Alphabeta Math
Pipeline-generated
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

✓ 7 results · all verified · 6 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs. The 1 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Matrix exponential and phi function actions

1 · Prerequisites

2 · Summary

The page defines the entire phi family by its convergent series, proves its recurrence, and obtains every phi action (including the value at zero) from one augmented exponential. It develops the Euclidean logarithmic norm and a finite-matrix semigroup bound, then uses variation of constants to express exponential-action error as a weighted defect integral.

For scalable computation, the finite scaled Taylor routine chooses from finite scaling and degree budgets and reports success only when rational tail and provider-supplied roundoff bounds meet tolerance. Its proof gives the geometric Taylor tail, propagation across substeps, and exact matrix-vector call count. The Arnoldi routine uses the augmented identity for reduced phi actions, reports numerical breakdown separately from happy breakdown, and keeps matvec, orthogonalization, initial-vector, reduced-action, and final-projection errors explicit. The residual estimate is a finite-dimensional bound; it implies no mesh-uniform result.

The declared page interfaces include the Krylov, matrix norm, semigroup, batch 34 Krylov-methods, and batch 68 matrix-functions pages. The current batch 34 and batch 68 A-page bodies were inspected during authoring. The batch 68 page defines analytic primary matrix functions and a conditional Padé scaling-and-squaring route; this page defines the exponential by its power series and proves a separate scaled Taylor certificate, so no Padé or contour hypothesis is imported.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

The phi-function family by everywhere-convergent series

Definition

Use the first-variable-linear standard inner product on Cn and its norm ∥x∥2=∑i<n∣xi∣2 from The standard formulas ⟨x,y⟩=∑k<nxkyk on Rn and ∑k<nxkyk‾ on Cn are inner products and The norm ∥v∥=⟨v,v⟩ induced by a real or complex inner product. The matrix norm ∥X∥2 is the operator norm of the linear map x↦Xx in The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||. On C0={0} this operator norm is 0, as specified in that definition.

For each integer k≥0, define the scalar phi function and its finite-matrix action by

φk(z):=∑j=0∞zj(j+k)!,φk(X):=∑j=0∞Xj(j+k)!.

Here z∈C, X∈Mn(C), n≥0, X0=In, and factorials are the usual positive integer factorials. The scalar and matrix phi series converge absolutely ↗ establishes that both series converge absolutely for every such input, so these expressions define values on their full stated domains. By the published power-series definition The complex exponential by its power series, φ0(z)=exp⁡z; for matrices, φ0(X) is the bounded-operator exponential series of The exponential series of a bounded operator.

At zero, the series gives φk(0)=1/k! and φk(0n×n)=In/k! for every n≥0. For n≥1, only the j=0 matrix term can be nonzero. For n=0, The vector space Mm×n(F):=F m×n of m by n matrices over a field, with entrywise operations and Rectangular matrix multiplication and the identity matrix In, including zero-sized shapes identify I0 with the unique empty matrix 00×0; every power Xj and every partial sum is that matrix. Thus φk(00×0)=I0/k!=00×0, including k=0.

The recurrence is always interpreted as a series identity, including at zero; division by z is not part of this definition.

DefinitionDefinition: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

The Euclidean logarithmic norm of a complex matrix

Definition

Let n≥1 and A∈Mn(C). In the standard complex inner product from The standard formulas ⟨x,y⟩=∑k<nxkyk on Rn and ∑k<nxkyk‾ on Cn are inner products, the adjoint matrix is the conjugate transpose A∗ by In orthonormal bases, the matrix of the adjoint is the conjugate transpose of the matrix. The Hermitian part is HA:=(A+A∗)/2. It is self-adjoint in the sense of Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space.

Define the Euclidean logarithmic norm by μ2(A):=λmax⁡(HA), where λmax⁡ denotes the largest eigenvalue of the Hermitian matrix HA. The item The Euclidean logarithmic norm is a real Rayleigh-quotient maximum ↗ establishes that this eigenvalue exists, is real, and equals max⁡∥x∥2=1Re⁡⟨Ax,x⟩. This is a real growth bound, not a matrix norm.

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Normwise certified matrix-vector and vector-step arithmetic

Definition

Fix a finite complex matrix M∈Cr×s with r,s≥1 and the Euclidean norms from The norm ∥v∥=⟨v,v⟩ induced by a real or complex inner product. A normwise certified vector arithmetic provider supplies the following outputs together with the stated bounds:

  • For a real scalar c and z∈Cs, a scaled matvec output z^∈Cr and an exactly represented nonnegative rational upper bound α satisfying ∥z^−cMz∥2≤α. This includes square matrix-vector products and the final Arnoldi basis projection.
  • For a real scalar c and vector z∈Cs, a scaled-vector output q^∈Cs and an exactly represented nonnegative rational upper bound ξ satisfying ∥q^−cz∥2≤ξ.
  • For vectors x,z∈Cs, a sum output s^∈Cs and an exactly represented nonnegative rational upper bound ζ satisfying ∥s^−(x+z)∥2≤ζ.

The bounds are part of the provider's mathematical contract and must enclose the exact norm errors for the supplied inputs. A call without a sound bound is uncertified and can only lead to an inconclusive status. This definition does not assert that a particular floating-point kernel supplies the contract.

