GMRES Fails Silently From Stagnation

[packquickly]

GMRES often fails to converge when restart is less than the problem dimension, but will return without indicating failure.

This failure (likely due to stagnation) seems to depend on the absolute difference between the problem dimension and the Krylov subspace dimension (restart.) ie. roughly 30% of the time GMRES will fail silently on a problem of dimension 25 if restart = 24, and will still fail 30% of the time with a problem of dimension 100 if restart = 99. For a difference in dimension of 5, the test below returns an incorrect value greater than 90% of the time.

Reproducing code:

import KrylovKit: linsolve, GMRES
import LinearAlgebra: cond, norm

for problem_dim in [25, 100]
    for excess_dim in [0, 1, 5]
        succeeded = 0
        restart = problem_dim - excess_dim
        for i = 1:100 
            condition_number = Inf
            matrix = nothing
            while condition_number > 1000
                matrix = randn(Float64, (problem_dim, problem_dim))
                condition_number = cond(matrix)
            end

            true_vec = randn(Float64, (problem_dim,))
            b = matrix * true_vec
            x_0 = zero(b)

            julia_soln, _ = linsolve(x->matrix*x, b, x_0, GMRES(krylovdim=restart))

            residual_norm = norm(julia_soln - true_vec)

            succeeded += (residual_norm < 1)  # very loose tolerance!
        end
        println("problem dim: $problem_dim. restart dim: $restart. succeeded: $succeeded")
    end
end

[Jutho]

Is this a problem that you observe specifically with my GMRES implementation, or is it due to the algorithm (which is beyond my control). That it doesn't error upon failing to converge is documented (check ? linsolve); if you increase the verbosity level, it will print an information message. There is also no need to do x -> matrix*x, you can just insert the matrix.

[packquickly]

It is every implementation of GMRES I have encountered. See also:
JuliaLinearAlgebra/IterativeSolvers.jl#338
JuliaSmoothOptimizers/Krylov.jl#729

[Jutho]

I guess this tells you something. I am not an expert on the convergence theory of iterative linear solvers, but from practical experience, I would say that, unfortunately, you need some insight into the structure of your problem, and it is not the case that there is one algorithm that works for all use cases (this is still an active field of research, with novel methods still being developed). There is also BiCG, BiCGStab, … SciPy furthermore has an implementation of an algorithm known as "LGMRES", which I would at some point like to implement. Would be interesting to see how your "random" test set works with that algorithm.

[Jutho]

Is there anything left to do here; if not I will close this issue?