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Krylov Subspaces, Arnoldi and GMRES
1 · Prerequisites
- Binary Operations, Monoids, Groups and Subgroups
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Determinants of Matrices over a Commutative Ring
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalue Iterations and the QR Algorithm
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Roots, Rational Powers, and Classical Inequalities
- Simple Field Extensions and the Construction of the Complex Numbers
- Suprema and Infima
- The ZFC Axioms and the Basic Set Constructions
- Vector Spaces, Linear Subspaces, Span and Direct Sums
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
The Krylov subspace
Definition
Let be a square matrix over or and let be a vector of matching size. Define
and for define the th Krylov subspace
Thus for every .
The grade of a start vector and its relative minimal polynomial
Definition
Let be a square matrix over or , and let be a vector of matching size. The relative minimal polynomial of with respect to is the unique monic polynomial over the same field of least degree such that
Its degree
is the grade of relative to .
If , then and .
This polynomial is well defined. If is , then the vectors are linearly dependent, so some nonzero polynomial annihilates ; 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.
The Krylov subspace consists exactly of the vectors for zero polynomials or polynomials of degree less than
Statement
Let be a square matrix over or , let be a vector of matching size, and let . Then
Facts & Assumptions
Given: A square matrix over or , a vector of matching size, and an integer .
For , the Krylov subspace is (The Krylov subspace ).
Proof
If , then [F1] gives scalars with With , either or , and .
Conversely, gives . If instead and , then which lies in the span from [F1]. Hence every displayed lies in .
The dimensions of the Krylov spaces grow by one until the grade and then stabilize
Statement
Let be the grade of relative to . Then:
- for every integer with , one has ;
- for every integer , one has .
Equivalently, the dimensions increase by one at each step until the grade and then stop changing.
Facts & Assumptions
Given: A square matrix , a vector , and its grade .
The relative minimal polynomial is monic of degree and satisfies (The grade of a start vector and its relative minimal polynomial).
Proof
If and , then the polynomial satisfies and . By minimality in [F1], this forces , so are linearly independent. Therefore .
Writing , the relation from [F1] shows that . Multiplying the same relation by for each gives as well. Hence every generator of for already lies in , so .
If is invertible, the exact solution of lies in the Krylov space at the grade
Statement
Let be invertible, let be a vector, and let . If is the exact solution of , then
Facts & Assumptions
Given: An invertible matrix , a vector , its grade , and the exact solution .
The relative minimal polynomial is monic of degree and satisfies (The grade of a start vector and its relative minimal polynomial).
For every , one has (The dimensions of the Krylov spaces grow by one until the grade and then stabilize).
Proof
If , then . Assume now that . Write . The constant term is nonzero, because would give and then , so invertibility of would imply with , contradicting [F1].
From we obtain Multiplying by and dividing by yields so . The inclusion is already enough, and [L1] shows it persists in every later Krylov space.
The Arnoldi process for building an orthonormal basis of a Krylov subspace
Definition
Let act on a real or complex inner-product space, and let . The Arnoldi process starts with
Given orthonormal vectors , define
and the orthogonalized residual
Set
If , define
The process is said to break down exactly at step when .
Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization
Statement
Let be an matrix over or , let be a nonzero vector of matching size, and let . Assume the Arnoldi process for runs through step without breakdown. Let
and let , where the Arnoldi coefficients occupy the positions and for . Then:
- are orthonormal;
- ;
- has the rectangular upper-Hessenberg zero pattern for ;
- .
Facts & Assumptions
Given: A square matrix over or , a nonzero start vector of matching size, an integer , and an Arnoldi run through step with no breakdown.
In Arnoldi, each step defines with , and when one sets (The Arnoldi process for building an orthonormal basis of a Krylov subspace).
For , one has (The Krylov subspace ).
The square-matrix upper-Hessenberg condition is the zero pattern for (Upper Hessenberg matrices and real symmetric tridiagonal matrices).
Proof
By construction in [F1], the vector is orthogonal to each for , because subtracts exactly the orthogonal projection of onto . Since there is no breakdown, , so is a unit vector orthogonal to . Starting from the unit vector , induction gives orthonormality of .
Again by [F1], each is a linear combination of . Induction using [F2] therefore gives for . Conversely, lies in , and induction from shows that all lie in . By [F2], the two spans are equal.
