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<!-- END doctoc generated TOC please keep comment here to allow auto update -->
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</details>
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<!-- key-features-start -->
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## :star:Key features
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## Key features
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- 🎯 **Intelligent Adaptive Sampling**: Adaptive focuses on areas of interest within a function, ensuring better results with fewer evaluations, saving time, and computational resources.
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- ⚡ **Parallel Execution**: The library leverages parallel processing for faster function evaluations, making optimal use of available computational resources.
@@ -61,7 +61,7 @@ To see Adaptive in action, try the [example notebook on Binder](https://mybinder
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<!-- key-features-end -->
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## :rocket:Example usage
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## Example usage
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Adaptively learning a 1D function and live-plotting the process in a Jupyter notebook:
Clone the repository and run `pip install -e ".[notebook,test,other]"` to add a link to the cloned repo into your Python path:
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@@ -198,7 +198,7 @@ pre-commit install
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in the repository.
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## :books:Citing
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## Citing
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If you used Adaptive in a scientific work, please cite it as follows.
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}
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```
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## :page_facing_up:Draft Paper
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## Draft Paper
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If you're interested in the scientific background and principles behind Adaptive, we recommend taking a look at the [draft paper](https://github.com/python-adaptive/paper) that is currently being written.
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This paper provides a comprehensive overview of the concepts, algorithms, and applications of the Adaptive library.
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## :sparkles:Credits
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## Credits
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We would like to give credits to the following people:
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