Small changes to documentation

optimagic-dev / optimagic

optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.

https://optimagic.readthedocs.io/

MIT License

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Small changes to documentation #548

Closed ChristianZimpelmann closed 3 weeks ago

ChristianZimpelmann commented 4 weeks ago

Fix some typos in documentation and docstrings

Also two more comments/questions:

If I am not mistaken, the description of pounders isn't correct. The criterion function seems to be expected as just returning value

```{eval-rst}
.. dropdown:: pounders

.. code-block::

    "pounders"

Minimize a function using the POUNDERS algorithm.

POUNDERs (:cite:`Benson2017`, :cite:`Wild2015`, `GitHub repository
<https://github.com/erdc/petsc4py>`_)
[]
The criterion function :func:`func` should return a dictionary with the following
fields:

1. ``"value"``: The sum of squared (potentially weighted) errors.
2. ``"root_contributions"``: An array containing the root (weighted) contributions.

The graph in how_to_algorithm_selection mentions scipy_ls_dogleg, but this algorithm doesn't seem to be supported, yet.

codecov[bot] commented 4 weeks ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Files with missing lines	Coverage Δ
src/optimagic/optimizers/_pounders/gqtpar.py	`90.54% <ø> (ø)`
...timagic/optimizers/_pounders/pounders_auxiliary.py	`97.32% <ø> (ø)`
src/optimagic/optimizers/pounders.py	`92.56% <ø> (ø)`

... and 1 file with indirect coverage changes

janosg commented 4 weeks ago

Thanks, I'll have a look at your comments. The description of the pounders objective function is definitely outdated. I'll check about scipy_ls_dogleg.

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janosg commented 3 weeks ago

The correct name of the algorithm is scipy_ls_dogbox because it is a dogleg algorithm with support for box constraints. I adjusted the decision tree.