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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Matrix exponential and phi function actions
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Bounded Linear Operators and Quotient Spaces
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Complex Norms and Realification
- Complexification, Realification and Real Structures
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalue Iterations and the QR Algorithm
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Finite Dimensional Normed Spaces and Riesz Lemma
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Krylov Subspaces, Arnoldi and GMRES
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Normed and Banach Spaces
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Strongly Continuous Semigroups and Hille Yosida
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Complex Exponential and Euler's Formula
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
The phi-function family by everywhere-convergent series
Definition
Use the first-variable-linear standard inner product on and its norm from The standard formulas on and on are inner products and The norm induced by a real or complex inner product. The matrix norm is the operator norm of the linear map in The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||. On this operator norm is , as specified in that definition.
For each integer , define the scalar phi function and its finite-matrix action by
Here , , , , 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, ; for matrices, is the bounded-operator exponential series of The exponential series of a bounded operator.
At zero, the series gives and for every . For , only the matrix term can be nonzero. For , The vector space of by matrices over a field, with entrywise operations and Rectangular matrix multiplication and the identity matrix , including zero-sized shapes identify with the unique empty matrix ; every power and every partial sum is that matrix. Thus , including .
The recurrence is always interpreted as a series identity, including at zero; division by is not part of this definition.
The Euclidean logarithmic norm of a complex matrix
Definition
Let and . In the standard complex inner product from The standard formulas on and on are inner products, the adjoint matrix is the conjugate transpose by In orthonormal bases, the matrix of the adjoint is the conjugate transpose of the matrix. The Hermitian part is . 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 , where denotes the largest eigenvalue of the Hermitian matrix . The item The Euclidean logarithmic norm is a real Rayleigh-quotient maximum ↗ establishes that this eigenvalue exists, is real, and equals . This is a real growth bound, not a matrix norm.
Normwise certified matrix-vector and vector-step arithmetic
Definition
Fix a finite complex matrix with and the Euclidean norms from The norm 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 and , a scaled matvec output and an exactly represented nonnegative rational upper bound satisfying . This includes square matrix-vector products and the final Arnoldi basis projection.
- For a real scalar and vector , a scaled-vector output and an exactly represented nonnegative rational upper bound satisfying .
- For vectors , a sum output and an exactly represented nonnegative rational upper bound satisfying .
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.
Variation of constants for a finite-matrix action defect
Statement
Let , , , , and be continuously differentiable, with one-sided derivatives at the endpoints. Define the continuous defect . Then, for every ,
The vector integral is the oriented componentwise Riemann integral after identifying with . In particular, if , the initial term vanishes.
Facts & Assumptions
Given: , a complex square matrix , , a vector , and a continuously differentiable curve with defect .
The exponential series of a bounded operator: for a bounded operator , is differentiable, , and .
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.
If is differentiable with integrable then ; and a bounded derivative makes 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.
is the real coordinate plane, with coordinate arithmetic and The standard formulas on and on are inner products: real and imaginary coordinates identify with a finite-dimensional real vector space, and the standard inner product is given coordinatewise.
Every finite-dimensional normed space is Banach: the finite-dimensional complex-inner-product space is complete.
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 over unit vectors.
A continuous function on 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
Set . If , rescaling in [F6] gives , while the same inequality is immediate for ; thus is bounded. By [F1] and [F5], the exponential series applies on the complete space . It gives , and .
Define . Differentiating the product and using that commutes with every power in its exponential series gives .
By [F2] and [F4], is a real finite-dimensional vector-valued curve with continuous derivative. Each coordinate of is continuous and therefore Riemann integrable on every nondegenerate closed subinterval by [F7]. If , then and the oriented integral is zero. If , [F3] on gives . If , [F3] on gives , which is the same oriented identity after reversing the bounds.
Multiply this equality by . Matrix multiplication is a fixed finite-dimensional linear map and therefore commutes with the componentwise integral; [F1] gives , , and . Substitution yields the stated identity.