LemmaStatement: AI-adaptedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

Variation of constants for a finite-matrix action defect

Statement

Let n≥1, A∈Mn(C), T>0, v∈Cn, and u:[−T,T]→Cn be continuously differentiable, with one-sided derivatives at the endpoints. Define the continuous defect r(s):=Au(s)−u′(s). Then, for every t∈[−T,T],

etAv−u(t)=etA(v−u(0))+∫0te(t−s)Ar(s) ds.

The vector integral is the oriented componentwise Riemann integral after identifying Cn with R2n. In particular, if u(0)=v, the initial term vanishes.

Facts & Assumptions

Given: n≥1, a complex square matrix A, T>0, a vector v, and a continuously differentiable curve u:[−T,T]→Cn with defect r(s)=Au(s)−u′(s).

[F1]

The exponential series of a bounded operator: for a bounded operator A, E(t):=etA is differentiable, E′(t)=AE(t)=E(t)A, and E(t)E(s)=E(t+s).

[F2]

The derivative and the Riemann integral of a vector-valued function: an intrinsic derivative and a componentwise integral: derivatives and integrals of finite-dimensional real vector-valued curves are taken componentwise.

[F3]

If f:[a,b]→Rm is differentiable with integrable f′ then ∫abf′=f(b)−f(a); and a bounded derivative makes f Lipschitz: if a real vector-valued curve is differentiable on a compact interval and its derivative is integrable, the integral of the derivative equals the endpoint difference.

[F4]

C is the real coordinate plane, with coordinate arithmetic and The standard formulas ⟨x,y⟩=∑k<nxkyk on Rn and ∑k<nxkyk‾ on Cn are inner products: real and imaginary coordinates identify Cn with a finite-dimensional real vector space, and the standard inner product is given coordinatewise.

[F5]

Every finite-dimensional normed space is Banach: the finite-dimensional complex-inner-product space Cn is complete.

[F6]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: for a linear map between finite-dimensional inner-product spaces, its operator norm is the maximum of ∥Ax∥2 over unit vectors.

[F7]

A continuous function on [a,b] is Riemann integrable, by Heine-Cantor and Riemann's criterion: every continuous real-valued function on a nondegenerate closed bounded interval is Riemann integrable.

Proof

technique · direct
1.1givenF1F5F6algebra

Set x(t):=etAv−u(t). If y≠0, rescaling y/∥y∥2 in [F6] gives ∥Ay∥2≤∥A∥2∥y∥2, while the same inequality is immediate for y=0; thus A is bounded. By [F1] and [F5], the exponential series applies on the complete space Cn. It gives x′(t)=AetAv−u′(t)=A(x(t)+u(t))−u′(t)=Ax(t)+r(t), and x(0)=v−u(0).

2.1step 1.1F1algebra

Define g(t):=e−tAx(t). Differentiating the product and using that A commutes with every power in its exponential series gives g′(t)=−Ae−tAx(t)+e−tA(Ax(t)+r(t))=e−tAr(t).

3.1step 2.1F2F3F4F7

By [F2] and [F4], g is a real finite-dimensional vector-valued curve with continuous derivative. Each coordinate of g′ is continuous and therefore Riemann integrable on every nondegenerate closed subinterval by [F7]. If t=0, then g(t)=g(0) and the oriented integral is zero. If t>0, [F3] on [0,t] gives g(t)=g(0)+∫0te−sAr(s) ds. If t<0, [F3] on [t,0] gives g(0)−g(t)=∫t0e−sAr(s) ds, which is the same oriented identity after reversing the bounds.

4.1step 3.1F1F2algebra∎

Multiply this equality by etA. Matrix multiplication is a fixed finite-dimensional linear map and therefore commutes with the componentwise integral; [F1] gives etAg(t)=x(t), etAg(0)=etA(v−u(0)), and etAe−sA=e(t−s)A. Substitution yields the stated identity.

LemmaStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

The scalar and matrix phi series converge absolutely

Statement

For every integer k≥0, the scalar series defining φk(z) converges absolutely for every z∈C, and the matrix series defining φk(X) converges absolutely in operator 2-norm for every X∈Mn(C), n≥0. At zero, φk(0)=1/k! and φk(0n×n)=In/k!.

Facts & Assumptions

Given: k≥0, z∈C, X∈Mn(C) with n≥0, and the series definitions of The phi-function family by everywhere-convergent series.

[F1]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: ∥X∥2=0 on C0; for n≥1, ∥X∥2=max⁡∥x∥2=1∥Xx∥2 for a linear endomorphism of Cn.

[F2]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: ∥YZ∥2≤∥Y∥2∥Z∥2 for compatible finite-dimensional maps.

[F3]

Every finite-dimensional normed space is Banach: every finite-dimensional normed space over R or C is complete.

Proof

technique · direct
1.1givenF4algebra

Put R:=∣z∣. Since (j+k)!≥j! for every j≥0, ∣zj/(j+k)!∣≤Rj/j!; the majorant sums to eR by [F4]. Thus the scalar series is absolutely convergent.

1.2givenF1F2F4

If n=0, the series definition gives Xj=I0=00×0 for every j≥0. Every term has norm 0 by [F1], so the matrix series is absolutely convergent and every partial sum equals the unique empty matrix. For n≥1, put R:=∥X∥2. The operator norm is defined by [F1]. Repeated submultiplicativity in [F2] gives ∥Xj∥2≤Rj, so ∥Xj/(j+k)!∥2≤Rj/j! for every j. The norms therefore have a finite sum by [F4].

