solver¶
Eigensolvers with a few extra computation methods
The Solver
class is the main interface for dealing with eigenvalue problems. It
is made to work specifically with pybinding’s Model
objects, but it may use any
eigensolver algorithm under the hood.
A few different algorithms are provided out of the box: the lapack()
, arpack()
and feast()
functions return concrete Solver
implementation using the LAPACK,
ARPACK and FEAST algorithms, respectively.
The Solver
may easily be extended with new eigensolver algorithms. All that is
required is a function which takes a Hamiltonian matrix and returns the computed
eigenvalues and eigenvectors. See _SolverPythonImpl
for example.
Classes
Solver (impl: _pybinding.Solver) 
Computes the eigenvalues and eigenvectors of a Hamiltonian matrix 
Functions
arpack (model, k[, sigma]) 
ARPACK Solver implementation for sparse matrices 
feast (model, energy_range, initial_size_guess) 
FEAST Solver implementation for sparse matrices 
lapack (model, \*\*kwargs) 
LAPACK Solver implementation for dense matrices 

class
Solver
(impl: _pybinding.Solver)¶ Computes the eigenvalues and eigenvectors of a Hamiltonian matrix
This the common interface for various eigensolver implementations. It should not be created directly, but via the specific functions:
lapack()
,arpack()
andfeast()
. Those functions will set up their specific solver strategy and return a properly configuredSolver
object.
calc_bands
(k0, k1, *ks, step=0.1)¶ Calculate the band structure on a path in reciprocal space
Parameters: k0, k1, *ks : array_like
Points in reciprocal space which form the path for the band calculation. At least two points are required.
step : float, optional
Calculation step length in reciprocal space units. Lower
step
values will return more detailed results.Returns:

calc_dos
(energies, broadening)¶ Calculate the density of states as a function of energy
\[\text{DOS}(E) = \frac{1}{c \sqrt{2\pi}} \sum_n{e^{\frac{(E_n  E)^2}{2 c^2}}}\]for each \(E\) in
energies
, where \(c\) isbroadening
and \(E_n\) iseigenvalues[n]
.Parameters: energies : array_like
Values for which the DOS is calculated.
broadening : float
Controls the width of the Gaussian broadening applied to the DOS.
Returns:

calc_eigenvalues
(map_probability_at=None)¶ Return an
Eigenvalues
result object with an optional probability colormapWhile the
eigenvalues
property returns the raw values array, this method returns a result object with more data. In addition to the energy states, this result may show a colormap of the probability density for each state at a single position.Parameters: map_probability_at : array_like, optional
Cartesian position where the probability density of each energy state should be calculated.
Returns:

calc_ldos
(energies, broadening, position, sublattice='', reduce=True)¶ Calculate the local density of states as a function of energy at the given position
\[\text{LDOS}(E) = \frac{1}{c \sqrt{2\pi}} \sum_n{\Psi_n(r)^2 e^{\frac{(E_n  E)^2}{2 c^2}}}\]for each \(E\) in
energies
, where \(c\) isbroadening
, \(E_n\) iseigenvalues[n]
and \(r\) is a single site position determined by the argumentsposition
andsublattice
.Parameters: energies : array_like
Values for which the DOS is calculated.
broadening : float
Controls the width of the Gaussian broadening applied to the DOS.
position : array_like
Cartesian position of the lattice site for which the LDOS is calculated. Doesn’t need to be exact: the method will find the actual site which is closest to the given position.
sublattice : str
Only look for sites of a specific sublattice, closest to
position
. The default value considers any sublattice.reduce : bool
This option is only relevant for multiorbital models. If true, the resulting LDOS will summed over all the orbitals at the target site and the result will be a 1D array. If false, the individual orbital results will be preserved and the result will be a 2D array with
shape == (energy.size, num_orbitals)
.Returns:

calc_probability
(n, reduce=1e05)¶ Calculate the spatial probability density
\[\text{P}(r) = \Psi_n(r)^2\]for each position \(r\) in
system.positions
where \(\Psi_n(r)\) iseigenvectors[:, n]
.Parameters: n : int or array_like
Index of the desired eigenstate. If an array of indices is given, the probability will be calculated at each one and a sum will be returned.
reduce : float, optional
Reduce degenerate states by summing their probabilities. Neighboring states are considered degenerate if their energy is difference is lower than the value of
reduce
. This is disabled by passingreduce=0
.Returns:

