Classical Gram-Schmidt with DGKS re-orthogonalization criterion

[GiggleLiu]

In Gram-Schmidt iterations, re-orthogonalization is often required to enforce the obtained spanning space is correct up to some precision in many practical applications.

https://www.cs.cornell.edu/~bindel/class/cs6210-f16/lec/2016-11-16.pdf

I didn't find such procedure in this repo yet. Is it because it is not needed in implementing expmv, or is it simply not implemented yet or have I missed it? I am happy to submit a PR if re-orthogonalization is really needed.

[YingboMa]

It doesn't need to be reorthorgonalized. We are using the modified Gram-Schmidt process. Also, if you want to add classical GS with DGKS reorthogonalization criterion 1. There is one in IterativeSolvers.jl https://github.com/JuliaMath/IterativeSolvers.jl/blob/master/src/orthogonalize.jl#L12.

[GiggleLiu]

Sorry, I mean here： https://github.com/SciML/ExponentialUtilities.jl/blob/ebb828b44a1a30326d6323af463f277db911a419/src/arnoldi.jl#L182

orthogonalize_and_normalize! is not used.

[YingboMa]

It doesn't need to be reorthorgonalized. Modified Gram-Schmidt is stable.

[GiggleLiu]

Yes, indeed. You are using modified GS. I missed it.

The complexity of MGS is kn^2, using icgsm is more efficient http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.67.9572&rep=rep1&type=pdf#page86 Page 72.

Wondering if the performance of GM is important in this case?

[ChrisRackauckas]

Do a profile on https://benchmarks.sciml.ai/html/MOLPDE/allen_cahn_fdm_wpd.html to see whether it's an important part.

[GiggleLiu]

Thanks, @ChrisRackauckas . This is a very nice notebook where I can find nice examples to benchmark.

[ChrisRackauckas]

Note that all of our benchmarking sources are at https://github.com/SciML/DiffEqBenchmarks.jl

[GiggleLiu]

ICGS seems to be more stable than modified-GS according to the following benchmark, the lanczos procedure may have the same issue. But ICGS is a bit slower than MGS. Let's keep this issue open and see if someone complains the precision problem.

The x-axis is the size of matrix to be orthogonalized.

The is the code for this plot,

"""
    icgs(u::AbstractVector, Q, M=nothing; alpha=0.5, maxit=3)

Iterative Classical M-orthogonal Gram-Schmidt orthogonalization.
`A` is a matrix to be orthogonalized,
`M` is a matrix or nothing, if not nothing, perform M-orthogonal.
"""
function icgs!(A::AbstractMatrix, j, M=nothing; alpha=0.5, maxit=3)
    u = @view A[:,j]
    Q = @view A[:,1:j-1]
    Mu = _dot(M, u)
    r_pre = sqrt(abs(dot(u, Mu)))
    local r1
    for it = 1:maxit
        u .-= Q * (Q' * Mu)
        Mu = _dot(M, u)
        r1 = sqrt(abs(dot(u, Mu)))
        if r1 > alpha * r_pre
            break
        end
        r_pre = r1
    end
    if r1 <= alpha * r_pre
        @warn "loss of orthogonality @icgs."
    end
end

_dot(::Nothing, b) = b
_dot(A::AbstractMatrix, b) = A*b

using LinearAlgebra, Test, Random

function generate_q(n, seed=2)
    Random.seed!(2)
    H = randn(ComplexF64, n, n)
    H = H + H'
    v = randn(ComplexF64, n)
    qs = []
    for i=1:n
        push!(qs, v)
        v = H * v
    end
    hcat(qs...)
end

@testset "icgs" begin
    n = 10
    q = generate_q(n, 2)
    for j = 1:n
        icgs!(q, j)
        @views q[:,j] ./= norm(q[:,j])
    end
    @test q' * q ≈ I
    @show sum(abs, q' * q - I)
end

function mgs!(V::AbstractMatrix, j)
    y = @view(V[:, j])
    @inbounds for i = 1:j-1
        α = dot(@view(V[:, i]), y)
        axpy!(-α, @view(V[:, i]), y)
    end
    return y
end

@testset "mgs" begin
    n = 10
    q = generate_q(n, 2)
    for j = 1:n
        mgs!(q, j)
        @views q[:,j] ./= norm(q[:,j])
    end
    @test q' * q ≈ I
    @show sum(abs, q' * q - I)
end

function get_eps(method, seed, ns)
    out = []
    for n = ns
        q = generate_q(n, seed)
        for j = 1:n
            method(q, j)
            @views q[:,j] ./= norm(q[:,j])
        end
        push!(out, sum(abs, q' * q - I))
    end
    out
end

ns = 1:10:200
s1 = get_eps(icgs!, 2, ns)
s2 = get_eps(mgs!, 2, ns)

using Plots
plot(ns, s1, yscale=:log10, label="icgs")
plot!(ns, s2, label="mgs!")
ylabel!("error")