ArnoldiMethodTransformations.jl

Shift-and-invert transformations for ArnoldiMethod.jl
Author wrs28
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Updated Last
7 Months Ago
Started In
March 2019

ArnoldiMethodTransformations

A package for easily interfacing with ArnoldiMethod, using the suggested transformations suggested in the documentation.

Installation

In REPL, type either ] add ArnoldiMethodTransformations or

using Pkg
Pkg.add("ArnoldiMethodTransformations")

This package mainly extends some methods of ArnoldiMethod, which needs to be separately installed. It exports three constants: USOLVER, PSOLVER, and MSOLVER, used to indicate whether to use UMFPACK, Pardiso, or MUMPS.

Example

Ordinary eigenvalue problem Ax=λx

using LinearAlgebra
using ArnoldiMethod
using ArnoldiMethodTransformations

# construct fixed eval matrix in random basis
D = diagm(0=>[0,1,2,3,4,5,6,7,8,9])
S = randn(10,10)
A = S\D*S

# find eigenpairs closest to 5.001 (cannot be 5 as algorithm is unstable if σ is exactly an eval)
σ = 5.001
decomp, hist = partialschur(A,σ)

# get evecs
λ, v = partialeigen(decomp,σ)

display(λ)
norm(A*v-v*diagm(0=>λ))
# should be ~1e-11 or smaller

Generalized eigenvalue problem Ax=λBx

using LinearAlgebra
using ArnoldiMethod
using ArnoldiMethodTransformations

# construct fixed eval matrix in random basis
A = rand(ComplexF64,10,10)
B = rand(ComplexF64,10,10)

# find eigenpairs closest to .5
σ = .5
decomp, hist = partialschur(A,B,σ)

# get evecs
λ, v = partialeigen(decomp,σ)

display(λ)
norm(A*v-B*v*diagm(0=>λ))
# should be ~1e-14 or smaller

Note that in both cases, ArnoldiMethod needed to be explicitly brought into scope with using.

Methods

This package exports none of its own methods, but extends partialschur and partialeigen from ArnoldiMethod.

It does export three constants: USOLVER, PSOLVER, MSOLVER.


`partialschur(A, [B], σ; [diag_inv_B, lupack=USOLVER, kwargs...]) -> decomp, history`

Partial Schur decomposition of A, with shift σ and mass matrix B, solving A*v=(λ-σ)*B*v for its smallest eigenvalues.

Keyword diag_inv_B defaults to true if B is both diagonal and invertible. This enables a simplified shift-and-invert scheme.

Keyword lupack determines what linear algebra library to use. Options are USOLVER (UMFPACK, the default), PSOLVER (Pardiso), and the default MSOLVER (MUMPS).

The relevant solver must be explicitly loaded at the top level to use it (e.g., using Pardiso must be called before lupack=PSOLVER can be used).

For other keywords, see ArnoldiMethod.partialschur


partialeigen(decomp, σ) -> λ, v

Transforms a partial Schur decomposition into an eigendecomposition, outputting evals λ and evecs v. It undoes the shift-and-invert of the eigenvalues by σ.


Note that the shifting to an exact eigenvalue poses a problem, see note on purification.

Linear Solvers

There are two solvers currently available for use in this package: UMFPACK (via Base.LinAlg), and Pardiso (via Pardiso).

Pardiso is often faster, and uses significantly less memory, but require separate installation, which not all users will want to do. This optional dependency is implemented with Requires.jl.

The default solver is UMFPACK. To use another solver, such as Pardiso (assuming it is installed), use the keyword :lupack=PSOLVER in partialschur.

To do: add MUMPS to the available solvers.

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