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13 results · all verified · 2 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 11 not AI-judged were verified by owner audit (typically over a confirmed judge false positive), not failures.

Krylov Subspaces, Arnoldi and GMRES

1 · Prerequisites

2 · Summary

This page builds the nonsymmetric Krylov route in the order the later proofs actually spend it. First come the Krylov spaces themselves, the relative minimal polynomial and the grade, because those control when the subspaces stop growing and when exact termination is even possible.

Arnoldi then turns that abstract Krylov filtration into an orthonormal basis and an upper Hessenberg reduction. Ritz data and GMRES are read off from that factorization. The page closes with the exact-arithmetic termination theorem, the diagonalizable residual bound that keeps the eigenvector-conditioning factor, the restart warning, and the matrix-free access model that explains which operations Krylov methods actually need.

3 · Logical flowchart

4 · Definitions, theorems and proofs

DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

The Krylov subspace Km(A,b)=span{b,Ab,,Am1b}

Definition

Let A be a square matrix over R or C and let b be a vector of matching size. Define

K0(A,b):={0},

and for m1 define the mth Krylov subspace

Km(A,b):=span{b,Ab,,Am1b}.

Thus Km(A,b)Km+1(A,b) for every m0.

DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-31Open item page →

The grade of a start vector and its relative minimal polynomial

Definition

Let A be a square matrix over R or C, and let b be a vector of matching size. The relative minimal polynomial of b with respect to A is the unique monic polynomial over the same field qA,b of least degree such that

qA,b(A)b=0.

Its degree

ν(A,b):=degqA,b

is the grade of b relative to A.

If b=0, then qA,b=1 and ν(A,b)=0.

This polynomial is well defined. If A is n×n, then the n+1 vectors b,Ab,,Anb are linearly dependent, so some nonzero polynomial annihilates b; dividing a least-degree annihilator by its leading coefficient makes it monic. If two monic least-degree annihilators existed, their difference would be an annihilator of strictly smaller degree, so they would be equal.

PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

The Krylov subspace consists exactly of the vectors p(A)b for zero polynomials or polynomials of degree less than m

Statement

Let A be a square matrix over R or C, let b be a vector of matching size, and let m1. Then

Km(A,b)={p(A)b:p=0 or degp<m}.

Facts & Assumptions

Given: A square matrix A over R or C, a vector b of matching size, and an integer m1.

[F1]

For m1, the Krylov subspace is Km(A,b)=span{b,Ab,,Am1b} (The Krylov subspace Km(A,b)=span{b,Ab,,Am1b}).

Proof

technique · direct
1.1

If vKm(A,b), then [F1] gives scalars c0,,cm1 with v=j=0m1cjAjb. With p(z):=j=0m1cjzj, either p=0 or degp<m, and v=p(A)b.

F1constructalgebra
2.1

Conversely, p=0 gives p(A)b=0Km(A,b). If instead degp<m and p(z)=j=0m1cjzj, then p(A)b=j=0m1cjAjb, which lies in the span from [F1]. Hence every displayed p(A)b lies in Km(A,b).

F1algebra
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

The dimensions of the Krylov spaces grow by one until the grade and then stabilize

Statement

Let ν=ν(A,b) be the grade of b relative to A. Then:

  1. for every integer m with 0m<ν, one has dimKm(A,b)=m;
  2. for every integer mν, one has Km(A,b)=Kν(A,b).

Equivalently, the dimensions increase by one at each step until the grade and then stop changing.

Facts & Assumptions

Given: A square matrix A, a vector b, and its grade ν=ν(A,b).

[F1]

The relative minimal polynomial qA,b is monic of degree ν and satisfies qA,b(A)b=0 (The grade of a start vector and its relative minimal polynomial).

Proof

technique · direct
1.1

If 0m<ν and j=0m1cjAjb=0, then the polynomial p(z)=j=0m1cjzj satisfies degp<m<ν and p(A)b=0. By minimality in [F1], this forces p=0, so b,Ab,,Am1b are linearly independent. Therefore dimKm(A,b)=m.

F1L1algebra
2.1

Writing qA,b(z)=a0+a1z++aν1zν1+zν, the relation qA,b(A)b=0 from [F1] shows that AνbKν(A,b). Multiplying the same relation by Ak for each k0 gives Aν+kbKν(A,b) as well. Hence every generator of Km(A,b) for mν already lies in Kν(A,b), so Km(A,b)=Kν(A,b).

F1algebra
CorollaryStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

If A is invertible, the exact solution of Ax=b lies in the Krylov space at the grade

Statement

Let A be invertible, let b be a vector, and let ν=ν(A,b). If x is the exact solution of Ax=b, then

xKν(A,b).