2.1givenstep 1.2F3algebra

For n≥1, let SN be the partial sums of the matrix series. For N>M, the triangle inequality bounds ∥SN−SM∥2 by the tail of the convergent scalar majorant in step 1.2, so (SN) is Cauchy. The matrix space is finite-dimensional and complete by [F3], hence (SN) converges in operator norm. For n=0, convergence was established directly in step 1.2.

3.1givenstep 1.1step 1.2step 2.1algebra∎

At z=0 only the j=0 term of the scalar series is nonzero, and at X=0 with n≥1 only the j=0 matrix term is nonzero; these terms are respectively 1/k! and In/k!. When n=0, every matrix term and I0/k! is the unique empty matrix by step 1.2. Hence both stated zero values follow for every allowed size.

LemmaStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

The Euclidean logarithmic norm is a real Rayleigh-quotient maximum

Statement

For n≥1 and A∈Mn(C), HA=(A+A∗)/2 is Hermitian, its eigenvalues are real, and

μ2(A)=λmax⁡(HA)=max⁡∥x∥2=1Re⁡⟨Ax,x⟩.

Facts & Assumptions

Given: n≥1, A∈Mn(C), and HA=(A+A∗)/2 as in The Euclidean logarithmic norm of a complex matrix.

[F1]

The standard formulas ⟨x,y⟩=∑k<nxkyk on Rn and ∑k<nxkyk‾ on Cn are inner products: ⟨x,y⟩=∑i<nxiyi‾ is the standard complex inner product, linear in the first variable.

[F2]

In orthonormal bases, the matrix of the adjoint is the conjugate transpose of the matrix: in orthonormal coordinates, the matrix of the adjoint is the conjugate transpose.

[F3]

Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space: a self-adjoint endomorphism satisfies H∗=H and is normal.

[F4]

Complex spectral theorem: a normal endomorphism of a finite-dimensional complex inner product space has an orthonormal eigenbasis, and conversely: every normal endomorphism of a finite-dimensional complex inner-product space has an orthonormal eigenbasis.

[F5]

The Euclidean logarithmic norm of a complex matrix: μ2(A)=λmax⁡(HA) for HA=(A+A∗)/2.

Proof

technique · direct
1.1givenF2F3algebra

By [F2], HA∗=(A∗+A)/2=HA, so HA is self-adjoint and hence normal by [F3].

2.1step 1.1F1F3F4choosealgebra

By [F4], choose an orthonormal eigenbasis u1,…,un for HA, with HAuj=λjuj. Since λj=⟨HAuj,uj⟩ and self-adjointness gives ⟨HAuj,uj⟩=⟨uj,HAuj⟩=⟨HAuj,uj⟩‾, each eigenvalue λj is real; the finite set has a largest member.

3.1givenstep 2.1F5

By [F5], μ2(A)=λmax⁡(HA)=max⁡jλj.

4.1step 2.1step 3.1F1F2algebra∎

For x=∑j=1ncjuj, [F1] and orthonormality give ⟨HAx,x⟩=∑jλj∣cj∣2≤μ2(A)∑j∣cj∣2=μ2(A)∥x∥22. Also ⟨HAx,x⟩=Re⁡⟨Ax,x⟩ because ⟨A∗x,x⟩=⟨Ax,x⟩‾. A unit eigenvector for the largest λj attains equality, proving the maximum formula.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableOpen item page →

Finite scaled Taylor action for the matrix exponential

Definition

Inputs: A∈Mn(C) with n≥1, a vector v∈Cn, an exactly represented finite time t∈R, positive rational tolerance ε, integer budgets smax⁡≥1 and pmax⁡≥0, rational upper bounds ρA≥∥A∥2, ρt≥∣t∣, and ρv≥∥v∥2, and a provider satisfying Normwise certified matrix-vector and vector-step arithmetic.

For s≥1 define the rational Rs:=ρtρA/s, which upper-bounds ∣t∣∥A∥2/s. For rational 0≤R≤1 and integer p≥0, define

Qp(R):=Rp+1(p+1)!(1−R/(p+2)).

Outputs: a vector candidate when returned, an extended nonnegative rational error bound in Q≥0∪{+∞} (+∞ means no finite certificate was established), the number of calls to A, and one status: success, inconclusive-tail-budget, inconclusive-roundoff, or invalid-input.

Procedure:

  1. Reject invalid dimensions, nonpositive tolerance, negative bounds, or invalid budgets by returning no candidate, bound +∞, zero calls to A, and invalid-input. If v=0, t=0, or ρA=0, return v, bound 0, zero calls to A, and status success. In the last case ∥A∥2=0 forces A=0.
  2. Among pairs 1≤s≤smax⁡ and 0≤p≤pmax⁡ with Rs≤1, select one minimizing sp subject to s 3s−1Qp(Rs)ρv≤ε/2. Break ties by smaller s, then smaller p. If no pair exists, return the zero vector candidate, bound +∞, and inconclusive-tail-budget.
  3. Set δ:=t/s and w0:=v. For each substep ℓ=1,…,s, initialize z0:=wℓ−1 and a0:=wℓ−1. For j=1,…,p, call the certified provider with coefficient c=δ/j and vector zj−1 to obtain zj and a bound αℓj for ∥zj−(δ/j)Azj−1∥2. Add zj to the partial sum through the provider, obtaining aj and a bound ζℓj for ∥aj−(aj−1+zj)∥2. Set dℓ0:=0 and compute dℓj:=(Rs/j)dℓ,j−1+αℓj. Set wℓ:=ap and ϵℓ:=∑j=1p(dℓj+ζℓj); for p=0, set wℓ:=wℓ−1 and ϵℓ:=0.
  4. If any provider result lacks a sound bound, return the current candidate, bound +∞, and inconclusive-roundoff. Otherwise compute B:=s 3s−1Qp(Rs)ρv+∑ℓ=1s3s−ℓϵℓ. Return ws, bound B, and success if B≤ε; otherwise return the same candidate and bound with inconclusive-roundoff.

