A Multi-precision Algorithm for Convex Quadratic Optimization | Geoffroy Leconte | JuliaCon 2022
In this talk, we describe a Julia implementation of RipQP, a regularized interior-point method for convex quadratic optimization. RipQP is able to solve problems in several floating-point formats, and can also start in a lower precision as a form of warm-start. The algorithm uses sparse factorizations or Krylov methods from the Julia package Krylov.jl. We present an easy way to use RipQP to solve problems modeled with QuadraticModels.jl and LLSModels.jl.
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Видео A Multi-precision Algorithm for Convex Quadratic Optimization | Geoffroy Leconte | JuliaCon 2022 канала The Julia Programming Language
For more info on the Julia Programming Language, follow us on Twitter: https://twitter.com/JuliaLanguage and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang
00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps
Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles
Видео A Multi-precision Algorithm for Convex Quadratic Optimization | Geoffroy Leconte | JuliaCon 2022 канала The Julia Programming Language
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27 июля 2022 г. 18:00:04
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