Facts & Assumptions

Given: An invertible matrix A, a vector b, its grade ν=ν(A,b), and the exact solution x=A1b.

[F1]

The relative minimal polynomial qA,b is monic of degree ν and satisfies qA,b(A)b=0 (The grade of a start vector and its relative minimal polynomial).

[L1]

For every mν, one has Km(A,b)=Kν(A,b) (The dimensions of the Krylov spaces grow by one until the grade and then stabilize).

Proof

technique · direct
1.1

If b=0, then x=0K0(A,b)=Kν(A,b). Assume now that b0. Write qA,b(z)=a0+a1z++aν1zν1+zν. The constant term a0 is nonzero, because a0=0 would give qA,b(z)=zr(z) and then 0=qA,b(A)b=Ar(A)b, so invertibility of A would imply r(A)b=0 with degr=ν1, contradicting [F1].

givenF1algebra
2.1

From qA,b(A)b=0 we obtain a0b+A(a1I++aν1Aν2+Aν1)b=0. Multiplying by A1 and dividing by a0 yields x=A1b=a01(a1I++aν1Aν2+Aν1)b, so xKν(A,b). The inclusion is already enough, and [L1] shows it persists in every later Krylov space.

F1L1algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

The Arnoldi process for building an orthonormal basis of a Krylov subspace

Definition

Let A act on a real or complex inner-product space, and let b0. The Arnoldi process starts with

v1:=bb.

Given orthonormal vectors v1,,vj, define

hij:=Avj,vi(1ij),

and the orthogonalized residual

wj:=Avji=1jhijvi.

Set

hj+1,j:=wj.

If hj+1,j0, define

vj+1:=wjhj+1,j.

The process is said to break down exactly at step j when hj+1,j=0.

TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization

Statement

Let A be an n×n matrix over R or C, let b be a nonzero vector of matching size, and let m1. Assume the Arnoldi process for (A,b) runs through step m without breakdown. Let

Qm=[v1vm],Qm+1=[v1vm+1],

and let Hˉm=(hij)1im+1,1jm, where the Arnoldi coefficients occupy the positions ij+1 and hij:=0 for i>j+1. Then:

  1. v1,,vm are orthonormal;
  2. span{v1,,vm}=Km(A,b);
  3. Hˉm has the rectangular upper-Hessenberg zero pattern hij=0 for i>j+1;
  4. AQm=Qm+1Hˉm.

Facts & Assumptions

Given: A square matrix A over R or C, a nonzero start vector b of matching size, an integer m1, and an Arnoldi run through step m with no breakdown.

[F1]

In Arnoldi, each step defines wj=Avji=1jhijvi with hij=Avj,vi, and when hj+1,j=wj0 one sets vj+1=wj/hj+1,j (The Arnoldi process for building an orthonormal basis of a Krylov subspace).

[F2]

For m1, one has Km(A,b)=span{b,Ab,,Am1b} (The Krylov subspace Km(A,b)=span{b,Ab,,Am1b}).

[F3]

The square-matrix upper-Hessenberg condition is the zero pattern hij=0 for i>j+1 (Upper Hessenberg matrices and real symmetric tridiagonal matrices).

Proof

technique · direct
1.1

By construction in [F1], the vector wj is orthogonal to each vi for 1ij, because hij=Avj,vi subtracts exactly the orthogonal projection of Avj onto vi. Since there is no breakdown, wj0, so vj+1=wj/wj is a unit vector orthogonal to v1,,vj. Starting from the unit vector v1=b/b, induction gives orthonormality of v1,,vm.

F1algebra
1.2

Again by [F1], each vj+1 is a linear combination of Avj,v1,,vj. Induction using [F2] therefore gives viKi(A,b)Km(A,b) for im. Conversely, Avj=i=1j+1hijvi lies in span{v1,,vj+1}, and induction from v1=b/b shows that b,Ab,,Am1b all lie in span{v1,,vm}. By [F2], the two spans are equal.

F1F2algebra
2.1

For each column j, the Arnoldi relation from [F1] involves only v1,,vj+1. By the definition of Hˉm in the Statement, its remaining entries satisfy hij=0 for i>j+1, the same zero pattern as the square upper-Hessenberg condition [F3]. The column relations assemble exactly into AQm=Qm+1Hˉm.

F1F3algebra
PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

Exact Arnoldi breakdown is equivalent to invariance of the current Krylov space

Statement

Let m1. Suppose Arnoldi has run through step m without earlier breakdown. Then the next Arnoldi residual vanishes, equivalently hm+1,m=0, if and only if the current Krylov space Km(A,b) is A-invariant.