The pair search is finite. The recurrence makes exactly sp calls to A when it reaches step 4; the error-bound provider performs no additional A calls. An implementation may return an inconclusive status whenever an outward scalar comparison or a local error certificate cannot be established.

Well-definedness and guarantee boundary

There are at most smax⁡(pmax⁡+1) pairs in the search, and for every admissible R∈[0,1] and p≥0 the denominator 1−R/(p+2) is at least 1/2, so each Qp(R) is defined. Every matrix-vector and partial-sum loop has a finite bound. The zero branches are exact: v=0 gives the zero action, t=0 gives e0Av=v, and ρA=0 forces A=0. A failed local certificate stops with inconclusive-roundoff; failure to find a pair within the declared budgets returns inconclusive-tail-budget. Neither status asserts an error guarantee.

The separate error/work theorem on this page proves the reported bound under the stated rational input bounds and successful local provider certificates. Finite budgets need not return success; no convergence claim beyond this conditional finite certificate is made. The count sp is the exact matrix-vector call count for a completed recurrence, not a claim about wall-clock cost.

TheoremStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

Phi recurrence and augmented-matrix action identities

Statement

For every integer k≥0, scalar z∈C, and square matrix X∈Mn(C) with n≥0, the series-defined functions of The phi-function family by everywhere-convergent series satisfy

φk(z)=1k!+zφk+1(z),φk(X)=1k!In+Xφk+1(X).

For k≥1 and w∈Cn, let Jk∈Mk(C) have ones on the superdiagonal and zeros elsewhere, and let e1,ek∈Ck be the first and last coordinate vectors. Define Bk(X,w):=[Xwe1∗0Jk]. Then the first n entries of eBk(X,w)[0ek] equal φk(X)w. The identity is valid at X=0 and requires no division by X or by a scalar argument.

Facts & Assumptions

Given: k≥0, z∈C, X∈Mn(C) with n≥0, and w∈Cn when k≥1.

[F1]

The phi-function family by everywhere-convergent series: φk(z)=∑j≥0zj/(j+k)! and φk(X)=∑j≥0Xj/(j+k)!.

[F2]

The scalar and matrix phi series converge absolutely: these scalar and matrix series converge absolutely, so their tails may be reindexed and their matrix-exponential blocks may be read termwise.

Proof

technique · direct
1.1F1F2algebra

Absolute convergence in [F2] permits an index shift: zφk+1(z)=∑j≥0zj+1/(j+k+1)!=∑r≥1zr/(r+k)!=φk(z)−1/k!. Rearranging proves the scalar recurrence, including z=0.

1.2F1F2algebra

For n=0, every matrix term is the unique empty matrix by [F1], so the matrix recurrence is the equality of that matrix with itself. For n≥1, the same index shift is valid in operator norm by [F2]; left multiplication by X sends the (j+k+1)! denominator term to Xj+1/(j+k+1)!. Thus Xφk+1(X)=φk(X)−In/k!, proving the matrix recurrence.

1.3givenalgebra

For n≥1, in Bk(X,w), a block-product path from the first block row to the last column of the last block must move through the we1∗ block and then through all k−1 superdiagonal entries of Jk. It uses exactly k block transitions; any remaining factors can only be copies of X before that first transition. Hence the last column of the top-right block of Bkr is zero for r<k, and is Xr−kw for r≥k.

2.1step 1.3F1F2algebra∎

For n≥1, by [F2] with k=0, eBk=φ0(Bk)=∑r≥0Bkr/r!. By step 1.3, the last column of its top-right block is ∑r≥kXr−kw/r!=∑j≥0Xjw/(j+k)!=φk(X)w. This is exactly the first n entries obtained by applying eBk to the last coordinate vector. When n=0, w is the unique vector in C0 and Bk(X,w)=Jk; the first zero entries of eJkek and φk(X)w are both the empty vector. Thus the augmented identity also holds in that case.

LemmaStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

The logarithmic norm bounds forward and reverse matrix exponentials

Statement

For A∈Mn(C), n≥1, and τ≥0,

∥eτA∥2≤eτμ2(A).

Consequently, for t≤0, ∥etA∥2≤e∣t∣μ2(−A). No normality, spectral stability, or sign assumption on μ2(A) is required.

Facts & Assumptions

Given: A∈Mn(C), n≥1, and τ≥0.

[F1]

The Euclidean logarithmic norm is a real Rayleigh-quotient maximum: Re⁡⟨Ax,x⟩≤μ2(A)∥x∥22 for all x∈Cn, and μ2(A)∈R.

[F2]

The exponential series of a bounded operator: on a Banach space, y(t)=etAx is differentiable with y′(t)=Ay(t).

[F3]

Every finite-dimensional normed space is Banach: the finite-dimensional normed space Cn is complete.

[F4]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: ∥T∥2=max⁡∥x∥2=1∥Tx∥2 for a finite-dimensional endomorphism.

