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haampie avatar haampie commented on June 1, 2024

Hey, yes, I should still write the utility functions. Right now the API is more or less similar to ARPACK (a function for matrix -> schur and one that does some post-processing with partial schur -> eigen). The eigs function in Arpack.jl is actually implemented in Julia itself and does more or less what you have there.

from arnoldimethod.jl.

jpfairbanks avatar jpfairbanks commented on June 1, 2024

Here is the version that is working for me in LG.

function eigs(A; kwargs...)
    schr =partialschur(A; kwargs...)
    vals, vectors = partialeigen(schr[1])
    reved = (kwargs[:which] == LR() || kwargs[:which] == LM())
    k = get(kwargs, :nev, length(vals))
    k = min(k, length(vals))
    perm = 1:k
    if vals[1] isa(Real)
        perm = sortperm(vals, rev=reved)
        perm = perm[1:k]
    end
    λ = vals[perm]
    Q = vectors[:, perm]
    return λ, Q
end

I can make a PR. I don't know about the other corner cases. These work for my needs which are mostly limited to Adjacency and Laplacian Matrices of graphs.

from arnoldimethod.jl.

haampie avatar haampie commented on June 1, 2024

So, the order of the eigenvalues has been fixed now when calling partialschur. Also the eigenvalues appear in the PartialSchur struct as decomp.eigenvalues. Further, partialeigen retains the order.

In the end eigs is basically no different from calling partialeigen(partialschur(...)[1]) then. I would be fine with a PR that implements this! Maybe it's good to throw when the method did not converge (?).

from arnoldimethod.jl.

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