Iterative solvers as iterators

In advanced use cases you might want to access the internal data structures of the solver, inject code to be run after each iteration, have total control over allocations or reduce overhead in initialization. The iterator approach of IterativeSolvers.jl makes this possible.


At this point BiCGStab(l), CG, Chebyshev, GMRES, MINRES, QMR and the stationary methods are implemented as iterators. However, the package does not yet export the iterators and helper methods themselves.

How iterators are implemented

The solvers listed above are basically a thin wrapper around an iterator. Among other things, they initialize the iterable, loop through the iterator and return the result:

function my_solver!(x, A, b)
    iterable = MySolverIterable(x, A, b)
    for item in iterable end
    return iterable.x

Rather than calling my_solver!(x, A, b), you could also initialize the iterable yourself and perform the for loop.

Example: avoiding unnecessary initialization

The Jacobi method for SparseMatrixCSC has some overhead in initialization; not only do we need to allocate a temporary vector, we also have to search for indices of the diagonal (and check their values are nonzero). The current implementation initializes the iterable as:

jacobi_iterable(x, A::SparseMatrixCSC, b; maxiter::Int = 10) =
    JacobiIterable{eltype(x), typeof(x)}(OffDiagonal(A, DiagonalIndices(A)), x, similar(x), b, maxiter)

Now if you apply Jacobi iteration multiple times with the same matrix for just a few iterations, it makes sense to initialize the iterable only once and reuse it afterwards:

julia> using LinearAlgebra, SparseArrays, IterativeSolvers

julia> A = spdiagm(-1 => -ones(3), 0 => 2*ones(4), 1 => -ones(3));

julia> b1 = [1.0, 2, 3, 4];

julia> b2 = [-1.0, 1, -1, 1];

julia> x = [0.0, -1, 1, 0];

julia> my_iterable = IterativeSolvers.jacobi_iterable(x, A, b1, maxiter = 2);

julia> norm(b1 - A * x) / norm(b1)

julia> for item in my_iterable
           println("Iteration for rhs 1")
Iteration for rhs 1
Iteration for rhs 1

julia> norm(b1 - A * x) / norm(b1)

julia> # Copy the next right-hand side into the iterable
       copyto!(my_iterable.b, b2);

julia> norm(b2 - A * x) / norm(b2)

julia> for item in my_iterable
           println("Iteration for rhs 2")
Iteration for rhs 2
Iteration for rhs 2

julia> norm(b2 - A * x) / norm(b2)

Other use cases

Other use cases include:

  • computing the (harmonic) Ritz values from the Hessenberg matrix in GMRES;
  • comparing the approximate residual of methods such as GMRES and BiCGStab(l) with the true residual during the iterations;
  • updating a preconditioner in flexible methods.