{"title":"Lineax: JAX和Equinox中统一的线性解和线性最小二乘","authors":"Jason Rader, Terry Lyons, Patrick Kidger","doi":"arxiv-2311.17283","DOIUrl":null,"url":null,"abstract":"We introduce Lineax, a library bringing linear solves and linear\nleast-squares to the JAX+Equinox scientific computing ecosystem. Lineax uses\ngeneral linear operators, and unifies linear solves and least-squares into a\nsingle, autodifferentiable API. Solvers and operators are user-extensible,\nwithout requiring the user to implement any custom derivative rules to get\ndifferentiability. Lineax is available at https://github.com/google/lineax.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"11 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lineax: unified linear solves and linear least-squares in JAX and Equinox\",\"authors\":\"Jason Rader, Terry Lyons, Patrick Kidger\",\"doi\":\"arxiv-2311.17283\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce Lineax, a library bringing linear solves and linear\\nleast-squares to the JAX+Equinox scientific computing ecosystem. Lineax uses\\ngeneral linear operators, and unifies linear solves and least-squares into a\\nsingle, autodifferentiable API. Solvers and operators are user-extensible,\\nwithout requiring the user to implement any custom derivative rules to get\\ndifferentiability. Lineax is available at https://github.com/google/lineax.\",\"PeriodicalId\":501256,\"journal\":{\"name\":\"arXiv - CS - Mathematical Software\",\"volume\":\"11 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Mathematical Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2311.17283\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Mathematical Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2311.17283","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Lineax: unified linear solves and linear least-squares in JAX and Equinox
We introduce Lineax, a library bringing linear solves and linear
least-squares to the JAX+Equinox scientific computing ecosystem. Lineax uses
general linear operators, and unifies linear solves and least-squares into a
single, autodifferentiable API. Solvers and operators are user-extensible,
without requiring the user to implement any custom derivative rules to get
differentiability. Lineax is available at https://github.com/google/lineax.