def solve(self):
residual = self.problem.residual_vector_eval
initial_guess_vector = self.problem.solution.vector()
jacobian = _Jacobian(self.problem.jacobian_matrix_eval)
try:
solution_vector, info = nonlin_solve(
residual,
initial_guess_vector,
jacobian=jacobian,
verbose=self._report,
f_tol=self._absolute_tolerance,
f_rtol=self._relative_tolerance,
x_rtol=self._solution_tolerance,
maxiter=self._maximum_iterations,
line_search=self._line_search,
callback=self._monitor,
full_output=True,
raise_exception=False)
if self._report:
if info["success"]:
print("scipy solver converged in " + str(info["nit"]) +
" iterations.")
else:
print("scipy solver diverged in " + str(info["nit"]) +
" iterations.")
self.problem.solution.vector()[:] = solution_vector
except ArithmeticError as error:
if self._report:
print("scipy solver diverged due to arithmetic error " +
str(error))
self.monitor(self.problem.solution)
def _check(self, jac, N, maxiter, complex=False, **kw):
np.random.seed(123)
A = np.random.randn(N, N)
if complex:
A = A + 1j * np.random.randn(N, N)
b = np.random.randn(N)
if complex:
b = b + 1j * np.random.randn(N)
def func(x):
return dot(A, x) - b
sol = nonlin.nonlin_solve(func, np.zeros(N), jac, maxiter=maxiter, f_tol=1e-6, line_search=None, verbose=0)
assert_(np.allclose(dot(A, sol), b, atol=1e-6))
def _check(self, jac, N, maxiter, complex=False, **kw):
np.random.seed(123)
A = np.random.randn(N, N)
if complex:
A = A + 1j*np.random.randn(N, N)
b = np.random.randn(N)
if complex:
b = b + 1j*np.random.randn(N)
def func(x):
return dot(A, x) - b
sol = nonlin.nonlin_solve(func, b*0, jac, maxiter=maxiter,
f_tol=1e-6, line_search=None, verbose=0)
assert_(np.allclose(dot(A, sol), b, atol=1e-6))