The scalar and matrix phi series converge absolutely
Statement
For every integer , the scalar series defining converges absolutely for every , and the matrix series defining converges absolutely in operator -norm for every , . At zero, and .
Facts & Assumptions
Given: , , with , and the series definitions of The phi-function family by everywhere-convergent series.
The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: on ; for , for a linear endomorphism of .
The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: for compatible finite-dimensional maps.
Every finite-dimensional normed space is Banach: every finite-dimensional normed space over or is complete.
Proof
Put . Since for every , ; the majorant sums to by [F4]. Thus the scalar series is absolutely convergent.
If , the series definition gives for every . Every term has norm by [F1], so the matrix series is absolutely convergent and every partial sum equals the unique empty matrix. For , put . The operator norm is defined by [F1]. Repeated submultiplicativity in [F2] gives , so for every . The norms therefore have a finite sum by [F4].
For , let be the partial sums of the matrix series. For , the triangle inequality bounds by the tail of the convergent scalar majorant in step 1.2, so is Cauchy. The matrix space is finite-dimensional and complete by [F3], hence converges in operator norm. For , convergence was established directly in step 1.2.
At only the term of the scalar series is nonzero, and at with only the matrix term is nonzero; these terms are respectively and . When , every matrix term and is the unique empty matrix by step 1.2. Hence both stated zero values follow for every allowed size.
The Euclidean logarithmic norm is a real Rayleigh-quotient maximum
Statement
For and , is Hermitian, its eigenvalues are real, and
Facts & Assumptions
Given: , , and as in The Euclidean logarithmic norm of a complex matrix.
The standard formulas on and on are inner products: is the standard complex inner product, linear in the first variable.
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.
Self-adjoint and normal endomorphisms of a finite-dimensional real or complex inner product space: a self-adjoint endomorphism satisfies and is normal.
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.
Proof
By [F2], , so is self-adjoint and hence normal by [F3].
By [F4], choose an orthonormal eigenbasis for , with . Since and self-adjointness gives , each eigenvalue is real; the finite set has a largest member.
By [F5], .
For , [F1] and orthonormality give . Also because . A unit eigenvector for the largest attains equality, proving the maximum formula.
Finite scaled Taylor action for the matrix exponential
Definition
Inputs: with , a vector , an exactly represented finite time , positive rational tolerance , integer budgets and , rational upper bounds , , and , and a provider satisfying Normwise certified matrix-vector and vector-step arithmetic.
For define the rational , which upper-bounds . For rational and integer , define
Outputs: a vector candidate when returned, an extended nonnegative rational error bound in ( means no finite certificate was established), the number of calls to , and one status: success, inconclusive-tail-budget, inconclusive-roundoff, or invalid-input.
Procedure:
- Reject invalid dimensions, nonpositive tolerance, negative bounds, or invalid budgets by returning no candidate, bound , zero calls to , and
invalid-input. If , , or , return , bound , zero calls to , and statussuccess. In the last case forces . - Among pairs and with , select one minimizing subject to . Break ties by smaller , then smaller . If no pair exists, return the zero vector candidate, bound , and
inconclusive-tail-budget. - Set and . For each substep , initialize and . For , call the certified provider with coefficient and vector to obtain and a bound for . Add to the partial sum through the provider, obtaining and a bound for . Set and compute . Set and ; for , set and .
- If any provider result lacks a sound bound, return the current candidate, bound , and
inconclusive-roundoff. Otherwise compute . Return , bound , andsuccessif ; otherwise return the same candidate and bound withinconclusive-roundoff.
The pair search is finite. The recurrence makes exactly calls to when it reaches step 4; the error-bound provider performs no additional 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 pairs in the search, and for every admissible and the denominator is at least , so each is defined. Every matrix-vector and partial-sum loop has a finite bound. The zero branches are exact: gives the zero action, gives , and forces . 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 is the exact matrix-vector call count for a completed recurrence, not a claim about wall-clock cost.