Facts & Assumptions

Given: An integer m1 and an Arnoldi run through step m with basis vectors v1,,vm.

[L1]

Before breakdown, Arnoldi produces an orthonormal basis of Km(A,b) and the relations Avjspan{v1,,vj+1} for 1jm (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).

Proof

technique · direct
1.1

Assume hm+1,m=0. Then the Arnoldi step gives Avmspan{v1,,vm}=Km(A,b). For every j<m, [L1] already gives AvjKj+1(A,b)Km(A,b). Since the vj form a basis of Km(A,b), linearity shows A(Km(A,b))Km(A,b).

L1algebra
2.1

Conversely, assume Km(A,b) is A-invariant. Because vmKm(A,b), one has AvmKm(A,b)=span{v1,,vm}. Therefore the part of Avm orthogonal to that span is zero, so the Arnoldi residual wm vanishes and hence hm+1,m=wm=0.

L1algebra
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction

Definition

Let Arnoldi run through step m and write

Hm:=QmAQm.

An eigenvalue θ of Hm is a Ritz value of A from the Arnoldi subspace Km(A,b).

If y0 satisfies

Hmy=θy,

then the lifted vector

u:=Qmy

is the corresponding Ritz vector.

PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

An Arnoldi Ritz pair has residual norm controlled by the last Hessenberg subdiagonal entry

Statement

Assume Arnoldi runs through step m without breakdown, let Hm=QmAQm, and let Hmy=θy with y2=1. If u=Qmy is the associated Ritz vector, then

Auθu=hm+1,m(emy)vm+1.

In particular,

Auθu2=hm+1,memy.

Facts & Assumptions

Given: An Arnoldi run through step m without breakdown, a unit eigenvector y of Hm, and the Ritz vector u=Qmy.

[F1]

In Arnoldi, Ritz values are eigenvalues of Hm=QmAQm and Ritz vectors have the form u=Qmy (Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction).

[L1]

Arnoldi gives AQm=Qm+1Hˉm, where Hˉm=[Hmhm+1,mem] (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).

Proof

technique · direct
1.1

Using [F1] and [L1], Au=AQmy=Qm+1Hˉmy=QmHmy+hm+1,m(emy)vm+1. Since Hmy=θy, this becomes Au=θQmy+hm+1,m(emy)vm+1.

F1L1algebra
2.1

Because Qmy=u, step 1.1 is exactly Auθu=hm+1,m(emy)vm+1. The Arnoldi vectors are orthonormal, so vm+12=1, and the norm formula follows.

step 1.1L1algebra
DefinitionDefinition: AI-adaptedProof: Not applicableaudited 2026-08-31Open item page →

The GMRES iterate as the residual minimizer over an affine Krylov space

Definition

Let Ax=b be a linear system, let x0 be an initial guess, and let

r0:=bAx0.

For m0, a GMRES iterate at step m is any vector xmx0+Km(A,r0) such that

bAxm2=minxx0+Km(A,r0)bAx2.

Thus GMRES is defined by residual minimization over the affine Krylov space generated by the initial residual.

TheoremStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

Arnoldi reduces GMRES to a least-squares problem for the small Hessenberg matrix

Statement

Let r0=bAx00 and set β=r02. Run Arnoldi on (A,r0) through step m without breakdown, so that v1=r0/β. Then every vector xx0+Km(A,r0) has the form x=x0+Qmy for some yFm, and

bAx=Qm+1(βe1Hˉmy).

Consequently,

minxx0+Km(A,r0)bAx2=minyFmβe1Hˉmy2.

Facts & Assumptions

Given: A linear system Ax=b, an initial guess x0, the nonzero initial residual r0=bAx0, and an Arnoldi run on (A,r0) through step m without breakdown.

[F1]

A GMRES iterate minimizes the residual norm over x0+Km(A,r0) (The GMRES iterate as the residual minimizer over an affine Krylov space).

[L1]

Arnoldi gives an orthonormal basis of Km(A,r0) and the factorization AQm=Qm+1Hˉm with Qm+1Qm+1=I (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).

Proof

technique · direct
1.1

By [L1], the columns of Qm form a basis of Km(A,r0), so every xx0+Km(A,r0) can be written uniquely as x=x0+Qmy. Since r0=βv1=βQm+1e1 and bAx0=r0, one has bAx=r0AQmy=Qm+1βe1Qm+1Hˉmy=Qm+1(βe1Hˉmy).