[F5]

The exponential function is smooth and (exp⁡)′=exp⁡: the scalar real exponential satisfies (ex)′=ex.

Proof

technique · direct
1.1givenF2F3algebra

Fix x∈Cn and set y(t):=etAx and q(t):=∥y(t)∥22. By [F2] and [F3], y′(t)=Ay(t). If Δh:=y(t+h)−y(t), differentiability gives Δh/h→Ay(t); expanding the inner product gives q(t+h)−q(t)=2Re⁡⟨Δh,y(t)⟩+∥Δh∥22, so q′(t)=2Re⁡⟨Ay(t),y(t)⟩.

2.1step 1.1F1F5algebra

By [F1], q′(t)≤2μ2(A)q(t). Put a(t):=e−2μ2(A)t and g(t):=a(t)q(t). By [F5], a′(t)=−2μ2(A)a(t): when μ2(A)≠0, write its difference quotient as (−2μ2(A))e−2μ2(A)t(e−2μ2(A)h−1)/(−2μ2(A)h) and use (er)′∣r=0=1; when μ2(A)=0, a is constant. The identity a(t+h)q(t+h)−a(t)q(t)h=a(t+h)q(t+h)−q(t)h+q(t)a(t+h)−a(t)h and the resulting limits give g′(t)=a(t)(q′(t)−2μ2(A)q(t))≤0.

3.1step 2.1F6F7algebra

If t=0, the desired vector inequality is equality. If t>0, [F6] applied to g on [0,t] and step 2.1 give g(t)≤g(0)=∥x∥22. By [F7], e2μ2(A)t>0; multiplying and taking nonnegative square roots yields ∥etAx∥2≤etμ2(A)∥x∥2.

4.1step 3.1F2F4algebra∎

Taking the maximum over unit vectors x and using [F4] proves ∥eτA∥2≤eτμ2(A). For t≤0, apply the proved inequality to −A at time ∣t∣; termwise equality of the exponential series gives e∣t∣(−A)=etA, proving the reverse-time claim.

TheoremStatement: Literature-sourcedProof: AI-generatedprecheck passOpen item page →

Scaled Taylor action has a finite normwise truncation and roundoff certificate

Statement

Let A∈Mn(C), n≥1, v∈Cn, t∈R, and choose s≥1, p≥0, and rational R∈[0,1] with ∣t∣∥A∥2/s≤R. Put δ=t/s, P:=∑j=0p(δA)j/j!, and

Qp(R):=Rp+1(p+1)!(1−R/(p+2)).

Suppose each computed substep w^ℓ has local term and summation certificates αℓj,ζℓj≥0 as in Finite scaled Taylor action for the matrix exponential, and define dℓ0:=0, dℓj:=(R/j)dℓ,j−1+αℓj, and ϵℓ:=∑j=1p(dℓj+ζℓj) (empty sums are zero). Then

∥etAv−w^s∥2≤s 3s−1Qp(R)∥v∥2+∑ℓ=1s3s−ℓϵℓ.

If the algorithm has rational upper bound ρv≥∥v∥2 and returns success, its reported certificate is valid and at most the requested tolerance. A completed recurrence uses exactly sp calls to A. A return before the recurrence makes zero such calls; an early stop during it makes at most sp calls.

Facts & Assumptions

Given: A finite complex matrix A, vector v, real time t, integers s≥1,p≥0, a rational upper bound R∈[0,1] for ∣t∣∥A∥2/s, and successful local arithmetic certificates as specified by Finite scaled Taylor action for the matrix exponential.

[F1]
[F2]

The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: the operator 2-norm is submultiplicative and homogeneous.

[F3]

The exponential series of a bounded operator: the matrix exponential satisfies etAesA=e(t+s)A.

[F4]

Finite scaled Taylor action for the matrix exponential: each computed Taylor term and partial sum has the supplied local normwise error certificate, and the loop performs one A call for each pair (ℓ,j).

Proof

technique · direct
1.1givenF1F2algebra

Let R∗:=∣δ∣∥A∥2≤R. For j≥p+1, consecutive scalar tail terms have ratio R∗/(j+1)≤R/(p+2)<1. The tail is therefore bounded by the geometric series beginning at Rp+1/(p+1)!, giving ∥eδA−P∥2≤∑j≥p+1Rj/j!≤Qp(R).

1.2F1F2algebra

For 0≤R≤1, eR=∑j≥0Rj/j!≤∑j≥01/j!≤3: for j≥1, the j−1 factors 2,…,j in j! are each at least 2, so j!≥2j−1 and ∑j≥12−(j−1)=2. Thus ∥eδA∥2≤3 and ∥P∥2≤∑j=0pRj/j!≤3.

1.3givenF2F4algebrainduction

Fix substep ℓ and let zℓ0∗:=w^ℓ−1 and zℓj∗:=(δ/j)Azℓ,j−1∗. The local provider certificate gives ∥zℓj−zℓj∗∥2≤(R/j)∥zℓ,j−1−zℓ,j−1∗∥2+αℓj; induction on j yields the bound dℓj. The partial-sum certificate adds at most ζℓj at stage j, so ∥w^ℓ−Pw^ℓ−1∥2≤ϵℓ.

2.1step 1.1step 1.2F2F3algebra

Write X:=eδA. The algebraic telescoping identity Xs−Ps=∑j=0s−1Xs−1−j(X−P)Pj holds by cancellation of adjacent terms. By step 1.2 every summand has norm at most 3s−1Qp(R), so ∥Xsv−Psv∥2≤s3s−1Qp(R)∥v∥2. By [F3], Xs=etA.

