Embedded Files
Join manifold optimization reading group! ◡̎
Goal
Goal
To increase literacy on manifold optimization, in order be become proficient in using available computational tools (https://www.manopt.org/, https://github.com/geomstats/geomstats), and better able to understand papers that use concepts from differential geometry (e.g. in optimization, statistical inference, deep learning with structure, physical forward models)
Format
Format
Presentations and demos from Nicolas Boumal's 2023 textbook on Riemannian optimization "An Introduction to Optimization on Smooth Manifolds", Cambridge University Press, https://www.nicolasboumal.net/book/.
Intended audience
Intended audience
Graduate students, postdocs, professors who are motivated to make progress in their practical understanding of manifold optimization in a group setting (study buddies help for motivation, accountability, and co-learning)
Graduate students, postdocs, professors who are motivated to make progress in their practical understanding of manifold optimization in a group setting (study buddies help for motivation, accountability, and co-learning)
Suggested prerequisites
Suggested prerequisites
Linear algebra - e.g. UBC MATH 307 Applied Linear Algebra
Optimization - e.g. UBC CPSC 406 Computational Optimization, CPSC 542F/MATH 604: Convex Analysis and Optimization, CPSC 536M: Optimization Theory, EECE 571Z - Convex Optimization
Schedule
Schedule
You can find the schedule and progress of the reading group here.
Contact
Contact
Join PACMAN Slack for staying in touch with the reading group. For more information, contact Geoffrey Woollard or PACMAN organizers.
Page updated
Google Sites
Report abuse