Mirror Descent and Exponentiated Gradient Algorithms Using Trace-Form Entropies
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Implication
Analysis shows improved convergence and robustness of algorithms using generalized entropies in non-Euclidean geometries, suggesting a unified approach.
Key Points
- Improved convergence behavior observed with algorithms based on generalized entropies in various geometric spaces.
- Tsallis entropy was a key focus, showing distinct Riemannian metrics affecting optimization outcomes.
- Mirror descent methods leverage adaptivity to non-Euclidean geometries through unique entropy choices.
- This framework may enable advancements in optimization techniques across diverse applications.
Cite This Study
Cichocki et al. (2025) studied this question.