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New analysis unifies convergence proofs for SAG, SAGA, and IAG algorithms

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a unified convergence analysis for SAG, SAGA, and IAG algorithms, which are commonly used in large-scale machine learning. This new analysis uses a novel Lyapunov function and concentration tools to establish bounds on delays caused by stochastic sub-sampling. The resulting proof is concise and modular, offering high-probability bounds for SAG and SAGA that can be extended to non-convex objectives. Additionally, this technique yields improved convergence rates for the IAG algorithm. AI

IMPACT Provides a more efficient and unified theoretical understanding for optimization algorithms used in large-scale machine learning.

RANK_REASON The cluster contains an academic paper detailing a new theoretical analysis of existing machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New analysis unifies convergence proofs for SAG, SAGA, and IAG algorithms

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The cluster contains an academic paper detailing a new theoretical analysis of existing machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Feng Zhu, Robert W. Heath Jr., Aritra Mitra ·

    A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms

    arXiv:2602.05304v2 Announce Type: replace Abstract: Stochastic variance-reduced algorithms such as Stochastic Average Gradient (SAG) and SAGA, and their deterministic counterparts like the Incremental Aggregated Gradient (IAG) method, have been extensively studied in large-scale …

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