2.2step 1.2step 1.3algebra

Set eℓ:=w^ℓ−Pw^ℓ−1. Repeated substitution gives w^s−Psv=∑ℓ=1sPs−ℓeℓ. By step 1.2, ∥Ps−ℓ∥2≤3s−ℓ, hence ∥w^s−Psv∥2≤∑ℓ=1s3s−ℓϵℓ.

3.1step 2.1step 2.2F4algebra

The triangle inequality applied to steps 2.1 and 2.2 gives the stated total error certificate. The algorithm declares success only when its outward rational bound is at most the tolerance.

4.1givenF4∎

If the recurrence completes, its nested loops make one call to A for each 1≤ℓ≤s and 1≤j≤p, giving exactly sp calls, including zero when p=0. No other step calls A, so a return before the recurrence makes zero calls and an early stop during it makes at most sp calls.

Remarks

The certificate is for the specified finite matrix, time, budgets, norm bounds, and local arithmetic certificates. It asserts no mesh-uniform estimate from finite-dimensional exactness and returns an inconclusive status if any bound cannot be certified.

AlgorithmAlgorithm: AI-adaptedProof: Not applicableOpen item page →

Arnoldi projection for exponential and phi-function actions

Definition

Inputs: A∈Mn(C) with n≥1, v∈Cn, time t∈R, integer k≥0, Krylov budget 1≤m≤n, nonnegative threshold τ, an exact-arithmetic Arnoldi run or a finite-precision Arnoldi backend, and a reduced-action configuration. In finite precision, the backend returns a length j with 1≤j≤m, β>0 for v≠0, Vj∈Cn×j, Hj∈Mj(C), and a computed subdiagonal; it may supply the relation certificate in Step 2 and an exact happy-breakdown certificate verifying the full Arnoldi recurrence from v through j steps with zero terminal residual. The reduced-action configuration supplies, for each Taylor call, an exactly represented call time, a rational upper bound on its absolute value, rational operator-norm and input-vector norm upper bounds, a positive rational tolerance, finite scaling/order budgets, and a provider satisfying Normwise certified matrix-vector and vector-step arithmetic for Taylor calls, scalar scaling, and final projection. Missing or invalid bounds produce an inconclusive status.

Outputs: a vector candidate when data are available, reduced-coefficient error bound ϵred∈R≥0∪{+∞}, final-projection error bound ηproj∈Q≥0∪{+∞}, Arnoldi data, any relation-error certificates, arnoldi_status (zero-start, happy-breakdown, projected, numerical-breakdown, inconclusive-arnoldi, or invalid-input), relation_status (exact, certified, inconclusive, or not-run), reduced_action_status (success, inconclusive, or not-run), and projection_status (exact, certified, inconclusive, or not-run). An invalid input may return no candidate.

Procedure:

  1. Reject invalid dimensions, m, k, or threshold by returning no candidate, both error bounds +∞, no Arnoldi data or relation certificates, arnoldi_status=invalid-input, and relation_status=reduced_action_status=projection_status=not-run. If v=0, return the zero vector with both error bounds zero, arnoldi_status=zero-start, and relation_status=reduced_action_status=projection_status=not-run, without division.
  2. In exact-arithmetic mode, run The Arnoldi process for building an orthonormal basis of a Krylov subspace for at most m steps and retain β:=∥v∥2, Vj=[v1,…,vj], and the leading square Hessenberg matrix Hj; set relation_status=exact. Stop when the Arnoldi residual norm is exactly zero and set arnoldi_status=happy-breakdown; otherwise continue until m steps and leave the Arnoldi status pending. In finite-precision mode, if the backend data have invalid dimensions or fail β>0 for v≠0, return the zero candidate, both bounds +∞, arnoldi_status=inconclusive-arnoldi, relation_status=inconclusive, and reduced_action_status=projection_status=not-run. Otherwise use the backend's Vj,Hj,β and computed subdiagonal. When available, it supplies a relation certificate AVj=VjHj+hj+1,jvj+1ej∗+D+Ω, with ∥vj+1∥2=1, column bounds ∥di∥2≤ηi for D=[d1,…,dj], and a norm bound for Ω. Set relation_status=certified if this relation is supplied and verified, and relation_status=inconclusive otherwise. A verified exact happy-breakdown certificate sets relation_status=certified and arnoldi_status=happy-breakdown; it verifies the exact Arnoldi recurrence, including AVj=VjHj and v=βVje1. Without that full certificate, a computed subdiagonal at most τ sets arnoldi_status=numerical-breakdown; continue to the reduced evaluation but never label it happy breakdown. Otherwise leave the Arnoldi status pending until Step 5.
  3. For k=0, request a reduced configuration for (Hj,βe1,t). If it is missing or invalid, including if it cannot supply the time t exactly and a rational upper bound on ∣t∣, treat the Taylor request as returning no candidate without calling it. Otherwise call Finite scaled Taylor action for the matrix exponential to approximate etHjβe1. If no reduced candidate is available, set ϵred=ηproj=+∞, set reduced_action_status=inconclusive and projection_status=not-run, set a pending arnoldi_status to inconclusive-arnoldi when relation_status=inconclusive and to projected otherwise, and return no candidate. Otherwise set c^ and ϵred to its candidate and bound. Set reduced_action_status=success only when the Taylor status is success; otherwise set it to inconclusive. Continue to Step 5 with the returned candidate in either case.
  4. For k≥1, form Bk(tHj,e1) from Phi recurrence and augmented-matrix action identities, request a reduced configuration for (Bk(tHj,e1),ej+k,1). If it is missing or invalid, treat the Taylor request as returning no candidate without calling it. Otherwise call Finite scaled Taylor action for the matrix exponential with the exactly represented time 1 and rational time bound 1. If no reduced candidate is available, set ϵred=ηproj=+∞, set reduced_action_status=inconclusive and projection_status=not-run, set a pending arnoldi_status to inconclusive-arnoldi when relation_status=inconclusive and to projected otherwise, and return no candidate. Otherwise let q be the first j entries of its candidate and use the certified vector-scaling provider with scalar β to obtain c^ and local bound ξβ. Set ϵred:=βϵaug+ξβ, where ϵaug is the augmented Taylor bound; if either certificate is missing set ϵred=+∞ and reduced_action_status=inconclusive. Set reduced_action_status=success when the Taylor status is success and the scaling certificate is sound; otherwise set it to inconclusive. Continue to Step 5 in either case.
  5. In exact-arithmetic mode set x:=Vjc^, ηproj=0, and projection_status=exact. In finite precision, use the certified matrix-vector provider with matrix Vj, scalar 1, and vector c^ to return x and bound ηproj; a sound certificate sets projection_status=certified, and missing certification sets ηproj=+∞ and projection_status=inconclusive. Return x. If arnoldi_status is still pending, set it to inconclusive-arnoldi when relation_status=inconclusive and projected otherwise.