calc_spatial_ldos
(energy, broadening)¶ Calculate the spatial local density of states at the given energy
\[\text{LDOS}(r) = \frac{1}{c \sqrt{2\pi}} \sum_n{\Psi_n(r)^2 e^{\frac{(E_n  E)^2}{2 c^2}}}\]for each position \(r\) in
system.positions
, where \(E\) isenergy
, \(c\) isbroadening
, \(E_n\) iseigenvalues[n]
and \(\Psi_n(r)\) iseigenvectors[:, n]
.Parameters: energy : float
The energy value for which the spatial LDOS is calculated.
broadening : float
Controls the width of the Gaussian broadening applied to the DOS.
Returns:

clear
()¶ Clear the computed results and start over

static
find_degenerate_states
(energies, abs_tolerance=1e05)¶ Return groups of indices which belong to degenerate states
Parameters: energies : array_like
abs_tolerance : float, optional
Examples
>>> energies = np.array([0.1, 0.1, 0.2, 0.5, 0.5, 0.5, 0.7, 0.8, 0.8]) >>> Solver.find_degenerate_states(energies) [[0, 1], [3, 4, 5], [7, 8]]
>>> energies = np.array([0.1, 0.2, 0.5, 0.7]) >>> Solver.find_degenerate_states(energies) []

report
(shortform=False) → str¶ Return a report of the last
solve()
computationParameters: shortform : bool, optional
Return a short one line version of the report

set_wave_vector
(k)¶ Set the wave vector for periodic models
Parameters: k : array_like
Wave vector in reciprocal space.

solve
()¶ Explicitly solve the eigenvalue problem right now
This method is usually not needed because the main result properties,
eigenvalues
andeigenvectors
, will call this implicitly the first time they are accessed. However, since thesolve()
routine may be computationally expensive, it is useful to have the ability to call it ahead of time as needed.

eigenvalues
¶ 1D array of computed energy states

eigenvectors
¶ 2D array where each column represents a wave function
eigenvectors.shape == (system.num_sites, eigenvalues.size)

model
¶ The tightbinding model attached to this solver

system
¶ The tightbinding system attached to this solver (shortcut for Solver.model.system)


arpack
(model, k, sigma=0, **kwargs)¶ ARPACK
Solver
implementation for sparse matricesThis solver is intended for large models with sparse Hamiltonian matrices. It only computes a small targeted subset of eigenvalues and eigenvectors. Internally this solver uses the
scipy.sparse.linalg.eigsh()
function for sparse Hermitian matrices.Parameters: model : Model
Model which will provide the Hamiltonian matrix.
k : int
The desired number of eigenvalues and eigenvectors. This number must be smaller than the size of the matrix, preferably much smaller for optimal performance. The computed eigenvalues are the ones closest to
sigma
.sigma : float, optional
Look for eigenvalues near
sigma
.**kwargs
Advanced arguments: forwarded to
scipy.sparse.linalg.eigsh()
.Returns:

feast
(model, energy_range, initial_size_guess, recycle_subspace=False, is_verbose=False)¶ FEAST
Solver
implementation for sparse matricesThis solver is only available if the C++ extension module was compiled with FEAST.
Parameters: model : Model
Model which will provide the Hamiltonian matrix.
energy_range : tuple of float
The lowest and highest eigenvalue between which to compute the solutions.
initial_size_guess : int
Initial user guess for number of eigenvalues which will be found in the given
energy_range
. This value may be completely wrong  the solver will autocorrect as needed. However, for optimal performance the estimate should be as close to 1.5 * actual_size as possible.recycle_subspace : bool, optional
Reuse previously computed values as a starting point for the next computation. This improves performance when subsequent computations differ only slightly, as is the case for the band structure of periodic systems where the results change gradually as a function of the wave vector. It may hurt performance otherwise.
is_verbose : bool, optional
Show the raw output from the FEAST routine.
Returns:

lapack
(model, **kwargs)¶ LAPACK
Solver
implementation for dense matricesThis solver is intended for small models which are best represented by dense matrices. Always solves for all the eigenvalues and eigenvectors. Internally this solver uses the
scipy.linalg.eigh()
function for dense Hermitian matrices.Parameters: model : Model
Model which will provide the Hamiltonian matrix.
**kwargs
Advanced arguments: forwarded to
scipy.linalg.eigh()
.Returns: