KrylovKit.jl

A Julia package collecting a number of Krylov-based algorithms for linear problems, singular value and eigenvalue problems and the application of functions of linear maps or operators to vectors.

Overview

KrylovKit.jl accepts general functions or callable objects as linear maps, and general Julia objects with vector like behavior (see below) as vectors.

The high level interface of KrylovKit is provided by the following functions:

Package features and alternatives

This section could also be titled "Why did I create KrylovKit.jl"?

There are already a fair number of packages with Krylov-based or other iterative methods, such as

These packages have certainly inspired and influenced the development of KrylovKit.jl. However, KrylovKit.jl distinguishes itself from the previous packages in the following ways:

1. KrylovKit accepts general functions to represent the linear map or operator that defines the problem, without having to wrap them in a LinearMap or LinearOperator type. Of course, subtypes of AbstractMatrix are also supported. If the linear map (always the first argument) is a subtype of AbstractMatrix, matrix vector multiplication is used, otherwise it is applied as a function call.

2. KrylovKit does not assume that the vectors involved in the problem are actual subtypes of AbstractVector. Any Julia object that behaves as a vector is supported, so in particular higher-dimensional arrays or any custom user type that supports the following functions (with v and w two instances of this type and α, β scalars (i.e. Number)):

• Base.:*(α, v): multiply v with a scalar α, which can be of a different scalar type; in particular this method is used to create vectors similar to v but with a different type of underlying scalars.
• Base.similar(v): a way to construct vectors which are exactly similar to v
• LinearAlgebra.mul!(w, v, α): out of place scalar multiplication; multiply vector v with scalar α and store the result in w
• LinearAlgebra.rmul!(v, α): in-place scalar multiplication of v with α; in particular with α = false, v is the corresponding zero vector
• LinearAlgebra.axpy!(α, v, w): store in w the result of α*v + w
• LinearAlgebra.axpby!(α, v, β, w): store in w the result of α*v + β*w
• LinearAlgebra.dot(v,w): compute the inner product of two vectors
• LinearAlgebra.norm(v): compute the 2-norm of a vector

Algorithms in KrylovKit.jl are tested against such a minimal implementation (named MinimalVec) in the test suite. This type is only defined in the tests. However, KrylovKit provides two types implementing this interface and slightly more, to make them behave more like AbstractArrays (e.g. also Base.:+ etc), which can facilitate certain applications:

• RecursiveVec can be used for grouping a set of vectors into a single vector like structure (can be used recursively). This is more robust than trying to use nested Vector{<:Vector} types.
• InnerProductVec can be used to redefine the inner product (i.e. dot) and corresponding norm (norm) of an already existing vector like object. The latter should help with implementing certain type of preconditioners.

Current functionality

The following algorithms are currently implemented

• linsolve: CG, GMRES
• eigsolve: a Krylov-Schur algorithm (i.e. with tick restarts) for extremal eigenvalues of normal (i.e. not generalized) eigenvalue problems, corresponding to Lanczos for real symmetric or complex hermitian linear maps, and to Arnoldi for general linear maps.
• geneigsolve: an customized implementation of the inverse-free algorithm of Golub and Ye for symmetric / hermitian generalized eigenvalue problems with positive definite matrix B in the right hand side of the generalized eigenvalue problem $A v = B v λ$. The Matlab implementation was described by Money and Ye and is known as EIGIFP; in particular it extends the Krylov subspace with a vector corresponding to the step between the current and previous estimate, analogous to the locally optimal preconditioned conjugate gradient method (LOPCG). In particular, with Krylov dimension 2, it becomes equivalent to the latter.
• svdsolve: finding largest singular values based on Golub-Kahan-Lanczos bidiagonalization (see GKL)
• exponentiate: a Lanczos based algorithm for the action of the exponential of a real symmetric or complex hermitian linear map.
• expintegrator: exponential integrator for a linear non-homogeneous ODE, computes a linear combination of the ϕⱼ functions which generalize ϕ₀(z) = exp(z).

Future functionality?

Here follows a wish list / to-do list for the future. Any help is welcomed and appreciated.

• More algorithms, including biorthogonal methods:
• for linsolve: MINRES, BiCG, BiCGStab(l), IDR(s), ...
• for eigsolve: BiLanczos, Jacobi-Davidson JDQR/JDQZ, subspace iteration (?), ...
• for geneigsolve: trace minimization, ...
• Support both in-place / mutating and out-of-place functions as linear maps
• Reuse memory for storing vectors when restarting algorithms (related to previous)
• Support non-BLAS scalar types using GeneralLinearAlgebra.jl and GeneralSchur.jl
• Least square problems
• Nonlinear eigenvalue problems
• Preconditioners
• Refined Ritz vectors, Harmonic Ritz values and vectors
• Block versions of the algorithms
• More relevant matrix functions

Partially done:

• Improved efficiency for the specific case where x is Vector (i.e. BLAS level 2 operations): any vector v::AbstractArray which has IndexStyle(v) == IndexLinear() now benefits from a multithreaded (use export JULIA_NUM_THREADS = x with x the number of threads you want to use) implementation that resembles BLAS level 2 style for the vector operations, provided ClassicalGramSchmidt(), ClassicalGramSchmidt2() or ClassicalGramSchmidtIR() is chosen as orthogonalization routine.