New Quantum Hermite-Hadamard Inequalities Utilizing Harmonic Convexity of the Functions
IEEE Access, 2019 The aim of this paper is to obtain some new Hermite-Hadamard type of inequalities via harmonic convex, strongly harmonic convex, strongly harmonic log-convex functions, and AH-convex in connection with quantum calculus. All the results reduce to ordinary
Bandar Bin-Mohsin +5 more
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A geometrically converging dual method for distributed optimization over time-varying graphs
, 2018 In this paper we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA), that is proven to converge R-linearly to the optimal point given that the
Jaldén, Joakim, Maros, Marie
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Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions [PDF]
, 2020 Artem Agafonov
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Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions. [PDF]
J Mach Learn Res, 2020 Spiridonoff A +2 more
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Refinements of Various Types of Fractional Inequalities via Generalized Convexity
Journal of MathematicsThis paper aims to find generalizations of inequalities that hold for unified integral operators by applying strongly exponentially α,ℏ−m−p-convex functions. These inequalities generate results for several fractional integral operators and simultaneously
Yong Tang +5 more
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AIMS MathematicsIn this paper, we take into account the notion of strongly multiplicative convex function and derive integral inequalities of Hermite-Hadamard ($ H.H $) type for such a function in the frame of multiplicative calculus.
Muhammad Umar +2 more
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Strongly MφMψ -Convex Functions, The Hermite–Hadamard–Fejér Inequality and Related Results [PDF]
, 2023 Mea Bombardelli, Sanja Varošanec
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Strongly F-Convex Functions with Structural Characterizations and Applications in Entropies
AxiomsStrongly 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.
Hasan Barsam +3 more
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High-Resolution Modeling of the Fastest First-Order Optimization Method for Strongly Convex Functions [PDF]
, 2020 Boya Sun, Jemin George, Solmaz S. Kia
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Parameter-free version of Adaptive Gradient Methods for Strongly-Convex Functions [PDF]
, 2023 Deepak Gouda +2 more
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