AdaSub: Stochastic Optimization Using Second-Order Information in Low-Dimensional Subspaces

Autor:	da Mata, João Victor Galvão, Andersen, Martin S.
Rok vydání:	2023
Předmět:	Mathematics - Optimization and Control Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/DSAA60987.2023.10302473
Popis:	We introduce AdaSub, a stochastic optimization algorithm that computes a search direction based on second-order information in a low-dimensional subspace that is defined adaptively based on available current and past information. Compared to first-order methods, second-order methods exhibit better convergence characteristics, but the need to compute the Hessian matrix at each iteration results in excessive computational expenses, making them impractical. To address this issue, our approach enables the management of computational expenses and algorithm efficiency by enabling the selection of the subspace dimension for the search. Our code is freely available on GitHub, and our preliminary numerical results demonstrate that AdaSub surpasses popular stochastic optimizers in terms of time and number of iterations required to reach a given accuracy. Comment: Published in: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.20060 Zobrazit plný text záznamu View this record from Arxiv