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Abstract
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Strongly convex functions form a central subclass of convex functions and have gained considerable
attention due to their structural advantages and broad applicability, particularly
in optimization and information theory. In this paper, we investigate the class of strongly
F-convex functions, which generalizes the classical notion of strong convexity by introducing
an auxiliary convex control function F. We establish several fundamental structural
characterizations of this class and provide a variety of nontrivial examples such as power,
logarithmic, and exponential functions. In addition, we derive refined Jensen-type and
Hermite–Hadamard-type inequalities adapted to the strongly F-convex concept, thereby
extending and sharpening their classical forms. As applications, we obtain new analytical
inequalities and improved error bounds for entropy-related quantities, including Shannon,
Tsallis, and Rényi entropies, demonstrating that the concept of strong F-convexity naturally
yields strengthened divergence and uncertainty estimates.
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