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New arXiv papers explore optimization algorithms for ML

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore advancements in optimization algorithms for machine learning. The first paper introduces Manifold Constrained Steepest Descent (MCSD) and its tangent-projected variant (MCSD-TP) for minimizing functions over complex feasible sets, with a specialization called SPEL for the Stiefel manifold. The second paper establishes a lower bound for stepsize-based acceleration of gradient descent, demonstrating that stepsize schedules alone cannot achieve optimal convergence rates for smooth convex optimization. AI

IMPACT These papers advance theoretical understanding of optimization algorithms, potentially leading to more efficient training of machine learning models.

RANK_REASON Two distinct academic papers published on arXiv concerning optimization algorithms.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers explore optimization algorithms for ML

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Kaiwei Yang, Lexiao Lai ·

    Manifold constrained steepest descent for smooth and closed-set optimization

    arXiv:2601.21487v2 Announce Type: replace-cross Abstract: We study minimization of smooth functions over feasible sets that have smooth embedded-manifold structure throughout or only on selected regions, using linear minimization oracles (LMOs) to determine search directions unde…

  2. arXiv stat.ML TIER_1 English(EN) · Jianhao Ma, Yuxin Chen ·

    A lower bound for stepsize-based acceleration of gradient descent

    arXiv:2608.10418v1 Announce Type: cross Abstract: Recent work has shown that, for smooth convex optimization, plain gradient descent can be accelerated from its textbook convergence rate of $O(T^{-1})$ (where $T$ denotes the number of iterations) to $O\big(T^{-\log_2(1+\sqrt{2})}…

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