forte2.helpers.davidsonliu
==========================

.. py:module:: forte2.helpers.davidsonliu




Module Contents
---------------

.. py:class:: DavidsonLiuSolver(size: int, nroot: int, basis_per_root: int = 4, collapse_per_root: int = 2, maxiter: int = 100, e_tol: float = 1e-12, r_tol: float = 1e-06, eta: float | None = None, log_level: int = logger.get_verbosity_level(), dtype: type = np.float64)

   
   Davidson-Liu solver for iterative diagonalization of Hermitian matrices.


   :Parameters:

       **size** : int
           Dimension of the matrix / number of basis vectors.

       **nroot** : int
           Number of roots to find.

       **basis_per_root** : int, optional, default=4
           Number of basis vectors to keep per root.

       **collapse_per_root** : int, optional, default=2
           Number of vectors to collapse to per root.

       **maxiter** : int, optional, default=100
           Maximum number of iterations to perform.

       **e_tol** : float, optional, default=1e-12
           Convergence tolerance for eigenvalues.

       **r_tol** : float, optional, default=1e-6
           Convergence tolerance for residuals.

       **eta** : float, optional
           Target eigenvalue shift for sorting eigenpairs.
           If None, no shift is applied.

       **log_level** : int, optional, default=logger.get_verbosity_level()
           Logging level for output messages.

       **dtype** : type, optional, default=np.float64
           Data type of the matrix to diagonalize. Must be float or complex.

   :Attributes:

       **iter** : int
           Current iteration number.

       **converged** : bool
           Whether the solver has converged.













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   .. py:attribute:: size


   .. py:attribute:: nroot


   .. py:attribute:: collapse_per_root
      :value: 2



   .. py:attribute:: basis_per_root
      :value: 4



   .. py:attribute:: maxiter
      :value: 100



   .. py:attribute:: e_tol
      :value: 1e-12



   .. py:attribute:: r_tol
      :value: 1e-06



   .. py:attribute:: eta
      :value: None



   .. py:attribute:: log_level
      :value: 3



   .. py:attribute:: dtype


   .. py:attribute:: collapse_size


   .. py:attribute:: max_subspace_size


   .. py:attribute:: b


   .. py:attribute:: sigma


   .. py:attribute:: r


   .. py:attribute:: h_diag
      :value: None



   .. py:attribute:: G


   .. py:attribute:: alpha


   .. py:attribute:: lam


   .. py:attribute:: lam_old


   .. py:attribute:: schmidt_discard_threshold
      :value: 1e-07



   .. py:attribute:: schmidt_orthogonality_threshold
      :value: 1e-12



   .. py:attribute:: basis_size
      :value: 0



   .. py:attribute:: sigma_size
      :value: 0



   .. py:method:: add_sigma_builder(sigma_builder)

      
      Add the function that builds the matrix-vector product.


      :Parameters:

          **sigma_builder** : callable
              This function should have signature ``sigma_builder(basis_block, sigma_block) -> None``,
              where ``basis_block.shape == (size, m)`` and ``sigma_block.shape == (size, m)``.
              and it modifies ``sigma_block`` in place, performing the operation
              ``sigma_block = matrix @ basis_block``.














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   .. py:method:: add_h_diag(h_diag)


   .. py:method:: add_guesses(guesses)

      
      Add initial guesses for the eigenvectors.


      :Parameters:

          **guesses: NDArray**
              ``guesses.shape == (size, n_guess)``, with n_guess between 1 and subspace_size.














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   .. py:method:: add_project_out(project_out)

      
      project_out: list of arrays each shape (size,)
















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   .. py:method:: solve()


   .. py:method:: add_rows_and_orthonormalize(A_existing: numpy.ndarray, B_candidates: numpy.ndarray, A_slots: numpy.ndarray) -> int

      
      Add candidate column vectors from B_candidates into A_slots, using iterative orthogonalization per vector.
      Returns the number of columns added.
















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   .. py:method:: orthonormality_check(b: numpy.ndarray, msg: str = 'Orthonormality check failed.')

      
      Check if the columns of b are orthonormal.
















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