We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| iiduka-researches/202408-adaptive | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| sym | Ran | iiduka-researches/202408-adaptive/src/manifolds/stiefel.py code served (permissive licence) · get_code("e17fde97480d49b5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper proposes a general framework of Riemannian adaptive optimization methods. The framework encapsulates several stochastic optimization algorithms on Riemannian manifolds and incorporates the mini-batch strategy that is often used in deep learning. Within this framework, we also propose AMSGrad on embedded submanifolds of Euclidean space. Moreover, we give convergence analyses valid for both a constant and a diminishing step size. Our analyses also reveal the relationship between the convergence rate and mini-batch size. In numerical experiments, we applied the proposed algorithm to principal component analysis and the low-rank matrix completion problem, which can be considered to be Riemannian optimization problems. Python implementations of the methods used in the numerical experiments are available at https://github.com/iiduka-researches/202408-adaptive.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2409.00859")
get_code_for_paper("2409.00859")
have("2409.00859")
Connect an agent — have() is free.