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docs/source/algorithms.md

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Check out {ref}`how-to-select-algorithms` to see how to select an algorithm and specify
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`algo_options` when using `maximize` or `minimize`.
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## Optimizers from scipy
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## Optimizers from SciPy
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(scipy-algorithms)=
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optimagic supports most [SciPy](https://scipy.org/) algorithms and SciPy is
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automatically installed when you install optimagic.
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```{eval-rst}
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.. dropdown:: ``scipy_lbfgsb``
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.. dropdown:: scipy_lbfgsb
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**How to use this algorithm:**
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docs/source/conf.py

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todo_emit_warnings = True
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# -- Options for myst-nb ----------------------------------------
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nb_execution_mode = "off" # "off", "force", "cache", "auto"
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nb_execution_mode = "force" # "off", "force", "cache", "auto"
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nb_execution_allow_errors = False
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nb_merge_streams = True
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docs/source/explanation/internal_optimizers.md

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of commonly used convergence and stopping criteria. We also align the default values for
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stopping and convergence criteria as much as possible.
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You can find the harmonized names and value [here](algo_options_docs).
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```{eval-rst}
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You can find the harmonized names and value here: :ref:`algo_options`.
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```
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To align the names of other tuning parameters as much as possible with what is already
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there, simple have a look at the optimizers we already wrapped. For example, if you are

docs/source/reference/algo_options.md

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(algo_options_docs)=
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(algo_options)=
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# The default algorithm options
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