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Use L-BFGS instead of CG, and remove rayleigh iteration refinement

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scipy CG doesn't seem to converge as well as L-BFGS... worth looking
into later
This commit is contained in:
jan 2017-12-21 20:11:30 -08:00
parent 16f97d7f6b
commit 85030448c3
1 changed files with 23 additions and 23 deletions
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46
fdfd_tools/bloch.py
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@ -359,6 +359,8 @@ def eigsolve(num_modes: int,
"""
h_size = 2 * epsilon[0].size
kmag = norm(G_matrix @ k0)
'''
Generate the operators
'''
@ -409,16 +411,26 @@ def eigsolve(num_modes: int,
result = scipy.optimize.minimize(rayleigh_quotient,
numpy.random.rand(*y_shape),
jac=True,
method='CG',
tol=1e-5,
options={'maxiter': 30, 'disp':True})
method='L-BFGS-B',
tol=1e-20,
options={'maxiter': 2000, 'gtol':0, 'ftol':1e-20 , 'disp':True})#, 'maxls':80, 'm':30})
result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False),
result.x,
jac=True,
method='CG',
tol=1e-13,
options={'maxiter': 100, 'disp':True})
method='L-BFGS-B',
tol=1e-20,
options={'maxiter': 2000, 'ptol':1e-18, 'disp':True})
for i in range(20):
result = scipy.optimize.minimize(lambda y: rayleigh_quotient(y, False),
result.x,
jac=True,
method='L-BFGS-B',
tol=1e-20,
options={'maxiter': 70, 'gtol':1e-18, 'disp':True})
z = result.x.reshape(y_shape)
@ -436,25 +448,13 @@ def eigsolve(num_modes: int,
v = eigvecs[:, i]
n = eigvals[i]
v /= norm(v)
logger.info('eigness {}: {}'.format(i, norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v )))
eigness = norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v )
f = numpy.sqrt(-numpy.real(n))
df = numpy.sqrt(-numpy.real(n + eigness))
neff_err = kmag * (1/df - 1/f)
logger.info('eigness {}: {}\n neff_err: {}'.format(i, eigness, neff_err))
ev2 = eigvecs.copy()
for i in range(len(eigvals)):
logger.info('Refining eigenvector {}'.format(i))
eigvals[i], ev2[:, i] = rayleigh_quotient_iteration(scipy_op,
guess_vector=eigvecs[:, i],
iterations=40,
tolerance=tolerance * numpy.real(numpy.sqrt(eigvals[i])) * 2,
solver = lambda A, b: spalg.bicgstab(A, b, maxiter=200)[0])
eigvecs = ev2
order = numpy.argsort(numpy.abs(eigvals))
for i in range(len(eigvals)):
v = eigvecs[:, i]
n = eigvals[i]
v /= norm(v)
logger.info('eigness {}: {}'.format(i, norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v )))
return eigvals[order], eigvecs.T[order]