Phi recurrence and augmented-matrix action identities
Statement
For every integer , scalar , and square matrix with , the series-defined functions of The phi-function family by everywhere-convergent series satisfy
For and , let have ones on the superdiagonal and zeros elsewhere, and let be the first and last coordinate vectors. Define . Then the first entries of equal . The identity is valid at and requires no division by or by a scalar argument.
Facts & Assumptions
Given: , , with , and when .
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
Absolute convergence in [F2] permits an index shift: . Rearranging proves the scalar recurrence, including .
For , every matrix term is the unique empty matrix by [F1], so the matrix recurrence is the equality of that matrix with itself. For , the same index shift is valid in operator norm by [F2]; left multiplication by sends the denominator term to . Thus , proving the matrix recurrence.
For , in , a block-product path from the first block row to the last column of the last block must move through the block and then through all superdiagonal entries of . It uses exactly block transitions; any remaining factors can only be copies of before that first transition. Hence the last column of the top-right block of is zero for , and is for .
For , by [F2] with , . By step 1.3, the last column of its top-right block is . This is exactly the first entries obtained by applying to the last coordinate vector. When , is the unique vector in and ; the first zero entries of and are both the empty vector. Thus the augmented identity also holds in that case.
The logarithmic norm bounds forward and reverse matrix exponentials
Statement
For , , and ,
Consequently, for , . No normality, spectral stability, or sign assumption on is required.
Facts & Assumptions
Given: , , and .
The Euclidean logarithmic norm is a real Rayleigh-quotient maximum: for all , and .
The exponential series of a bounded operator: on a Banach space, is differentiable with .
Every finite-dimensional normed space is Banach: the finite-dimensional normed space is complete.
The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: for a finite-dimensional endomorphism.
The exponential function is smooth and : the scalar real exponential satisfies .
The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with : a real function continuous on a closed interval and differentiable on its interior satisfies the mean-value identity at some interior point.
The exponential is a continuous bijection from onto : for every .
Proof
Fix and set and . By [F2] and [F3], . If , differentiability gives ; expanding the inner product gives , so .
By [F1], . Put and . By [F5], : when , write its difference quotient as and use ; when , is constant. The identity and the resulting limits give .
If , the desired vector inequality is equality. If , [F6] applied to on and step 2.1 give . By [F7], ; multiplying and taking nonnegative square roots yields .
Taking the maximum over unit vectors and using [F4] proves . For , apply the proved inequality to at time ; termwise equality of the exponential series gives , proving the reverse-time claim.
Scaled Taylor action has a finite normwise truncation and roundoff certificate
Statement
Let , , , , and choose , , and rational with . Put , , and
Suppose each computed substep has local term and summation certificates as in Finite scaled Taylor action for the matrix exponential, and define , , and (empty sums are zero). Then
If the algorithm has rational upper bound and returns success, its reported certificate is valid and at most the requested tolerance. A completed recurrence uses exactly calls to . A return before the recurrence makes zero such calls; an early stop during it makes at most calls.
Facts & Assumptions
Given: A finite complex matrix , vector , real time , integers , a rational upper bound for , and successful local arithmetic certificates as specified by Finite scaled Taylor action for the matrix exponential.
The operator norm is submultiplicative and satisfies ||T^*T|| = ||T||^2: the operator -norm is submultiplicative and homogeneous.
The exponential series of a bounded operator: the matrix exponential satisfies .
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 call for each pair .
Proof
Let . For , consecutive scalar tail terms have ratio . The tail is therefore bounded by the geometric series beginning at , giving .
For , : for , the factors in are each at least , so and . Thus and .
Fix substep and let and . The local provider certificate gives ; induction on yields the bound . The partial-sum certificate adds at most at stage , so .
Write . The algebraic telescoping identity holds by cancellation of adjacent terms. By step 1.2 every summand has norm at most , so . By [F3], .
