Together with Roland Herzog and Hajg Jasa, we published a new algorithm to solve convex, non-smooth optimization problems on Riemannian manifolds:
The Riemannian Convex Bundle Method
Https://arxiv.org/abs/2402.13670

#Manopt #Julia #Optimization

The Riemannian Convex Bundle Method

We introduce the convex bundle method to solve convex, non-smooth optimization problems on Riemannian manifolds of bounded sectional curvature. Each step of our method is based on a model that involves the convex hull of previously collected subgradients, parallelly transported into the current serious iterate. This approach generalizes the dual form of classical bundle subproblems in Euclidean space. We prove that, under mild conditions, the convex bundle method converges to a minimizer. Several numerical examples implemented using Manopt.jl illustrate the performance of the proposed method and compare it to the subgradient method, the cyclic proximal point algorithm, as well as the proximal bundle method.

arXiv.org

📢 Manopt v0.4.12 is out! 🏔️

For Difference of Convex problems, the objective f = g - h can be written as a difference of two convex functions.
Both the Difference of Convex Algorithm (DCA) and the Difference of Convex Proximal Point Algorithm (DCPPA) are now available in Manopt.jl to solve such problems!

Both involve a subproblem, for each of which we provide cost & gradient. If you sub-problems have a closed-form solution, you can pass that as well.
📚 https://manoptjl.org/stable/solvers/difference_of_convex/
#Manifolds #Manopt

Difference of Convex · Manopt.jl

📢 Manopt v0.4.8 is out! 🏔️

In this new version of Manopt.jl we introduce solver state reports!

If you now run a solver you can get the resulting
minimizer (2nd to last code line) or return the state (last code line)
which displays a summary of the solver state, like important parameters
and a detailed stopping reason and its interpretation. #Manifolds #Manopt @julialang

📦 Repository: https://github.com/JuliaManifolds/Manopt.jl
📚 Documentation: https://manoptjl.org
🗄️ Available solvers: https://manoptjl.org/stable/solvers/

GitHub - JuliaManifolds/Manopt.jl: 🏔️Manopt. jl – Optimization on Manifolds in Julia

🏔️Manopt. jl – Optimization on Manifolds in Julia. Contribute to JuliaManifolds/Manopt.jl development by creating an account on GitHub.

GitHub

Since Mastodon got more active recently, here's a new #introduction.

Hi 👋,
I am Førsteamanuensis (Associate Professor) at the Department of Mathematical Sciences, NTNU, Trondheim, Norway, where I enjoy having an office with a view to the Trondheimfjord.
My main reseacrh areas are numerical analysis and optimization on Riemannian manifolds. I also work on a few #OpenSource packages in #JuliaLang  related to my research with #manifolds and #manopt.