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scipy L-BFGS silently converts to float, so view as floats when dealing with it.'

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jan 2017-12-27 01:44:45 -08:00
parent a70687f5e3
commit 66712efd49
1 changed files with 18 additions and 6 deletions
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24
fdfd_tools/bloch.py
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@ -392,7 +392,7 @@ def eigsolve(num_modes: int,
onto the space orthonormal to Z. If approx_grad is True, the approximate onto the space orthonormal to Z. If approx_grad is True, the approximate
inverse of the maxwell operator is used to precondition the gradient. inverse of the maxwell operator is used to precondition the gradient.
""" """
z = Z.reshape(y_shape) z = Z.view(dtype=complex).reshape(y_shape)
U = numpy.linalg.inv(z.conj().T @ z) U = numpy.linalg.inv(z.conj().T @ z)
zU = z @ U zU = z @ U
AzU = scipy_op @ zU AzU = scipy_op @ zU
@ -402,26 +402,35 @@ def eigsolve(num_modes: int,
df_dy = scipy_iop @ (AzU - zU @ zTAzU) df_dy = scipy_iop @ (AzU - zU @ zTAzU)
else: else:
df_dy = (AzU - zU @ zTAzU) df_dy = (AzU - zU @ zTAzU)
return numpy.abs(f), numpy.sign(f) * numpy.real(df_dy).ravel()
df_dy_flat = df_dy.view(dtype=float).ravel()
return numpy.abs(f), numpy.sign(f) * df_dy_flat
''' '''
Use the conjugate gradient method and the approximate gradient calculation to Use the conjugate gradient method and the approximate gradient calculation to
quickly find approximate eigenvectors. quickly find approximate eigenvectors.
''' '''
result = scipy.optimize.minimize(rayleigh_quotient, result = scipy.optimize.minimize(rayleigh_quotient,
numpy.random.rand(*y_shape), numpy.random.rand(*y_shape, 2),
jac=True, jac=True,
method='L-BFGS-B', method='L-BFGS-B',
tol=1e-20, tol=1e-20,
options={'maxiter': 2000, 'gtol':0, 'ftol':1e-20 , 'disp':True})#, 'maxls':80, 'm':30}) options={'maxiter': 2000, 'gtol':0, 'ftol':1e-20 , 'disp':True})#, 'maxls':80, 'm':30})
result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, True),
result.x,
jac=True,
method='L-BFGS-B',
tol=1e-20,
options={'maxiter': 2000, 'gtol':0, 'disp':True})
result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False), result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False),
result.x, result.x,
jac=True, jac=True,
method='L-BFGS-B', method='L-BFGS-B',
tol=1e-20, tol=1e-20,
options={'maxiter': 2000, 'ptol':1e-18, 'disp':True}) options={'maxiter': 2000, 'gtol':0, 'disp':True})
for i in range(20): for i in range(20):
result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False), result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False),
@ -429,10 +438,13 @@ def eigsolve(num_modes: int,
jac=True, jac=True,
method='L-BFGS-B', method='L-BFGS-B',
tol=1e-20, tol=1e-20,
options={'maxiter': 70, 'gtol':1e-18, 'disp':True}) options={'maxiter': 70, 'gtol':0, 'disp':True})
if result.nit == 0:
# We took 0 steps, so re-running won't help
break
z = result.x.reshape(y_shape) z = result.x.view(dtype=complex).reshape(y_shape)
''' '''
Recover eigenvectors from Z Recover eigenvectors from Z