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New optimization method achieves near-optimal complexity for convex finite-sum problems

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Researchers have developed a new optimization method for smooth convex finite-sum problems, achieving a complexity of \tilde{\mathcal{O}}(n + n^{5/7} \epsilon^{-2/7}). This method, a Taylor-corrected lazy Newton approach, improves upon previous work by leveraging individual smoothness and convexity assumptions. The new algorithm's performance matches the established lower bound for this stronger assumption, up to logarithmic factors. AI

IMPACT This research could lead to more efficient training of machine learning models by improving optimization algorithms.

RANK_REASON The cluster contains a research paper detailing a new optimization algorithm and its theoretical complexity. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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New optimization method achieves near-optimal complexity for convex finite-sum problems

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The cluster contains a research paper detailing a new optimization algorithm and its theoretical complexity. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xinliang Zhang, Lesi Chen, Zonghan Li, Jingzhao Zhang ·

    Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness

    arXiv:2610.42040v1 Announce Type: cross Abstract: We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components. Very recently, Tsedenov \& Kovalev (2026) showed that the minimax optimal complexity under averaged smoot…

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