待翻譯：Basin: Efficient and Extensible Numerical Optimization in Rust

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--> [Submitted on 11 Aug 2026] Title:Basin: Efficient and Extensible Numerical Optimization in Rust View a PDF of the paper titled Basin: Efficient and Extensible Numerical Optimization in Rust, by Johan Larsson View PDF HTML (experimental) Abstract: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. Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC) MSC classes: 65Y15 ACM classes: D.2.13; G.1.6 Cite as: arXiv:2608.11279 [cs.LG] (or arXiv:2608.11279v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2608.11279 arXiv-issued DOI via DataCite (pending registration) Submission history From: Johan Larsson [view email] [v1] Tue, 11 Aug 2026 10:47:37 UTC (12 KB) Full-text links: Access Paper: View a PDF of the paper titled Basin: Efficient and Extensible Numerical Optimization in Rust, by Johan Larsson View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-08 Change to browse by: cs math math.OC References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both 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. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)