Set . Repeated substitution gives . By step 1.2, , hence .
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.
If the recurrence completes, its nested loops make one call to for each and , giving exactly calls, including zero when . No other step calls , so a return before the recurrence makes zero calls and an early stop during it makes at most 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.
Arnoldi projection for exponential and phi-function actions
Definition
Inputs: with , , time , integer , Krylov budget , nonnegative threshold , an exact-arithmetic Arnoldi run or a finite-precision Arnoldi backend, and a reduced-action configuration. In finite precision, the backend returns a length with , for , , , 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 through 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 , final-projection error bound , 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:
- Reject invalid dimensions, , , or threshold by returning no candidate, both error bounds , no Arnoldi data or relation certificates,
arnoldi_status=invalid-input, andrelation_status=reduced_action_status=projection_status=not-run. If , return the zero vector with both error bounds zero,arnoldi_status=zero-start, andrelation_status=reduced_action_status=projection_status=not-run, without division. - In exact-arithmetic mode, run The Arnoldi process for building an orthonormal basis of a Krylov subspace for at most steps and retain , , and the leading square Hessenberg matrix ; set
relation_status=exact. Stop when the Arnoldi residual norm is exactly zero and setarnoldi_status=happy-breakdown; otherwise continue until steps and leave the Arnoldi status pending. In finite-precision mode, if the backend data have invalid dimensions or fail for , return the zero candidate, both bounds ,arnoldi_status=inconclusive-arnoldi,relation_status=inconclusive, andreduced_action_status=projection_status=not-run. Otherwise use the backend's and computed subdiagonal. When available, it supplies a relation certificate , with , column bounds for , and a norm bound for . Setrelation_status=certifiedif this relation is supplied and verified, andrelation_status=inconclusiveotherwise. A verified exact happy-breakdown certificate setsrelation_status=certifiedandarnoldi_status=happy-breakdown; it verifies the exact Arnoldi recurrence, including and . Without that full certificate, a computed subdiagonal at most setsarnoldi_status=numerical-breakdown; continue to the reduced evaluation but never label it happy breakdown. Otherwise leave the Arnoldi status pending until Step 5. - For , request a reduced configuration for . If it is missing or invalid, including if it cannot supply the time exactly and a rational upper bound on , treat the Taylor request as returning no candidate without calling it. Otherwise call Finite scaled Taylor action for the matrix exponential to approximate . If no reduced candidate is available, set , set
reduced_action_status=inconclusiveandprojection_status=not-run, set a pendingarnoldi_statustoinconclusive-arnoldiwhenrelation_status=inconclusiveand toprojectedotherwise, and return no candidate. Otherwise set and to its candidate and bound. Setreduced_action_status=successonly when the Taylor status issuccess; otherwise set it toinconclusive. Continue to Step 5 with the returned candidate in either case. - For , form from Phi recurrence and augmented-matrix action identities, request a reduced configuration for . 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 and rational time bound . If no reduced candidate is available, set , set
reduced_action_status=inconclusiveandprojection_status=not-run, set a pendingarnoldi_statustoinconclusive-arnoldiwhenrelation_status=inconclusiveand toprojectedotherwise, and return no candidate. Otherwise let be the first entries of its candidate and use the certified vector-scaling provider with scalar to obtain and local bound . Set , where is the augmented Taylor bound; if either certificate is missing set andreduced_action_status=inconclusive. Setreduced_action_status=successwhen the Taylor status issuccessand the scaling certificate is sound; otherwise set it toinconclusive. Continue to Step 5 in either case. - In exact-arithmetic mode set , , and
projection_status=exact. In finite precision, use the certified matrix-vector provider with matrix , scalar , and vector to return and bound ; a sound certificate setsprojection_status=certified, and missing certification sets andprojection_status=inconclusive. Return . Ifarnoldi_statusis still pending, set it toinconclusive-arnoldiwhenrelation_status=inconclusiveandprojectedotherwise.
