is 5y, while the exact-J factor is (5−42)/(5−22)2≈−0.1393.

Sources