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Jensen–Steffensen inequality for strongly convex functions [PDF]
Journal of Inequalities and Applications, 2018 The Jensen inequality for convex functions holds under the assumption that all of the included weights are nonnegative. If we allow some of the weights to be negative, such an inequality is called the Jensen–Steffensen inequality for convex functions. In
M. Klaričić Bakula
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Majorization theorems for strongly convex functions [PDF]
Journal of Inequalities and Applications, 2019 In the article, we present several majorization theorems for strongly convex functions and give their applications in inequality theory. The given results are the improvement and generalization of the earlier results.
Syed Zaheer Ullah +2 more
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Integral Inequalities Involving Strongly Convex Functions [PDF]
Journal of Function Spaces, 2018 We study the notions of strongly convex function as well as F-strongly convex function. We present here some new integral inequalities of Jensen’s type for these classes of functions.
Ying-Qing Song +3 more
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Strongly Convex Functions of Higher Order Involving Bifunction [PDF]
Mathematics, 2019 Some new concepts of the higher order strongly convex functions involving an arbitrary bifuction are considered in this paper. Some properties of the higher order strongly convex functions are investigated under suitable conditions.
Bandar B. Mohsen +3 more
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New Improvements of the Jensen–Mercer Inequality for Strongly Convex Functions with Applications [PDF]
AxiomsIn this paper, we use the generalized version of convex functions, known as strongly convex functions, to derive improvements to the Jensen–Mercer inequality. We achieve these improvements through the newly discovered characterizations of strongly convex
Muhammad Adil Khan +2 more
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The minimizer of the sum of two strongly convex functions [PDF]
Optimization, 2023 The optimization problem concerning the determination of the minimizer for the sum of convex functions holds significant importance in the realm of distributed and decentralized optimization. In scenarios where full knowledge of the functions is not available, limiting information to individual minimizers and convexity parameters – either due to ...
Kuwaranancharoen, Kananart +1 more
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Acceleration of the PDHGM on Partially Strongly Convex Functions. [PDF]
J Math Imaging Vis, 2017 We propose several variants of the primal-dual method due to Chambolle and Pock. Without requiring full strong convexity of the objective functions, our methods are accelerated on subspaces with strong convexity. This yields mixed rates, O ( 1 / N 2 ) with respect to initialisation and O(1 / N) with respect to the dual sequence, and the residual part ...
Valkonen T, Pock T.
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On generalized strongly modified h-convex functions [PDF]
Journal of Inequalities and Applications, 2020 We derive some properties and results for a new extended class of convex functions, generalized strongly modified h-convex functions. Moreover, we discuss Schur-type, Hermite–Hadamard-type, and Fejér-type inequalities for this class.
Taiyin Zhao +4 more
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Revista De La Real Academia De Ciencias Exactas, Fisicas Y Naturales - Serie A: Matematicas, 2023 Sharp bounds are given for the second Hankel determinant of the logarithmic coefficients of strongly starlike and strongly convex functions.
Adam Lecko +2 more
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On Strongly Convex Functions via Caputo–Fabrizio-Type Fractional Integral and Some Applications
Journal of Mathematics, 2021 The theory of convex functions plays an important role in the study of optimization problems. The fractional calculus has been found the best to model physical and engineering processes.
Qi Li +4 more
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