In exact arithmetic the projected formula is βVjφk(tHj)e1. Exact happy breakdown means the generated Krylov subspace is invariant and this projected formula equals φk(tA)v; a finite Taylor evaluation still has its separately reported reduced-action error. A numerical-breakdown status asserts no exact invariance.

Well-definedness and status semantics

Input validation precedes the norm division. The zero-start branch returns immediately, and in the exact The Arnoldi process for building an orthonormal basis of a Krylov subspace every basis normalization occurs only after a nonzero residual norm has been established. The Arnoldi loop stops after at most m steps. The finite-precision backend must supply a finite basis and reduced matrix; absent a verified relation, relation_status=inconclusive preserves that gap. Each reduced action delegates to the finite-budget Finite scaled Taylor action for the matrix exponential, whose missing-bound and exhausted-budget outcomes are returned as inconclusive.

happy-breakdown is assigned only in exact arithmetic after a zero Arnoldi residual, or from a verified exact Arnoldi-recurrence certificate with zero terminal residual. A small computed subdiagonal without that full certificate is numerical-breakdown; it makes no claim about whether the true subspace is invariant. The four statuses separately report Arnoldi breakdown evidence, relation certification, reduced-action certification, and final-projection certification. For k=0, the local action-error theorem adds the reduced-coefficient and final-projection error bounds under its hypotheses. For k≥1, the local Taylor theorem certifies the augmented reduced action, and the vector-scaling provider certifies multiplication by β.

TheoremStatement: Literature-sourcedProof: AI-generatedprecheck passjudge pass (gpt-6-luna)Open item page →

Arnoldi action error is the semigroup-weighted residual integral

Statement

Let A∈Mn(C), n≥1, v∈Cn, β≥0, V∈Mn×m(C), H∈Mm(C), and 1≤m≤n. For exact Arnoldi, β=∥v∥2; in a certified finite-precision run, β may be the backend's returned nonnegative scalar, with its effect included in the initial mismatch. Put c(s):=esHβe1 and u(s):=Vc(s). For every real t, the exact defect identity is

etAv−u(t)=etA(v−βVe1)+∫0te(t−s)A(AV−VH)c(s) ds,

where the integral is oriented. Let It=[0,t] for t≥0 and It=[t,0] for t<0; let σt=1 for t≥0 and σt=−1 for t<0. If t=0 the integral is zero. Suppose the Arnoldi relation is

AV=VH+hvm+1em∗+D+Ω,

where h≥0, ∥vm+1∥2=1 when h>0, D=[d1,…,dm], ηj≥0, ∥dj∥2≤ηj, ηorth≥0, and ∥Ω∥2≤ηorth. In exact happy breakdown, interpret the rank-one term as zero and set D=Ω=0. If a reduced computation returns c^(t) with ∥c^(t)−c(t)∥2≤ϵred(t) for ϵred(t)≥0 and its final projection returns x^ with ∥x^−Vc^(t)∥2≤ηproj for ηproj≥0, then

∥etAv−x^∥2≤e∣t∣μ2(σtA)∥v−βVe1∥2+∫Ite∣t−s∣μ2(σtA)(h∣em∗c(s)∣+(∑j=1mηj2+ηorth)∥c(s)∥2)ds+∥V∥2ϵred(t)+ηproj.

For exact Arnoldi from a nonzero start with no breakdown through m, D=Ω=0, V has orthonormal columns, and v=βVe1, recovering the standard residual integral. Whenever AV=VH and v=βVe1, the invariant-subspace relation gives φk(tA)v=βVφk(tH)e1 for every k≥0 and every real t; exact happy breakdown of Arnoldi from a nonzero start is a sufficient case.

Facts & Assumptions

Given: The matrices and vectors in the Statement; for the numerical relation clause, the stated decomposition and columnwise error bounds; for the reduced term, a certified bound ϵred(t) and a final projection certificate ∥x^−Vc^(t)∥2≤ηproj.

