ml-optimized-orthogonal-basis-pp

Experimental results for research on: H. Waclawek and S. Huber, “Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation,” in Learning and Intelligent Optimization, Cham: Springer Nature Switzerland, 2025, pp. 427–441.

https://github.com/hawaclawek/ml-optimized-orthogonal-basis-pp

Science Score: 31.0%

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Keywords

approximation chebyshev-polynomials electronic-cams gradient-descent piecewise-polynomials tensorflow
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Experimental results for research on: H. Waclawek and S. Huber, “Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation,” in Learning and Intelligent Optimization, Cham: Springer Nature Switzerland, 2025, pp. 427–441.

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approximation chebyshev-polynomials electronic-cams gradient-descent piecewise-polynomials tensorflow
Created about 2 years ago · Last pushed 12 months ago
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readme.md

Experimental results for research on:

H. Waclawek and S. Huber, “Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation,” in Learning and Intelligent Op- timization, Cham: Springer Nature Switzerland, 2025, pp. 427–441. DOI: 10. 1007/978-3-031-75623-833_

See citation.bib for details on citation.

The first three Jupyter notebook exports deal with preliminary work,
like implementing Chebyshev basis into our exiting solution or segment size experiments.
Notebook exports 03 - 06 cover the essential results discussed in our paper.
Notebook export 07 documents generation of plots for the paper.

The code used to generate these results is also available in a more presentable/cleaned form here:
https://github.com/JRC-ISIA/ml-optimized-orthogonal-basis-1d-pp

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Citation (citation.bib)

@inproceedings{WH24,
    keywords = {cdg},
    title = {{Machine Learning Optimized Orthogonal Basis Piecewise Polynomial Approximation}},
    author = {Waclawek, Hannes and Huber, Stefan},
    year = {2025},
    booktitle = {{Learning and Intelligent Optimization}},
    publisher = {{Springer Nature Switzerland}},
    address = {Cham},
    pages = {427-441},
    isbn = {978-3-031-75623-8},
    doi = {10.1007/978-3-031-75623-8_33}
}

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