🔈 Manopt.jl v0.4.54 🏔️

We introduce two new solvers:

• The Convex Bundle Method https://manoptjl.org/stable/solvers/convex_bundle_method/

• The Proximal Bundle Method https://manoptjl.org/stable/solvers/proximal_bundle_method/

to solve, (convex) nonsmooth optimization problems on Riemannian manifolds. The first one is also discussed in the paper “The Riemannian Convex Bundle Method” https://arxiv.org/abs/2402.13670.

🔈 Manopt.jl v0.4.34 🏔️

introduces the keyword `objective_type=:Euclidean`,

which allows you to provide a Euclidean cost, gradient, Hessian in the embedding of a manifold,

we then perform the conversion to Riemannian gradient and Hessian automatically in Manopt.jl

See https://manoptjl.org/stable/tutorials/EmbeddingObjectives/

Are you using Manifolds.jl or would like to learn how to use this package?
We now have a paper giving an introduction on Manifolds.jl – and comparing its efficientcy to other packages implementing Riemannian manifolds

S. D. Axen, M. Baran, R. Bergmann, K. Rzecki
“Manifolds.jl: An Extensible Julia Framework for Data Analysis on Manifolds”, ACM TOMS, http://dx.doi.org/10.1145/3618296

Good morning Julia World. 👋

This is an account about the JuliaManifolds GitHub organisation.
The idea is to post about important updates, new tutorials or packages – within that organisation or around differential geometry and manifolds in general