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New research shows entropy-smooth convex optimization cannot be accelerated

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv by Dragomir et al. demonstrates that entropy-smooth convex optimization cannot be accelerated. The research proves a lower bound for the convergence rate of minimization methods within this class of functions, indicating that first-order methods are optimal up to a logarithmic factor. This finding is particularly notable because accelerated methods are typically available under standard smoothness assumptions, but this work shows non-acceleration for a specific prox-function with a favorable structure. AI

IMPACT This theoretical finding may influence the development of optimization algorithms used in machine learning and AI research.

RANK_REASON The cluster contains a research paper published on arXiv detailing theoretical findings in mathematical optimization. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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

New research shows entropy-smooth convex optimization cannot be accelerated

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The cluster contains a research paper published on arXiv detailing theoretical findings in mathematical optimization. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jacob M. Aguirre, Dmitrii M. Ostrovskii ·

    Entropy-Smooth Convex Optimization Cannot Be Accelerated

    arXiv:2607.27476v1 Announce Type: cross Abstract: We prove an $\Omega(L/T)$ lower bound for the convergence rate of minimization in the class of functions that are convex and $L$-smooth relative to negative entropy on the standard $d$-simplex, valid for every first-order method w…

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