{"slug": "basin-efficient-and-extensible-numerical-optimization-in-rust", "title": "Basin: Efficient and Extensible Numerical Optimization in Rust", "summary": "Basin, a numerical optimization library for the Rust programming language, provides a consistent interface for stating and solving optimization problems with a broad catalog of solvers and first-class support for constraints, as described in a paper submitted to arXiv on August 11, 2026. The library aims to serve applications across the sciences, including model fitting, simulation calibration, and machine learning training.", "body_md": "# Computer Science > Machine Learning\n\n[Submitted on 11 Aug 2026]\n\n# Title:Basin: Efficient and Extensible Numerical Optimization in Rust\n\n[View PDF](/pdf/2608.11279)\n\n[HTML (experimental)](https://arxiv.org/html/2608.11279v1)\n\nAbstract:Basin is a numerical optimization library for the Rust programming language. Numerical optimization is the task of finding the inputs that minimize a function, and it is a fundamental element across the sciences: fitting a model to data, calibrating a simulation, training a machine learning model, or choosing engineering parameters that minimize cost. Basin gives users a single, consistent way to both state and solve such problems, with a broad catalog of solvers and first-class support for constraints.\n\n### Current browse context:\n\ncs.LG\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/basin-efficient-and-extensible-numerical-optimization-in-rust", "canonical_source": "https://arxiv.org/abs/2608.11279", "published_at": "2026-08-13 04:00:00+00:00", "updated_at": "2026-08-13 04:14:20.762592+00:00", "lang": "en", "topics": ["machine-learning", "developer-tools"], "entities": ["Basin", "Rust", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/basin-efficient-and-extensible-numerical-optimization-in-rust", "markdown": "https://wpnews.pro/news/basin-efficient-and-extensible-numerical-optimization-in-rust.md", "text": "https://wpnews.pro/news/basin-efficient-and-extensible-numerical-optimization-in-rust.txt", "jsonld": "https://wpnews.pro/news/basin-efficient-and-extensible-numerical-optimization-in-rust.jsonld"}}