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This is the next alpha release of `minterpy`, which adds several new functionalities and enhances code quality and performance.
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## new features
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* partial derivatives for canonical and Newton polynomials
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* support of arbitrary positive `lp_degree`
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* ordinary regression based on multivariate polynomials as the first extra feature
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## maintenance
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* bug fixes
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* adding API documentation
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* improvement of user documentations
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* clean-up: deletion of code/comments, which are no longer used
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* introduction of a corporate design including logo and banner to docs, repository, README etc.
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This code is still marked as experimental and there is no issuance, that neither everything works as expected, nor if further releases will break the current API.
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# Version 0.1.0-alpha
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This is the initial alpha release of `minterpy`. It contains general structures to perform the polynomial interpolation task in multiple dimensions:
This code is still highly experimental and there is no issuance, that neither everything works as expected, nor if further releases will break the current API.
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[![Code style: black][black-badge]][black-link]
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# minterpy
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<figureclass="quote">
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<blockquote>
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to minterpy *sth.* (transitive verb) -- to produce a multivariate polynomial representation of *sth.* .
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</blockquote>
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<figcaption>
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— The minterpy developers in <cite>["Lifting the curse of dimensionality"](link-to-math-intro)</cite>
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— The minterpy developers in <cite>["Lifting the curse of dimensionality"](https://interpol.pages.hzdr.de/minterpy/fundamentals/introduction.html)</cite>
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</figcaption>
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</figure>
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Since this implementation is a prototype,
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we currently only provide the installation by self-building from source.
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We recommend to use`git` to get the `minterpy` source:
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We recommend to using`git` to get the `minterpy` source:
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