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

Oct 30, 2026 Source ↗

Key Insights

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 c.

Cite this synthesis DOI: 10.48550/arXiv.2610.42040 ↗

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@misc{ acaciamarkets-research-near-optimal-second-order-convex-finite-sum-optimization-und,
  title = { Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2610.42040 },
  url = { http://arxiv.org/abs/2610.42040v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Near-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2610.42040
UR  - http://arxiv.org/abs/2610.42040v1
ER  -

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Overview

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 c

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market-microstructure quantitative-modeling

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SQI 0.750