cg
============

.. py:module:: jaxns.experimental.solvers.cg

.. rubric:: :code:`jaxns.experimental.solvers.cg`


.. rubric:: Module Contents

.. py:class:: CGDiagnostics

   Bases: :py:obj:`NamedTuple`


   .. py:attribute:: iterations
      :type:  jaxns.internals.types.IntArray


   .. py:attribute:: final_res_norm
      :type:  jaxns.internals.types.FloatArray


.. py:data:: DomainType

.. py:function:: cg_solve(A, b, x0, M = _identity, maxiter = 100, tol = 1e-05, atol = 0.0)

   Solve a linear system Ax = b using the conjugate gradient method.

   :param A: a square PSD linear operator
   :param b: the right-hand side
   :param x0: an initial guess for the solution
   :param M: a preconditioner for A
   :param maxiter: the maximum number of iterations, if None then size of b
   :param tol: the relative tolerance for the residual norm
   :param atol: the absolute tolerance for the residual norm

   :returns: the solution x and diagnostics