New research shows entropy-smooth convex optimization cannot be accelerated

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 →

• arXiv
• Dragomir et al.
• Entropy-Smooth Convex Optimization
• Hugging Face
• mathematical optimization
• Mirror descent
• von Neumann entropy

• paper
• other

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