Feng Zhu, Robert W. Heath, Aritra Mitra
This paper presents a unified convergence analysis for the SAG, SAGA, and IAG algorithms, providing a simpler and more comprehensive understanding of their performance.
The paper focuses on improving the understanding of three important algorithms used in machine learning: SAG, SAGA, and IAG. These algorithms help in efficiently optimizing large-scale problems, but their existing analyses are complex and varied. The authors provide a new, simpler method to analyze these algorithms together using a unified approach. This new analysis not only simplifies the understanding but also improves the performance guarantees for these algorithms.
