arXiv (All)
open access
Convergence of Rotation-based Matrix Optimizers: A Unified Analysis of SOAP, Conda, and SPlus
In this work, we develop a unified theoretical framework for analyzing the convergence of rotation-based matrix optimizers, which apply orthogonal transformations to map the momentum into a rotated space, perform coordinate-wise or normalized updates there, and then rotate the updates back. Our fram
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Nonsmooth Optimization via Orthogonalized Momentum#1110366
- MatrixFSDP: communication-free matrix optimizers under ZeRO-3 parameter sharding#926111
- Adaptive Algorithmic Simulation for Nonlinear Eigenvalue Problems in Mathematical Physics#250530
- A General Algorithm to Calculate the Inverse Principal $p$-th Root of Symmetric Positive Definite Matrices#1128455
- Matrix Methods in Data Analysis, Signal Processing, and Machine Learning#3620
Record · ID 1050693
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive