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Fix solver options for Real space #5144

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Fix solver options for Real space #5144

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2 changes: 1 addition & 1 deletion firedrake/solving_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@ def set_defaults(solver_parameters, arguments, *, ksp_defaults=None, snes_defaul
if "pc_type" in keys:
# Might reasonably expect to get petsc defaults
skip.update({"pc_factor_mat_solver_type", "ksp_type"})
if parameters.get("mat_type") in {"matfree", "nest"}:
if parameters.get("mat_type") in {"matfree", "nest", "python"}:
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@pbrubeck pbrubeck May 28, 2026

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Whoever populated this dictionary with {"mat_type": A.type} within the adjoint should have set {"mat_type": "matfree" if A.type == "python" else A.type}.

Or more correclty, the matrix type should come from _SNESContext.mat_type

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@pbrubeck pbrubeck May 29, 2026 •
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I think the logic here could be responsible, but not 100% sure

firedrake/firedrake/matrix.py

Lines 22 to 27 in 2018173

def _get_mat_type(petscmat: PETSc.Mat) -> str:
"""Maps PETSc matrix types to Firedrake notation"""
mat_type = petscmat.getType()
if mat_type.startswith("seq") or mat_type.startswith("mpi"):
return mat_type[3:]
return mat_type

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@connorjward connorjward May 29, 2026

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Yeah that's what I found. I think we should:

  1. Intercept mat_type=python
  2. Extract the Python context from the mat
  3. Use that to return matfree, denserow etc

# Non-LU defaults.
ksp_defaults["ksp_type"] = "gmres"
ksp_defaults["pc_type"] = "jacobi"
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@pbrubeck pbrubeck May 28, 2026

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This might be unrelated, but MUMPS supports MatNest when all blocks are aij. We shouldn't default to jacobi in that case.

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@connorjward connorjward May 29, 2026

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Yeah that's definitely unrelated to this. But we should open an issue for it at least.

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20 changes: 20 additions & 0 deletions tests/firedrake/adjoint/test_solving.py
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Expand Up @@ -314,6 +314,26 @@ def test_two_nonlinear_solves():
assert rf.tape.recompute_count == 5


@pytest.mark.skipcomplex
def test_real_solve(rg):
mesh = UnitSquareMesh(8, 8)
V = FunctionSpace(mesh, "CG", 1)
R = FunctionSpace(mesh, "R", 0)

u = TrialFunction(R)
v = TestFunction(R)
m = Function(V).assign(1.0)

def J(m):
a = u * v * dx
L = m * v * dx
solution = Function(R)
solve(a == L, solution)
return assemble(solution**2 * dx)

_test_adjoint(J, m, rg)


def convergence_rates(E_values, eps_values):
from numpy import log
r = []
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