# ================================== LICENSE ===================================
# Magnopy - Python package for magnons.
# Copyright (C) 2023-2026 Magnopy Team
#
# e-mail: anry@uv.es, web: magnopy.org
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#
# ================================ END LICENSE =================================
import numpy as np
from numpy.linalg import LinAlgError
from magnopy._exceptions import ColpaFailed
# Save local scope at this moment
old_dir = set(dir())
old_dir.add("old_dir")
def _check_grand_dynamical_matrix(D):
r"""
Check that grand dynamical matrix is (2N, 2N) matrix
Parameters
----------
D : |array-like|_
Candidate for the grand dynamical matrix
Returns
-------
D : (2N, 2N) :numpy:`ndarray`
Grand dynamical matrix.
N : int
Raises
------
ValueError
If the check is not passed
"""
D = np.array(D)
if len(D.shape) != 2:
raise ValueError(f"Grand dynamical matrix is not 2-dimensional, got {D.shape}.")
if D.shape[0] != D.shape[1]:
raise ValueError(f"Grand dynamical matrix is not square, got {D.shape}.")
if D.shape[0] % 2 != 0:
raise ValueError(
f"Size of the grand dynamical matrix is not even, got {D.shape}."
)
return D, D.shape[0] // 2
def _inverse_by_colpa(matrix):
# Compute G from G^-1 (or vise versa) following Colpa, see equation (3.7) for details
N = matrix.shape[0] // 2
matrix = np.conjugate(matrix).T
matrix[:N, N:] *= -1
matrix[N:, :N] *= -1
return matrix
[docs]
def solve_via_colpa(D):
r"""
Diagonalizes grand-dynamical matrix following the method of Colpa.
An algorithm is described in section 3, remark 1 of [1]_.
Solves the Bogoliubov Hamiltonian of the form
.. math::
\hat{H} = \sum_{r^{\prime}, r = 1}^m
a_{r^{\prime}}^{\dagger}\boldsymbol{\Delta}_1^{r^{\prime}r}a_r +
a_{r^{\prime}}^{\dagger}\boldsymbol{\Delta}_2^{r^{\prime}r}a_{m+r}^{\dagger} +
a_{m+r^{\prime}}^{\dagger}\boldsymbol{\Delta}_3^{r^{\prime}r}a_r +
a_{m+r^{\prime}}^{\dagger}\boldsymbol{\Delta}_4^{r^{\prime}r}a_{m+r}^{\dagger}
ensuring the bosonic commutation relations.
In a matrix form the Hamiltonian can be written as
.. math::
\hat{H} = \boldsymbol{\cal A}^{\dagger} \boldsymbol{D} \boldsymbol{\cal A}
where
.. math::
\boldsymbol{\cal A} =
\begin{pmatrix}
a_1 \\
\cdots \\
a_m \\
a_{m+1} \\
\cdots \\
a_{2m} \\
\end{pmatrix}
After diagonalization the Hamiltonian has the form
.. math::
\hat{H}
=
\boldsymbol{\cal B}^{\dagger} \boldsymbol{\mathcal{E}} \boldsymbol{\cal B}
where
.. math::
\boldsymbol{\cal B} =
\begin{pmatrix}
b_1 \\
\cdots \\
b_m \\
b_{m+1} \\
\cdots \\
b_{2m} \\
\end{pmatrix}
Parameters
----------
D : (2N, 2N) |array-like|_
Grand dynamical matrix. If it is Hermitian and positive-defined, then obtained
eigenvalues are positive and real.
.. math::
\boldsymbol{\mathcal{D}} = \begin{pmatrix}
\boldsymbol{\Delta_1} & \boldsymbol{\Delta_2} \\
\boldsymbol{\Delta_3} & \boldsymbol{\Delta_4}
\end{pmatrix}
Returns
-------
E : (2N,) :numpy:`ndarray`
The eigenvalues. It is an array of the diagonal elements of the diagonal matrix
:math:`\boldsymbol{\mathcal{E}}`. First N elements correspond to the
:math:`b^{\dagger}_1b_1, \dots, b^{\dagger}_mb_m` and last N elements - to
the :math:`b^{\dagger}_{m+1}b_{m+1}, \dots, b^{\dagger}_{2m}b_{2m}`.
