Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds

Autor:	Dodd, Daniel, Sharrock, Louis, Nemeth, Christopher
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	In recent years, interest in gradient-based optimization over Riemannian manifolds has surged. However, a significant challenge lies in the reliance on hyperparameters, especially the learning rate, which requires meticulous tuning by practitioners to ensure convergence at a suitable rate. In this work, we introduce innovative learning-rate-free algorithms for stochastic optimization over Riemannian manifolds, eliminating the need for hand-tuning and providing a more robust and user-friendly approach. We establish high probability convergence guarantees that are optimal, up to logarithmic factors, compared to the best-known optimally tuned rate in the deterministic setting. Our approach is validated through numerical experiments, demonstrating competitive performance against learning-rate-dependent algorithms. Comment: ICML 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.02296 Zobrazit plný text záznamu View this record from Arxiv