Results 121 to 130 of about 50,520 (161)
Is exponential stability achievable in singular perturbed delayed systems with time-varying parameters? A comprehensive analysis. [PDF]
Chen R, Ouyang M, Zhang J, Masoudinia F.
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On the use of elliptic PDEs for the parameterisation of planar multipatch domains: Part 1: Numerical algorithms and foundations of parametric control. [PDF]
Hinz J, Buffa A.
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On O ( n ) algorithms for projection onto the top- k -sum sublevel set. [PDF]
Roth J, Cui Y.
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Generalised Entropy Accumulation. [PDF]
Metger T, Fawzi O, Sutter D, Renner R.
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Schur-convexity and Schur-geometrically concavity of Gini means [PDF]
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Huan-nan Shi
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Schur-convexity and the Simpson formula
The main objective of this work is to give a necessary and sufficient condition for the function defined as the difference of the Simpson quadrature rule and the arithmetic integral mean to be Schur-convex.
Josip Pečarić, Iva Franjic
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The NLMS Is Steady-State Schur-Convex
IEEE Signal Processing Letters, 2021In this work, we study the impact of input-spread on the steady-state excess mean squared error (EMSE) of the normalized least mean squares (NLMS) algorithm. First, we use the concept of majorization to order the input-regressors according to their spread.
Anum Ali +2 more
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Strong Schur-Convexity of the Integral Mean
Results in Mathematics, 2023The strong Schur-convexity of the integral mean as well as of the left and right gaps in the Hermite- Hadamard inequality for strongly convex functions are proved. An useful characterization of strongly Schur convex functions F : I-n -> R by partial derivatives is given.
Mirosław Adamek +2 more
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Majorization and Schur Convexity with Respect to Partial Orders
Mathematics of Operations Research, 1993Majorization and Schur convexity constitute an important tool for establishing inequalities, in particular, due to the Schur-Ostrowski Theorem which provides a simple characterization of Schur convex functions through local two-coordinate conditions. However, the usefulness of the approach is limited by two stringent requirements.
Frank K Hwang, Uriel Rothblum
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