In exact arithmetic the projected formula is . Exact happy breakdown means the generated Krylov subspace is invariant and this projected formula equals ; 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 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 , the local action-error theorem adds the reduced-coefficient and final-projection error bounds under its hypotheses. For , the local Taylor theorem certifies the augmented reduced action, and the vector-scaling provider certifies multiplication by .
Arnoldi action error is the semigroup-weighted residual integral
Statement
Let , , , , , , and . For exact Arnoldi, ; in a certified finite-precision run, may be the backend's returned nonnegative scalar, with its effect included in the initial mismatch. Put and . For every real , the exact defect identity is
where the integral is oriented. Let for and for ; let for and for . If the integral is zero. Suppose the Arnoldi relation is
where , when , , , , , and . In exact happy breakdown, interpret the rank-one term as zero and set . If a reduced computation returns with for and its final projection returns with for , then
For exact Arnoldi from a nonzero start with no breakdown through , , has orthonormal columns, and , recovering the standard residual integral. Whenever and , the invariant-subspace relation gives for every and every real ; 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 and a final projection certificate .
Variation of constants for a finite-matrix action defect: for a continuously differentiable approximate trajectory , the exact action error is its initial error plus the oriented integral of .
The logarithmic norm bounds forward and reverse matrix exponentials: for , for every finite complex matrix .
Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization: before breakdown, Arnoldi columns are orthonormal and .
The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||: for a finite-dimensional linear map , ; rescaling gives for all .
Cauchy–Schwarz: , with equality exactly for linearly dependent vectors: in a complex Euclidean space.
The Arnoldi process for building an orthonormal basis of a Krylov subspace: Arnoldi starts with , defines , and exact breakdown at step means .
The exponential series of a bounded operator: for a bounded operator on a Banach space, its exponential is differentiable with derivative .
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.
Every finite-dimensional normed space is Banach: each finite-dimensional normed space over is complete.
For a finite-precision relation, and all displayed norm bounds are certified data; the theorem does not infer them from a small computed subdiagonal.
The final computed projection satisfies for a nonnegative bound , with when the projection is exact.
Proof
For any nonzero , rescaling in [F4] shows that the finite matrix is bounded; the inequality is immediate at . By [F9], is complete, so [F7] applies and gives . Hence is continuously differentiable and its defect is .
For , and ; the same identity gives . Thus [F2] bounds the initial factor by and the integral kernel by .
Apply [F1] to this . It gives the displayed exact integral identity, including the initial mismatch .
Substitute the certified relation [A1] into the defect in step 2.1. The rank-one term has norm because . For any unit vector , Cauchy–Schwarz [F5] gives ; hence . The remaining term is at most . Applying the kernel bound of step 1.2 inside the integral proves the first two terms of the claimed estimate.
Under the exact invariant-subspace hypotheses and in the Statement, induction gives for every : it holds at , and left multiplication by gives . Applying the convergent series [F8] term by term yields for every . To verify the stated exact Arnoldi case, each earlier column satisfies by the recurrence, while the last satisfies because [F6] gives ; hence , and the initialization in [F6] gives . This proves exact invariant-space projection for every phi action; the separate hypothesis in this theorem concerns the exponential trajectory .
The reduced-action error satisfies by [F4]. By [A2] and the triangle inequality, replacing by adds at most . Adding both errors to the exact projected-action error from step 3.1 proves the claimed estimate.
In exact Arnoldi without breakdown, [F3] supplies the orthonormal basis and the relation , so ; [F6] gives . Orthonormality gives , and an exact reduced action has . 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
- Awad H. Al-Mohy and Nicholas J. Higham, Computing the action of the matrix exponential, with an application to exponential integrators
- Jitse Niesen and Will M. Wright, A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators
- Nicholas J. Higham, What Is the Logarithmic Norm?
- Nicholas J. Higham and Lijing Lin, Functions of Matrices: A Short Course