First N eigenvalues are sorted in descending order, while last N eigenvalues are
sorted in ascending order.
.. math::
\boldsymbol{\mathcal{E}}
=
(\boldsymbol{G}^{\dagger})^{-1} \boldsymbol{D} \boldsymbol{G}^{-1}
G : (2N, 2N) :numpy:`ndarray`
Transformation matrix, that changes the basis from the original set of bosonic
operators :math:`\boldsymbol{a}` to the set of new bosonic operators
:math:`\boldsymbol{b}` which diagonalize the Hamiltonian:
.. math::
\boldsymbol{\cal B} = \boldsymbol{G} \boldsymbol{\cal A}
The rows are ordered in the same way as the eigenvalues.
Raises
------
ColpaFailed
If the algorithm fails. Typically it means that the grand dynamical matrix
:math:`\boldsymbol{D}` is not positive-defined.
ValueError
If the grand dynamical matrix is not square or its shape is not even.
References
----------
.. [1] Colpa, J.H.P., 1978.
Diagonalization of the quadratic boson hamiltonian.
Physica A: Statistical Mechanics and its Applications,
93(3-4), pp.327-353.
Examples
--------
For already diagonal matrix this function does not do much
.. doctest::
>>> import magnopy
>>> D = [[1, 0], [0, 2]]
>>> E, G = magnopy.solve_via_colpa(D)
>>> E
array([1., 2.])
>>> G
array([[ 1., -0.],
[-0., 1.]])
.. doctest::
>>> import magnopy
>>> D = [[1, 1j], [-1j, 2]]
>>> E, G = magnopy.solve_via_colpa(D)
>>> E
array([0.61803399+0.j, 1.61803399+0.j])
>>> G
array([[ 1.08204454-0.j , 0. +0.41330424j],
[-0. -0.41330424j, 1.08204454-0.j ]])
>>> E, G = magnopy.solve_via_colpa(D)
>>> E
array([0.61803399+0.j, 1.61803399+0.j])
>>> G # doctest: +SKIP
array([[1.08204454+0.j , 0. -0.41330424j],
[0. +0.41330424j, 1.08204454+0.j ]])
"""
# Guarantee that D is 2Nx2N matrix
D, N = _check_grand_dynamical_matrix(D)
# Para-unitary matrix
g = np.diag(np.concatenate((np.ones(N), -np.ones(N))))
# Cholesky decomposition D = K^{\dag}K
try:
# In Colpa article decomposition is K^{\dag}K,
# while numpy gives KK^{\dag} by default,
# thus upper=True is needed
K = np.linalg.cholesky(D, upper=True)
except LinAlgError:
raise ColpaFailed
# Solve the standard eigenvalue problem for K g K^{\dag}
L, U = np.linalg.eig(K @ g @ np.conjugate(K).T)
# Sort with respect to L, in descending order
# Step 1 put L as a first line on top of U:
# [
# [L_0, ..., L_2N],
# [U_0_0, ..., U_0_2N],
# ...
# [U_2N_0, ..., U_2N_2N],
# ]
U = np.concatenate((L[np.newaxis, :], U), axis=0)
# Step 2 sort each row according to the sorted order of the first row (L)
U = U[:, np.argsort(U[0])]
# Step 3 separate L and U, and reverse the order to have
# first N eigenvalues to be positive
# and last N eigenvalues to be negative
L = U[0, ::-1]
U = U[1:, ::-1]
# Compute actual eigenvalues of D
E = g @ L
# Compute G_inv
G_inv = np.linalg.inv(K) @ U @ np.sqrt(np.diag(E))
# Inverse it by the Colpa's method to get G
G = _inverse_by_colpa(G_inv)
return E, G
# Populate __all__ with objects defined in this file
__all__ = list(set(dir()) - old_dir)
# Remove all semi-private objects
__all__ = [i for i in __all__ if not i.startswith("_")]
del old_dir
