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https://github.com/benchopt/benchmark_huber_l2

Benchopt benchmark for L2-regularized Huber regression
https://github.com/benchopt/benchmark_huber_l2

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Benchopt benchmark for L2-regularized Huber regression

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Benchmark for L2-regularized Huber regression
=============================================
|Build Status| |Python 3.6+|

Benchopt is a package to simplify and make more transparent and
reproducible the comparisons of optimization algorithms.
This benchmark is dedicated to solver:

$$\\min_{w, \\sigma} {\\sum_{i=1}^n\\left(\\sigma + H_{\\epsilon}\\left(\\frac{X_{i}w - y_{i}}{\\sigma}\\right)\\sigma\\right) + \\alpha {\\|w\\|_2}^2}$$

where $n$ (or ``n_samples``) stands for the number of samples, $p$ (or ``n_features``) stands for the number of features

$$y \\in \\mathbb{R}^n, \\quad X \\in \\mathbb{R}^{n \\times p}$$

and

$$
H_{\\epsilon}(z) = \\begin{cases} z^2 & \\text {if } \\vert z \\vert < \\epsilon, \\\\ 2 \\epsilon \\vert z \\vert - \\epsilon^2 & \\text{otherwise} \\end{cases}
$$

Install
--------

This benchmark can be run using the following commands:

.. code-block::

$ pip install -U benchopt
$ git clone https://github.com/benchopt/benchmark_huber_l2
$ cd benchmark_huber_l2
$ benchopt run .

Apart from the problem, options can be passed to ``benchopt run``, to restrict the benchmarks to some solvers or datasets, e.g.:

.. code-block::

$ benchopt run . -s sklearn -d simulated --max-runs 10 --n-repetitions 10

Use ``benchopt run -h`` for more details about these options, or visit https://benchopt.github.io/api.html.

.. |Build Status| image:: https://github.com/benchopt/benchmark_huber_l2/workflows/Tests/badge.svg
:target: https://github.com/benchopt/benchmark_huber_l2/actions
.. |Python 3.6+| image:: https://img.shields.io/badge/python-3.6%2B-blue
:target: https://www.python.org/downloads/release/python-360/