L1constructalgebra
2.1

The columns of Qm+1 are orthonormal by [L1], so multiplying by Qm+1 preserves the Euclidean norm. Therefore bAx2=βe1Hˉmy2. Taking minima over the corresponding parametrized sets gives the stated small least-squares problem. This is the residual norm whose minimizers [F1] calls GMRES iterates.

F1L1step 1.1
CorollaryStatement: AI-adaptedProof: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0)

Statement

If ym minimizes βe1Hˉmy2 in the Arnoldi coordinates of the previous theorem, then

xm:=x0+Qmym

is a GMRES iterate. Equivalently, GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0).

Facts & Assumptions

Given: The Arnoldi parametrization from the GMRES reduction theorem and a vector ym minimizing βe1Hˉmy2.

[F1]

A GMRES iterate is, by definition, a residual minimizer over x0+Km(A,r0) (The GMRES iterate as the residual minimizer over an affine Krylov space).

[L1]

Arnoldi identifies the residual norm on x0+Km(A,r0) with the small least-squares norm βe1Hˉmy2 (Arnoldi reduces GMRES to a least-squares problem for the small Hessenberg matrix).

Proof

technique · direct
1.1

By [L1], every xx0+Km(A,r0) has the form x=x0+Qmy, and for that x one has bAx2=βe1Hˉmy2.

L1
2.1

Since ym minimizes the right-hand side, the vector xm=x0+Qmym minimizes the left-hand side over the whole affine Krylov space. By [F1], that is exactly the GMRES condition.

F1step 1.1
CorollaryStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

For unrestarted GMRES, the residual norms are nonincreasing

Statement

Let xm be an unrestarted GMRES iterate at step m. Then

bAxm+12bAxm2(m0).

Facts & Assumptions

Given: Unrestarted GMRES iterates xm and xm+1 for the same initial guess x0.

[L1]

Each GMRES iterate minimizes the residual norm over its affine Krylov space x0+Km(A,r0) (GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0)).

Proof

technique · direct
1.1

Because Km(A,r0)Km+1(A,r0), one has x0+Km(A,r0)x0+Km+1(A,r0). In particular, xm is an admissible competitor for the (m+1)st minimization problem.

givenalgebra
2.1

By [L1], xm+1 minimizes the residual norm over the larger affine space, so bAxm+12bAxm2.

L1step 1.1
PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

The GMRES residual has the form rm=pm(A)r0 with pm(0)=1 and degpmm

Statement

Let xm be an mth GMRES iterate for Ax=b with initial residual r0=bAx0, and let rm=bAxm. Then there is a polynomial pm such that

rm=pm(A)r0,pm(0)=1,degpmm.

Moreover, among all polynomials p with degpm and p(0)=1, GMRES chooses one minimizing p(A)r02.

Facts & Assumptions

Given: A GMRES iterate xm with residual rm=bAxm.

[L1]

Every vector in Km(A,r0) has the form q(A)r0 with degq<m (The Krylov subspace consists exactly of the vectors p(A)b for zero polynomials or polynomials of degree less than m).

[L2]

GMRES minimizes the residual norm over x0+Km(A,r0) (GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0)).

Proof

technique · direct
1.1

Since xmx0Km(A,r0), [L1] gives a polynomial q with degq<m and xm=x0+q(A)r0. Therefore rm=bAxm=r0Aq(A)r0=pm(A)r0, where pm(z):=1zq(z). Then pm(0)=1 and degpmm.

L1constructalgebra
2.1

Conversely, every polynomial p with degpm and p(0)=1 can be written as p(z)=1zq(z) with degq<m. The corresponding vector x=x0+q(A)r0 lies in x0+Km(A,r0) by [L1], and its residual is p(A)r0. Since [L2] makes xm minimize that residual norm over all such x, the polynomial attached in step 1.1 minimizes p(A)r02 among all admissible p.

L1L2step 1.1
TheoremStatement: AI-adaptedProof: AI-generatedprecheck passjudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

In exact arithmetic, unrestarted GMRES terminates no later than the relative grade

Statement

Let A be invertible, let x0 be an initial guess, let r0=bAx0, and let ν=ν(A,r0). In exact arithmetic, unrestarted GMRES produces a zero residual no later than step ν.

Facts & Assumptions

Given: An invertible matrix A, an initial guess x0, the initial residual r0=bAx0, and its grade ν=ν(A,r0).

[L1]

The exact correction A1r0 lies in Kν(A,r0) (If A is invertible, the exact solution of Ax=b lies in the Krylov space at the grade).

[L2]

GMRES minimizes the residual norm over x0+Kν(A,r0) (GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0)).

Proof

technique · direct
1.1

Let x be the exact solution of Ax=b. Then xx0=A1(bAx0)=A1r0, so [L1] gives xx0+Kν(A,r0).