[F1]

Variation of constants for a finite-matrix action defect: for a continuously differentiable approximate trajectory u, the exact action error is its initial error plus the oriented integral of e(t−s)A(Au−u′).

[F2]

The logarithmic norm bounds forward and reverse matrix exponentials: for τ≥0, ∥eτB∥2≤eτμ2(B) for every finite complex matrix B.

[F3]

Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization: before breakdown, Arnoldi columns are orthonormal and AVm=VmHm+hm+1,mvm+1em∗.

[F4]

The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: for a finite-dimensional linear map T, ∥T∥2=max⁡∥x∥2=1∥Tx∥2; rescaling gives ∥Tx∥2≤∥T∥2∥x∥2 for all x.

[F5]

Cauchy–Schwarz: ∣⟨u,v⟩∣≤∥u∥∥v∥, with equality exactly for linearly dependent vectors: ∣⟨x,y⟩∣≤∥x∥2∥y∥2 in a complex Euclidean space.

[F6]

The Arnoldi process for building an orthonormal basis of a Krylov subspace: Arnoldi starts with v1=v/∥v∥2, defines wj=Avj−∑i=1jhijvi, and exact breakdown at step j means hj+1,j=∥wj∥2=0.

[F7]

The exponential series of a bounded operator: for a bounded operator on a Banach space, its exponential is differentiable with derivative AetA.

[F8]

The phi-function family by everywhere-convergent series and The scalar and matrix phi series converge absolutely: every finite-matrix phi function is defined by an absolutely convergent power series.

[F9]

Every finite-dimensional normed space is Banach: each finite-dimensional normed space over C is complete.

[A1]

For a finite-precision relation, AV=VH+hvm+1em∗+D+Ω and all displayed norm bounds are certified data; the theorem does not infer them from a small computed subdiagonal.

[A2]

The final computed projection satisfies ∥x^−Vc^(t)∥2≤ηproj for a nonnegative bound ηproj, with ηproj=0 when the projection is exact.

Proof

technique · direct
1.1givenF4F7F9algebra

For any nonzero x, rescaling x/∥x∥2 in [F4] shows that the finite matrix H is bounded; the inequality is immediate at x=0. By [F9], Cm is complete, so [F7] applies and gives c′(s)=Hc(s). Hence u(s)=Vc(s) is continuously differentiable and its defect is Au(s)−u′(s)=(AV−VH)c(s).

1.2givenF2algebra

For s∈It, ∣t−s∣≥0 and e(t−s)A=e∣t−s∣σtA; the same identity gives etA=e∣t∣σtA. Thus [F2] bounds the initial factor by e∣t∣μ2(σtA) and the integral kernel by e∣t−s∣μ2(σtA).

2.1step 1.1F1

Apply [F1] to this u. It gives the displayed exact integral identity, including the initial mismatch v−u(0)=v−βVe1.

3.1givenA1F4F5step 2.1step 1.2algebra

Substitute the certified relation [A1] into the defect in step 2.1. The rank-one term has norm h∣em∗c(s)∣ because ∥vm+1∥2=1. For any unit vector x∈Cm, Cauchy–Schwarz [F5] gives ∥Dx∥2≤∑j∣xj∣ηj≤(∑jηj2)1/2; hence ∥D∥2≤(∑jηj2)1/2. The remaining term is at most ηorth∥c(s)∥2. Applying the kernel bound of step 1.2 inside the integral proves the first two terms of the claimed estimate.

3.2givenF6F8step 2.1algebrainduction

Under the exact invariant-subspace hypotheses AV=VH and v=βVe1 in the Statement, induction gives AjV=VHj for every j≥0: it holds at j=0, and left multiplication by A gives Aj+1V=AVHj=VHHj=VHj+1. Applying the convergent series [F8] term by term yields φk(tA)v=βVφk(tH)e1 for every k≥0. To verify the stated exact Arnoldi case, each earlier column satisfies Avℓ=∑i=1ℓ+1hiℓvi by the recurrence, while the last satisfies Avm=∑i=1mhimvi because [F6] gives wm=0; hence AV=VH, and the initialization in [F6] gives v=βVe1. This proves exact invariant-space projection for every phi action; the separate ϵred(t) hypothesis in this theorem concerns the exponential trajectory c(s).

4.1givenA2F4step 3.1algebra

The reduced-action error satisfies ∥V(c^(t)−c(t))∥2≤∥V∥2ϵred(t) by [F4]. By [A2] and the triangle inequality, replacing Vc^(t) by x^ adds at most ηproj. Adding both errors to the exact projected-action error from step 3.1 proves the claimed estimate.

5.1givenF3F6algebra∎

In exact Arnoldi without breakdown, [F3] supplies the orthonormal basis and the relation AV=VH+hvm+1em∗, so D=Ω=0; [F6] gives v=βVe1. Orthonormality gives ∥V∥2=1, and an exact reduced action has ϵred=0. Substitution in the proved identity and bound leaves exactly the usual initial-zero residual integral and its semigroup-weighted estimate.

Remarks

The residual integral is exact; its upper bound is conditional on the displayed semigroup, matvec, orthogonalization, initial-vector, reduced-action, and final-projection data. A near-zero computed Arnoldi residual without an exact-zero or relation certificate never establishes happy breakdown. No mesh-uniform estimate follows automatically from this finite-dimensional result.

5 · Examples, counterexamples and false statements

None yet.

Sources