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This is a Experimental Study study.

October 16, 2025Open Access

Generalized Exponentiated Gradient Algorithms Using the Euler Two-Parameter Logarithm

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The proposed algorithms utilize Bregman divergence for enhanced generalized gradient descent algorithms.
A new link function, the Euler logarithm, enables adaptive performance tuning via hyperparameterization.
The study focuses on generalized entropies, exploring over fifty potential deformed logarithmic functions.
Application in Online Portfolio Selection demonstrates improved performance through advanced gradient updates.

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Implication

Proposed algorithms enhance performance and robustness in Online Portfolio Selection using Bregman divergence, suggesting new insights into gradient descent.

Cite This Study

Andrzej Cichocki (2025) studied this question.

synapsesocial.com/papers/68f0d5eb105731330a2b1f19 https://doi.org/10.48550/arxiv.2502.17500

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Implication

Proposed algorithms enhance performance and robustness in Online Portfolio Selection using Bregman divergence, suggesting new insights into gradient descent.

Generalized Exponentiated Gradient Algorithms Using the Euler Two-Parameter Logarithm

Key Points

Discussion

Implication

Cite This Study

Generalized Exponentiated Gradient Algorithms Using the Euler Two-Parameter Logarithm

Key Points

Discussion

Implication

Cite This Study

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