L1algebra
2.1

The exact solution has residual bAx=0. Since x is an admissible point in the νth GMRES affine space, [L2] forces the νth GMRES residual norm to be at most 0, hence equal to 0. Therefore unrestarted GMRES terminates by step ν.

L2step 1.1
PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

For a diagonalizable matrix, the GMRES residual bound carries the eigenvector-conditioning factor κ(V)

Statement

Assume A=VΛV1 is diagonalizable, with Λ=diag(λ1,,λn). If rm is the mth GMRES residual, then

rm2κ2(V)mindegpmp(0)=1max1inp(λi)r02,

where κ2(V)=V2V12.

Facts & Assumptions

Given: A diagonalization A=VΛV1 and an mth GMRES residual rm.

[L1]

One has rm=pm(A)r0 for some polynomial pm with degpmm and pm(0)=1, and GMRES chooses such a polynomial minimizing the residual norm (The GMRES residual has the form rm=pm(A)r0 with pm(0)=1 and degpmm).

Proof

technique · direct
1.1

By [L1], rm=pm(A)r0=Vpm(Λ)V1r0. Hence rm2V2pm(Λ)2V12r02. Since pm(Λ) is diagonal, its operator norm is maxipm(λi). Therefore rm2κ2(V)maxipm(λi)r02.

L1algebra
2.1

The polynomial pm from [L1] minimizes p(A)r02 over all polynomials with degpm and p(0)=1, so the bound from step 1.1 also holds after taking the minimum over that admissible class.

L1step 1.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

Restarted GMRES(m) as repeated GMRES cycles on fixed-size Krylov spaces

Definition

Fix a restart length m1. Restarted GMRES(m) produces iterates in cycles:

  1. start from x(0)=x0;
  2. at cycle j, form the residual r(j)=bAx(j);
  3. run ordinary GMRES for exactly m steps with initial guess x(j) and initial residual r(j);
  4. call the resulting residual minimizer x(j+1) and restart from it.

Thus each cycle uses only the current residual and a fresh size-m Krylov space.

PropositionStatement: AI-adaptedProof: AI-generatedprecheck passaudited 2026-08-31Open item page →

Restarted GMRES preserves cyclewise residual minimization but loses the unrestarted finite-termination guarantee

Statement

In restarted GMRES(m), each cycle output x(j+1) minimizes the residual norm over the current affine space x(j)+Km(A,r(j)). However, after restarting one no longer minimizes over the full unrestarted space x0+Kk(A,r0) for large k, so the unrestarted finite-termination theorem does not carry over automatically.

Facts & Assumptions

Given: A restarted GMRES(m) cycle from x(j) with residual r(j)=bAx(j).

[F1]

Restarted GMRES(m) is defined by running ordinary GMRES for m steps from the current iterate and then restarting from the cycle output (Restarted GMRES(m) as repeated GMRES cycles on fixed-size Krylov spaces).

[L1]

Ordinary GMRES minimizes the residual norm over its affine Krylov space (GMRES minimizes the residual norm over the affine Krylov space x0+Km(A,r0)).

[L2]

Unrestarted finite termination uses the fact that the exact solution lies in one large affine Krylov space built from the original residual (In exact arithmetic, unrestarted GMRES terminates no later than the relative grade).

Proof

technique · direct
1.1

By [F1], the (j+1)st restarted iterate is obtained by applying ordinary GMRES to the system with initial guess x(j) and initial residual r(j). Therefore [L1] gives bAx(j+1)2=minxx(j)+Km(A,r(j))bAx2.

F1L1
2.1

The unrestarted theorem [L2] uses the single expanding family x0+Kk(A,r0). After a restart, [F1] replaces that space by the new cycle space x(j)+Km(A,r(j)), which need not contain the exact solution even when some larger unrestarted space would. Hence cyclewise minimization remains true, but the unrestarted finite-termination guarantee is no longer a formal consequence after restart.

F1L2step 1.1
DefinitionDefinition: AI-adaptedProof: Not applicablejudge pass (gpt-5.6-terra)audited 2026-08-31Open item page →

The matrix-free access model for Krylov methods

Definition

In the matrix-free access model for a Krylov method, the algorithm does not need the entries of A as stored data. It only needs:

  1. the ability to apply A to chosen vectors vAv;
  2. vector additions and scalar multiples;
  3. inner products and norms, when the method orthogonalizes or measures a residual.

Thus Arnoldi and GMRES may be implemented from an operator routine for vAv without explicitly assembling the full matrix.

5 · Examples, counterexamples and false statements

None yet.

Sources