forte2.mcopt.mc_optimizer
=========================

.. py:module:: forte2.mcopt.mc_optimizer




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

.. py:class:: MCOptimizerBase

   Bases: :py:obj:`abc.ABC`, :py:obj:`forte2.base_classes.SystemMixin`, :py:obj:`forte2.base_classes.MOsMixin`, :py:obj:`forte2.base_classes.MOSpaceMixin`


   
   Two-step optimizer for multi-configurational wavefunctions.


   :Parameters:

       **ci_solver** : CIBase | RelCIBase
           The CI solver to use. This should be an instance of a class that inherits from CIBase or RelCIBase.

       **active_frozen_orbitals** : list[int], optional
           List of active orbital indices to be frozen in the MCSCF optimization.
           If provided, all gradients involving these orbitals will be zeroed out.

       **maxiter** : int, optional, default=50
           Maximum number of macroiterations.

       **e_tol** : float, optional, default=1e-8
           Energy convergence tolerance.

       **g_tol** : float, optional, default=1e-7
           Gradient convergence tolerance.

       **die_if_not_converged** : bool, optional, default=True
           If True, raises an error if the optimization does not converge.

       **freeze_inter_gas_rots** : bool, optional, default=False
           Whether to freeze inter-GAS orbital rotations when multiple GASes are defined.

       **micro_maxiter** : int, optional, default=6
           Maximum number of microiterations for L-BFGS.

       **max_rotation** : float, optional, default=0.2
           Maximum orbital rotation size for L-BFGS.

       **do_transition_dipole** : bool, optional, default=False
           Whether to compute and report transition dipole moments at the end of the optimization.

       **final_orbital** : str, optional, default="semicanonical"
           Whether to return the final orbitals in the semicanonical basis or the original basis.











   .. rubric:: Notes

   See J. Chem. Phys. 152, 074102 (2020) for the current implementation
   of a unified CASSCF/GASSCF gradient and diagonal Hessian.
   The non-GAS part of diagonal Hessian implementation follows Theor. Chem. Acc. 97, 88-95 (1997).
   An earlier implementation (CASSCF only) used J. Chem. Phys. 142, 224103 (2015).



   ..
       !! processed by numpydoc !!

   .. py:attribute:: ci_solver
      :type:  forte2.base_classes.CIBase | forte2.base_classes.RelCIBase


   .. py:attribute:: active_frozen_orbitals
      :type:  list[int]
      :value: None



   .. py:attribute:: freeze_inter_gas_rots
      :type:  bool
      :value: False



   .. py:attribute:: maxiter
      :type:  int
      :value: 50



   .. py:attribute:: e_tol
      :type:  float
      :value: 1e-08



   .. py:attribute:: g_tol
      :type:  float
      :value: 1e-07



   .. py:attribute:: die_if_not_converged
      :type:  bool
      :value: True



   .. py:attribute:: micro_maxiter
      :type:  int
      :value: 6



   .. py:attribute:: max_rotation
      :type:  float
      :value: 0.2



   .. py:attribute:: do_transition_dipole
      :type:  bool
      :value: False



   .. py:attribute:: final_orbital
      :type:  str
      :value: 'semicanonical'



   .. py:attribute:: converged
      :type:  bool
      :value: False



   .. py:attribute:: executed
      :type:  bool
      :value: False



   .. py:method:: run()

      
      Run the two-step orbital-CI optimization.





      :Returns:

          **self** : MCOptimizer
              The instance of the optimizer with the results stored in its attributes.











      ..
          !! processed by numpydoc !!


   .. py:method:: make_average_1rdm()


   .. py:method:: make_average_2rdm()


   .. py:method:: make_average_2cumulant()


   .. py:method:: make_average_3rdm()


   .. py:method:: make_average_3cumulant()


   .. py:method:: make_average_cumulants()


.. py:class:: MCOptimizer

   Bases: :py:obj:`MCOptimizerBase`


   
   Two-step optimizer for multi-configurational wavefunctions.


   :Parameters:

       **ci_solver** : CIBase | RelCIBase
           The CI solver to use. This should be an instance of a class that inherits from CIBase or RelCIBase.

       **active_frozen_orbitals** : list[int], optional
           List of active orbital indices to be frozen in the MCSCF optimization.
           If provided, all gradients involving these orbitals will be zeroed out.

       **maxiter** : int, optional, default=50
           Maximum number of macroiterations.

       **e_tol** : float, optional, default=1e-8
           Energy convergence tolerance.

       **g_tol** : float, optional, default=1e-7
           Gradient convergence tolerance.

       **die_if_not_converged** : bool, optional, default=True
           If True, raises an error if the optimization does not converge.

       **freeze_inter_gas_rots** : bool, optional, default=False
           Whether to freeze inter-GAS orbital rotations when multiple GASes are defined.

       **micro_maxiter** : int, optional, default=6
           Maximum number of microiterations for L-BFGS.

       **max_rotation** : float, optional, default=0.2
           Maximum orbital rotation size for L-BFGS.

       **do_transition_dipole** : bool, optional, default=False
           Whether to compute and report transition dipole moments at the end of the optimization.

       **final_orbital** : str, optional, default="semicanonical"
           Whether to return the final orbitals in the semicanonical basis or the original basis.











   .. rubric:: Notes

   See J. Chem. Phys. 152, 074102 (2020) for the current implementation
   of a unified CASSCF/GASSCF gradient and diagonal Hessian.
   The non-GAS part of diagonal Hessian implementation follows Theor. Chem. Acc. 97, 88-95 (1997).
   An earlier implementation (CASSCF only) used J. Chem. Phys. 142, 224103 (2015).



   ..
       !! processed by numpydoc !!

   .. py:method:: make_sd_1rdm(left_root: int, right_root: int | None = None) -> tuple[numpy.typing.NDArray, numpy.typing.NDArray]


   .. py:method:: make_sd_2rdm(left_root: int, right_root: int | None = None) -> tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]


   .. py:method:: make_sd_3rdm(left_root: int, right_root: int | None = None) -> tuple[numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray, numpy.typing.NDArray]


   .. py:method:: make_sf_1rdm(left_root: int, right_root: int | None = None) -> numpy.typing.NDArray


   .. py:method:: make_sf_2rdm(left_root: int, right_root: int | None = None) -> numpy.typing.NDArray