.. _sphx_glr_auto_gallery_4-computation-tips:

Computational Tips
--------------------------
The generic splicing technique certifiably guarantees the best subset can be selected in a polynomial time. 
In practice, the computational efficiency can be improved to handle large scale datasets. 
The tips for computational improvement are applicable for: 

1. **ultra-high dimensional data** via

   - feature screening;
   - focus on important variables;
2. **large-sample data** via

   - golden-section searching;
   - early-stop scheme;
3. **sparse inputs** via

   - sparse matrix computation;
4. **specific models** via

   - covariance update for ``LinearRegression`` and ``MultiTaskRegression``;
   - quasi Newton iteration for ``LogisticRegression``, ``PoissonRegression``, ``CoxRegression``, etc.

More importantly, the technique in these tips can be use simultaneously. 
For example, ``abess`` allow algorithms to use both feature screening and golden-section searching such that 
algorithms can handle datasets with large-sample and ultra-high dimension. 
The following contents illustrate the above tips.

Besides, ``abess`` efficiently implements warm-start initialization and parallel computing, 
which are very useful for fast computing. 
To help use leverage them, we will also describe their implementation details in the following. 


.. raw:: html

    <div class="sphx-glr-thumbnails">


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Ultra-High dimensional data">

.. only:: html

  .. image:: /auto_gallery/4-computation-tips/images/thumb/sphx_glr_plot_large_dimension_thumb.png
    :alt:

  :ref:`sphx_glr_auto_gallery_4-computation-tips_plot_large_dimension.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Ultra-High dimensional data</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Large-Sample Data">

.. only:: html

  .. image:: /auto_gallery/4-computation-tips/images/thumb/sphx_glr_plot_large_sample_thumb.png
    :alt:

  :ref:`sphx_glr_auto_gallery_4-computation-tips_plot_large_sample.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Large-Sample Data</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Sparse Inputs">

.. only:: html

  .. image:: /auto_gallery/4-computation-tips/images/thumb/sphx_glr_plot_sparse_inputs_thumb.png
    :alt:

  :ref:`sphx_glr_auto_gallery_4-computation-tips_plot_sparse_inputs.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Sparse Inputs</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Specific Models">

.. only:: html

  .. image:: /auto_gallery/4-computation-tips/images/thumb/sphx_glr_plot_specific_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_gallery_4-computation-tips_plot_specific_models.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Specific Models</div>
    </div>


.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_gallery/4-computation-tips/plot_large_dimension
   /auto_gallery/4-computation-tips/plot_large_sample
   /auto_gallery/4-computation-tips/plot_sparse_inputs
   /auto_gallery/4-computation-tips/plot_specific_models