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