For each column , the Arnoldi relation from [F1] involves only . By the definition of in the Statement, its remaining entries satisfy for , the same zero pattern as the square upper-Hessenberg condition [F3]. The column relations assemble exactly into .
Exact Arnoldi breakdown is equivalent to invariance of the current Krylov space
Statement
Let . Suppose Arnoldi has run through step without earlier breakdown. Then the next Arnoldi residual vanishes, equivalently , if and only if the current Krylov space is -invariant.
Facts & Assumptions
Given: An integer and an Arnoldi run through step with basis vectors .
Before breakdown, Arnoldi produces an orthonormal basis of and the relations for (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).
Proof
Assume . Then the Arnoldi step gives . For every , [L1] already gives . Since the form a basis of , linearity shows .
Conversely, assume is -invariant. Because , one has . Therefore the part of orthogonal to that span is zero, so the Arnoldi residual vanishes and hence .
Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction
Definition
Let Arnoldi run through step and write
An eigenvalue of is a Ritz value of from the Arnoldi subspace .
If satisfies
then the lifted vector
is the corresponding Ritz vector.
An Arnoldi Ritz pair has residual norm controlled by the last Hessenberg subdiagonal entry
Statement
Assume Arnoldi runs through step without breakdown, let , and let with . If is the associated Ritz vector, then
In particular,
Facts & Assumptions
Given: An Arnoldi run through step without breakdown, a unit eigenvector of , and the Ritz vector .
In Arnoldi, Ritz values are eigenvalues of and Ritz vectors have the form (Ritz values and Ritz vectors extracted from the Arnoldi Hessenberg reduction).
Proof
Using [F1] and [L1], Since , this becomes
Because , step 1.1 is exactly . The Arnoldi vectors are orthonormal, so , and the norm formula follows.
The GMRES iterate as the residual minimizer over an affine Krylov space
Definition
Let be a linear system, let be an initial guess, and let
For , a GMRES iterate at step is any vector such that
Thus GMRES is defined by residual minimization over the affine Krylov space generated by the initial residual.
Arnoldi reduces GMRES to a least-squares problem for the small Hessenberg matrix
Statement
Let and set . Run Arnoldi on through step without breakdown, so that . Then every vector has the form for some , and
Consequently,
Facts & Assumptions
Given: A linear system , an initial guess , the nonzero initial residual , and an Arnoldi run on through step without breakdown.
A GMRES iterate minimizes the residual norm over (The GMRES iterate as the residual minimizer over an affine Krylov space).
Arnoldi gives an orthonormal basis of and the factorization with (Before breakdown, Arnoldi produces an orthonormal Krylov basis and a rectangular upper-Hessenberg factorization).
Proof
By [L1], the columns of form a basis of , so every can be written uniquely as . Since and , one has
The columns of are orthonormal by [L1], so multiplying by preserves the Euclidean norm. Therefore 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.
GMRES minimizes the residual norm over the affine Krylov space
Statement
If minimizes in the Arnoldi coordinates of the previous theorem, then
is a GMRES iterate. Equivalently, GMRES minimizes the residual norm over the affine Krylov space .
Facts & Assumptions
Given: The Arnoldi parametrization from the GMRES reduction theorem and a vector minimizing .
A GMRES iterate is, by definition, a residual minimizer over (The GMRES iterate as the residual minimizer over an affine Krylov space).
Arnoldi identifies the residual norm on with the small least-squares norm (Arnoldi reduces GMRES to a least-squares problem for the small Hessenberg matrix).
Proof
By [L1], every has the form , and for that one has
Since minimizes the right-hand side, the vector minimizes the left-hand side over the whole affine Krylov space. By [F1], that is exactly the GMRES condition.
For unrestarted GMRES, the residual norms are nonincreasing
Statement
Let be an unrestarted GMRES iterate at step . Then
Facts & Assumptions
Given: Unrestarted GMRES iterates and for the same initial guess .
Each GMRES iterate minimizes the residual norm over its affine Krylov space (GMRES minimizes the residual norm over the affine Krylov space ).
Proof
Because , one has . In particular, is an admissible competitor for the st minimization problem.
By [L1], minimizes the residual norm over the larger affine space, so
The GMRES residual has the form with and
Statement
Let be an th GMRES iterate for with initial residual , and let . Then there is a polynomial such that
Moreover, among all polynomials with and , GMRES chooses one minimizing .
