Results 41 to 50 of about 807,538 (297)
On Generalized Strongly Convex Functions and Unified Integral Operators
Mathematical Problems in Engineering, 2021 In this paper, we define a strongly exponentially α , h − m -convex function that generates several kinds of strongly convex and convex functions.
Timing Yu +4 more
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The second Hankel determinant for strongly convex and Ozaki close-to-convex functions
Annali di Matematica Pura ed Applicata, 2021 Let f be analytic in the unit disk D={z∈C:|z|
Y. Sim, A. Lecko, D. Thomas
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Journal of Mathematical Inequalities, 2021 . Sharp upper and lower bounds are found of the second and third order Hermitian Toeplitz determinants for the classes of strongly starlike and strongly convex functions of order α ( α ∈ [ 0 , 1 ) ).
B. Kowalczyk +2 more
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A comprehensive review of the Hermite-Hadamard inequality pertaining to fractional differential operators [PDF]
Surveys in Mathematics and its Applications, 2023 A review on Hermite-Hadamard type inequalities connected with a different classes of convexities and fractional differential operators is presented. In the various classes of convexities it includes, classical convex functions, quasi-convex functions, p ...
Muhammad Tariq +3 more
doaj
On the converse Jensen inequality for strongly convex functions
Journal of Mathematical Analysis and Applications, 2016 Kazimierz Nikodem +1 more
exaly +2 more sources
Ostrowski Type Inequalities for $n$-Times Strongly $m$-$MT$-Convex Functions [PDF]
Sahand Communications in Mathematical Analysis, 2023 In this paper, we introduce the class of strongly $m$--$MT$-convex functions based on the identity given in [P. Cerone et al., 1999]. We establish new inequalities of the Ostrowski-type for functions whose $n^{th}$ derivatives are strongly $m$--$MT ...
Badreddine Meftah, Chayma Marrouche
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ON THE NEIGHBOURHOODS OF STRONGLY CONVEX FUNCTIONS
Tamkang Journal of Mathematics, 1996 In this paper neighbourhoods of strongly convex and strongly starlike function are determined.
Parvatham, R., Premabai, Millicent
openaire +3 more sources
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions [PDF]
SIAM Journal on Optimization, 2018 We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent (GD) and accelerated gradient (AG) methods for minimizing strongly convex functions when the gradient ...
N. Aybat +3 more
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Convex Analysis for Minimizing and Learning Submodular Set Functions [PDF]
, 2013 The connections between convexity and submodularity are explored, for purposes of minimizing and learning submodular set functions. First, we develop a novel method for minimizing a particular class of submodular functions, which can be expressed as a
Peter Stobbe, Stobbe, Peter
core +1 more source
A Robust Accelerated Optimization Algorithm for Strongly Convex Functions [PDF]
American Control Conference, 2017 This work proposes an accelerated first-order algorithm we call the Robust Momentum Method for optimizing smooth strongly convex functions. The algorithm has a single scalar parameter that can be tuned to trade off robustness to gradient noise versus ...
Saman Cyrus +3 more
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