{"type":"ScholarlyArticle","title":"SIFAR: A Simple Faster Accelerated Variance-Reduced Gradient Method","authors":[{"name":"Zhize Li","slug":"zhize-li","given":"Zhize","family":"Li","affiliation":""}],"abstract":"In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use double-loop structures. Moreover, we provide a novel dynamic multi-stage convergence analysis, which is the key for improving previous results to the optimal rates. Our new theoretical rates and novel convergence analysis for the fundamental finite-sum problem can directly lead to key improvements for many other related problems, such as distributed/federated/decentralized optimization problems. Finally, the numerical experiments show that SIFAR converges faster than the previous state-of-the-art Varag, validating our theoretical results and confirming the practical superiority of SIFAR.","doi":"10.24963/ijcai.2025/630","url":"https://ijcai.org/proceedings/2025/630","pdf":"https://ijcai.org/proceedings/2025/0630.pdf","markdown":"https://ijcai.org/proceedings/2025/630.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/630","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":630,"pages":{"first":5662,"last":5670},"keywords":["Machine Learning: ML: Supervised Learning","Machine Learning: ML: Optimization","Data Mining: DM: Big data and scalability"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}