meanas/fdfd/solvers.py

@@ -44,7 +44,7 @@ def _scipy_qmr(
         nonlocal ii
         ii += 1
         if ii % 100 == 0:
-            cur_norm = norm(A @ xk - b) / norm(b)
+            cur_norm = norm(A @ xk - b)
             logger.info(f'Solver residual at iteration {ii} : {cur_norm}')
 
     if 'callback' in kwargs:
@@ -69,13 +69,11 @@ def generic(
         J: vcfdfield_t,
         epsilon: vfdfield_t,
         mu: vfdfield_t | None = None,
-        *,
         pec: vfdfield_t | None = None,
         pmc: vfdfield_t | None = None,
         adjoint: bool = False,
         matrix_solver: Callable[..., ArrayLike] = _scipy_qmr,
         matrix_solver_opts: dict[str, Any] | None = None,
-        E_guess: vcfdfield_t | None = None,
         ) -> vcfdfield_t:
     """
     Conjugate gradient FDFD solver using CSR sparse matrices.
@@ -102,8 +100,6 @@ def generic(
                  which doesn't return convergence info and logs the residual
                  every 100 iterations.
         matrix_solver_opts: Passed as kwargs to `matrix_solver(...)`
-        E_guess: Guess at the solution E-field. `matrix_solver` must accept an
-                `x0` argument with the same purpose.
 
     Returns:
         E-field which solves the system.
@@ -124,13 +120,6 @@ def generic(
         A = Pl @ A0 @ Pr
         b = Pl @ b0
 
-    if E_guess is not None:
-        if adjoint:
-            x0 = Pr.H @ E_guess
-        else:
-            x0 = Pl @ E_guess
-        matrix_solver_opts['x0'] = x0
-
     x = matrix_solver(A.tocsr(), b, **matrix_solver_opts)
 
     if adjoint: