dubfi.linalg.mpipad.worker_helpers
==================================

.. py:module:: dubfi.linalg.mpipad.worker_helpers

.. autoapi-nested-parse::

   Helper functions for MPI linear algebra worker (private module).

   This module contains alternative implementations of helper functions
   used by :mod:`dubfi.linalg.mpipad.worker`.



Functions
---------

.. autoapisummary::

   dubfi.linalg.mpipad.worker_helpers._trace_product_self_parallel
   dubfi.linalg.mpipad.worker_helpers._trace_product_self_ensopt
   dubfi.linalg.mpipad.worker_helpers._trace_product_self_ens


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

.. py:function:: _trace_product_self_parallel(s: int, e: int, hens: numpy.ndarray, x: numpy.ndarray, loc: numpy.ndarray, chainT: numpy.ndarray) -> numpy.ndarray

   Compute part of trace product for :class:`MpiGradOpWorker` (special case).

   Helper for :meth:`MpiGradOpWorker._trace_product` in commonly used case.

   Runtime: O(n_state * n_obs**3) + O(n_state * n_ens * n_obs**2) + O(n_state**2 * n_ens * n_obs)

   Required memory (theoretically): 16 * n_obs**2 Bytes per numba thread

   :param s: start index in reduced observation dimension
   :type s: int
   :param e: end index in reduced observation dimension
   :type e: int
   :param hens: centered ensemble observation operator, shape (n_ens, n_state, n_obs)
   :type hens: array
   :param x: ensemble of states, shape (n_ens, n_obs)
   :type x: array
   :param loc: localization, real-symmetric matrix, shape (n_obs, n_obs)
   :type loc: array
   :param chainT: chained array, real-symmetric matrix, shape (n_obs, n_obs)
   :type chainT: array


.. py:function:: _trace_product_self_ensopt(s: int, e: int, hens: numpy.ndarray, x: numpy.ndarray, loc: numpy.ndarray, chain: numpy.ndarray) -> numpy.ndarray

   Compute part of trace product for :class:`MpiGradOpWorker` (special case).

   Helper for :meth:`MpiGradOpWorker._trace_product` in commonly used case,
   assuming that loc and chain are symmetric. This function can be faster than
   :func:`_trace_product_self_parallel` if n_state > 2*n_obs**2.

   Runtime: O(n_ens**2 * n_obs**3) + ...

   Required memory: 16 * (n_ens + 1) * n_obs**2 Bytes

   Define:
   s[k, i1, j2] = (hens[m1, k, i1] x[m1, j1] + i1 <-> j1) loc[i1, j1] chain[j1, j2] (sum over m1, j1)
   Compute: result[k, l] = s[k, i1, j2] s[l, j2, i1] (sum over i1=s:e, j2=0:n_obs)

   Consider fixed m1, m2. Then result consists of two parts:
   A: hens[m1, k, i1] x[m1, j1] loc[i1, j1] chain[j1, j2] hens[m2, l, j2] x[m2, i2] loc[i2, j2] chain[i2, i1]
   B: hens[m1, k, i1] x[m1, j1] loc[i1, j1] chain[j1, j2] hens[m2, l, i2] x[m2, j2] loc[i2, j2] chain[i2, i1]



.. py:function:: _trace_product_self_ens(s: int, e: int, hens: numpy.ndarray, x: numpy.ndarray, loc: numpy.ndarray, chain: numpy.ndarray) -> numpy.ndarray

   Compute part of trace product for :class:`MpiGradOpWorker` (special case).

   Helper for :meth:`MpiGradOpWorker._trace_product` in commonly used case,
   assuming that loc and chain are symmetric.

   Runtime: O(n_ens**2 * n_obs**3) + ...

   Define:
   s[k, i1, j2] = (hens[m1, k, i1] x[m1, j1] + i1 <-> j1) loc[i1, j1] chain[j1, j2] (sum over m1, j1)
   Compute: result[k, l] = s[k, i1, j2] s[l, j2, i1] (sum over i1=s:e, j2=0:n_obs)

   Consider fixed m1, m2. Then result consists of two parts:
   A: hens[m1, k, i1] x[m1, j1] loc[i1, j1] chain[j1, j2] hens[m2, l, j2] x[m2, i2] loc[i2, j2] chain[i2, i1]
   B: hens[m1, k, i1] x[m1, j1] loc[i1, j1] chain[j1, j2] hens[m2, l, i2] x[m2, j2] loc[i2, j2] chain[i2, i1]