:py:mod:`cotengra.presets`
==========================

.. py:module:: cotengra.presets

.. autoapi-nested-parse::

   Preset configured optimizers.



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

Classes
~~~~~~~

.. autoapisummary::

   cotengra.presets.AutoOptimizer
   cotengra.presets.AutoHQOptimizer



Functions
~~~~~~~~~

.. autoapisummary::

   cotengra.presets.estimate_optimal_hardness



Attributes
~~~~~~~~~~

.. autoapisummary::

   cotengra.presets.auto_optimize
   cotengra.presets.auto_hq_optimize
   cotengra.presets.greedy_optimize
   cotengra.presets.optimal_optimize
   cotengra.presets.optimal_outer_optimize


.. py:function:: estimate_optimal_hardness(inputs)

   Provides a very rough estimate of how long it would take to find the
   optimal contraction order for a given set of inputs. The runtime is
   *very* approximately exponential in this number:

   .. math::

       T \propto \exp {n^2 * k^0.5}

   Where :math:`n` is the number of tensors and :math:`k` is the average
   degree of the hypergraph.


.. py:class:: AutoOptimizer(optimal_cutoff=250, minimize='combo', cache=True, **hyperoptimizer_kwargs)


   Bases: :py:obj:`cotengra.oe.PathOptimizer`

   An optimizer that automatically chooses between optimal and
   hyper-optimization, designed for everyday use.

   .. py:method:: _get_optimizer_hyper_threadsafe()


   .. py:method:: search(inputs, output, size_dict, **kwargs)


   .. py:method:: __call__(inputs, output, size_dict, **kwargs)



.. py:class:: AutoHQOptimizer(**kwargs)


   Bases: :py:obj:`AutoOptimizer`

   An optimizer that automatically chooses between optimal and
   hyper-optimization, designed for everyday use on harder contractions or
   those that will be repeated many times, and thus warrant a more extensive
   search.


.. py:data:: auto_optimize

   

.. py:data:: auto_hq_optimize

   

.. py:data:: greedy_optimize

   

.. py:data:: optimal_optimize

   

.. py:data:: optimal_outer_optimize