Facts & Assumptions
Given: A GMRES iterate with residual .
Every vector in has the form with (The Krylov subspace consists exactly of the vectors for zero polynomials or polynomials of degree less than ).
GMRES minimizes the residual norm over (GMRES minimizes the residual norm over the affine Krylov space ).
Proof
Since , [L1] gives a polynomial with and . Therefore where . Then and .
Conversely, every polynomial with and can be written as with . The corresponding vector lies in by [L1], and its residual is . Since [L2] makes minimize that residual norm over all such , the polynomial attached in step 1.1 minimizes among all admissible .
In exact arithmetic, unrestarted GMRES terminates no later than the relative grade
Statement
Let be invertible, let be an initial guess, let , and let . In exact arithmetic, unrestarted GMRES produces a zero residual no later than step .
Facts & Assumptions
Given: An invertible matrix , an initial guess , the initial residual , and its grade .
The exact correction lies in (If is invertible, the exact solution of lies in the Krylov space at the grade).
GMRES minimizes the residual norm over (GMRES minimizes the residual norm over the affine Krylov space ).
Proof
Let be the exact solution of . Then , so [L1] gives .
The exact solution has residual . Since is an admissible point in the th GMRES affine space, [L2] forces the th GMRES residual norm to be at most , hence equal to . Therefore unrestarted GMRES terminates by step .
For a diagonalizable matrix, the GMRES residual bound carries the eigenvector-conditioning factor
Statement
Assume is diagonalizable, with . If is the th GMRES residual, then
where .
Facts & Assumptions
Given: A diagonalization and an th GMRES residual .
One has for some polynomial with and , and GMRES chooses such a polynomial minimizing the residual norm (The GMRES residual has the form with and ).
Proof
By [L1], Hence Since is diagonal, its operator norm is . Therefore
The polynomial from [L1] minimizes over all polynomials with and , so the bound from step 1.1 also holds after taking the minimum over that admissible class.
Restarted GMRES as repeated GMRES cycles on fixed-size Krylov spaces
Definition
Fix a restart length . Restarted GMRES produces iterates in cycles:
- start from ;
- at cycle , form the residual ;
- run ordinary GMRES for exactly steps with initial guess and initial residual ;
- call the resulting residual minimizer and restart from it.
Thus each cycle uses only the current residual and a fresh size- Krylov space.
Restarted GMRES preserves cyclewise residual minimization but loses the unrestarted finite-termination guarantee
Statement
In restarted GMRES, each cycle output minimizes the residual norm over the current affine space . However, after restarting one no longer minimizes over the full unrestarted space for large , so the unrestarted finite-termination theorem does not carry over automatically.
Facts & Assumptions
Given: A restarted GMRES cycle from with residual .
Restarted GMRES is defined by running ordinary GMRES for steps from the current iterate and then restarting from the cycle output (Restarted GMRES as repeated GMRES cycles on fixed-size Krylov spaces).
Ordinary GMRES minimizes the residual norm over its affine Krylov space (GMRES minimizes the residual norm over the affine Krylov space ).
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
By [F1], the st restarted iterate is obtained by applying ordinary GMRES to the system with initial guess and initial residual . Therefore [L1] gives
The unrestarted theorem [L2] uses the single expanding family . After a restart, [F1] replaces that space by the new cycle space , 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.
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 as stored data. It only needs:
- the ability to apply to chosen vectors ;
- vector additions and scalar multiples;
- inner products and norms, when the method orthogonalizes or measures a residual.
Thus Arnoldi and GMRES may be implemented from an operator routine for without explicitly assembling the full matrix.
5 · Examples, counterexamples and false statements
None yet.
Sources
- Yousef Saad, CSCI 8314 Set 13: Krylov subspace methods; Introduction; Krylov subspaces; Gram-Schmidt process (review); The Arnoldi process; FOM and GMRES
- Yousef Saad, Iterative Methods for Sparse Linear Systems — lecture notes on Arnoldi's method
- Lloyd N. Trefethen and David Bau III, Numerical Linear Algebra
- Yousef Saad, CSCI 8314 Set 14: Krylov subspace methods (continued); Practical variants: restarting and truncating; Hermitian case: The Lanczos algorithm; Conjugate gradients