The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Tianyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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some inequalities of hermite hadamard type for ga Convex Functions with applications to means
Le Matematiche, 2013Co-Authors: Tianyu ZhangAbstract:In the paper, the authors, by Holder's inequality, establish some Hermite-Hadamard type integral inequalities for GA-Convex Functions and apply these inequalities to construct several inequalities for special means.
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on integral inequalities of hermite hadamard type for s geometrically Convex Functions
Abstract and Applied Analysis, 2012Co-Authors: Tianyu ZhangAbstract:The authors introduce the concept of the s-geometrically Convex Functions. By the well-known Holder inequality, they establish some integral inequalities of Hermite-Hadamard type related to the s-geometrically Convex Functions and apply these inequalities to special means.
Sever S Dragomir - One of the best experts on this subject based on the ideXlab platform.
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inequalities of hermite hadamard type for h Convex Functions on linear spaces
Proyecciones (antofagasta), 2015Co-Authors: Sever S DragomirAbstract:Some inequalities of Hermite-Hadamard type for h-Convex Functions defined on Convex subsets in real or complex linear spaces are given. Applications for norm inequalities are provided as well.
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superadditivity and monotonicity of some functionals associated with the hermite hadamard inequality for Convex Functions in linear spaces
Rocky Mountain Journal of Mathematics, 2012Co-Authors: Sever S DragomirAbstract:The superadditivity and monotonicity properties of some func- tionals associated with Convex Functions and the Hermite-Hadamard inequality in the general setting of linear spaces are investigated. Applications for norms and Convex Functions of a real variable are given. Some inequalities for arithmetic, geometric, harmonic, logarithmic and identric means are improved.
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on some new inequalities for differentiable co ordinated Convex Functions
Journal of Inequalities and Applications, 2012Co-Authors: M A Latif, Sever S DragomirAbstract:Several new inequalities for differentiable co-ordinated Convex and concave Functions in two variables which are related to the left side of Hermite- Hadamard type inequality for co-ordinated Convex Functions in two variables are obtained.
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hermite hadamard s type inequalities for operator Convex Functions
Applied Mathematics and Computation, 2011Co-Authors: Sever S DragomirAbstract:Some Hermite–Hadamard’s type inequalities for operator Convex Functions of selfadjoint operators in Hilbert spaces are given. Applications for particular cases of interest are also provided.
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new some hadamard s type inequalities for co ordinated Convex Functions
arXiv: Classical Analysis and ODEs, 2010Co-Authors: Mehmet Zeki Sarikaya, Muhamet Emin Ozdemir, Sever S Dragomir, Erhan SetAbstract:In this paper, we establish new some Hermite-Hadamard's type inequalities of Convex Functions of 2 variables on the co-ordinates.
Alex Olshevsky - One of the best experts on this subject based on the ideXlab platform.
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stochastic gradient push for strongly Convex Functions on time varying directed graphs
IEEE Transactions on Automatic Control, 2016Co-Authors: Angelia Nedic, Alex OlshevskyAbstract:We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of $O((\ln t)/t)$ for strongly Convex Functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the $O((\ln t)/\sqrt{t})$ rate previously known for (general) Convex Functions.
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stochastic gradient push for strongly Convex Functions on time varying directed graphs
arXiv: Optimization and Control, 2014Co-Authors: Angelia Nedic, Alex OlshevskyAbstract:We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of $O \left((\ln t)/t \right)$ for strongly Convex Functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the $O \left((\ln t)/\sqrt{t} \right)$ rate previously known for (general) Convex Functions.
P Gochhayat - One of the best experts on this subject based on the ideXlab platform.
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the fekete szego problem for k uniformly Convex Functions and for a class defined by the owa srivastava operator
Journal of Mathematical Analysis and Applications, 2008Co-Authors: A K Mishra, P GochhayatAbstract:Abstract By making use of a fractional differential operator due to Owa and Srivastava, a subclass of analytic Functions related to k -uniformly Convex Functions, is introduced. For this class and in particular for the class of k -uniformly Convex Functions the Fekete–Szego problem is completely solved.
Angelia Nedic - One of the best experts on this subject based on the ideXlab platform.
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stochastic gradient push for strongly Convex Functions on time varying directed graphs
IEEE Transactions on Automatic Control, 2016Co-Authors: Angelia Nedic, Alex OlshevskyAbstract:We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of $O((\ln t)/t)$ for strongly Convex Functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the $O((\ln t)/\sqrt{t})$ rate previously known for (general) Convex Functions.
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stochastic gradient push for strongly Convex Functions on time varying directed graphs
arXiv: Optimization and Control, 2014Co-Authors: Angelia Nedic, Alex OlshevskyAbstract:We investigate the convergence rate of the recently proposed subgradient-push method for distributed optimization over time-varying directed graphs. The subgradient-push method can be implemented in a distributed way without requiring knowledge of either the number of agents or the graph sequence; each node is only required to know its out-degree at each time. Our main result is a convergence rate of $O \left((\ln t)/t \right)$ for strongly Convex Functions with Lipschitz gradients even if only stochastic gradient samples are available; this is asymptotically faster than the $O \left((\ln t)/\sqrt{t} \right)$ rate previously known for (general) Convex Functions.