Results 31 to 40 of about 7,544 (125)
Properties and Applications of Symmetric Quantum Calculus
Fractal and FractionalSymmetric derivatives and integrals are extensively studied to overcome the limitations of classical derivatives and integral operators. In the current investigation, we explore the quantum symmetric derivatives on finite intervals.
Miguel Vivas-Cortez +4 more
doaj +1 more source
Heights, Regulators and Schinzel's determinant inequality
, 2016 We prove inequalities that compare the size of an S-regulator with a product of heights of multiplicatively independent S-units. Our upper bound for the S-regulator follows from a general upper bound for the determinant of a real matrix proved by ...
Akhtari, Shabnam, Vaaler, Jeffrey D.
core +1 more source
Validation of machine learning based scenario generators
Journal of Risk and Insurance, EarlyView.Abstract Machine learning (ML) methods are becoming increasingly important for designing economic scenario generators for internal models. Validating data‐driven models requires different methods than validating classical, theory‐based models. We discuss two novel aspects of such validation: first, checking the multivariate distribution of risk factors,
Gero Junike, Solveig Flaig, Ralf Werner
wiley +1 more source
Refined and Generalized Versions of Hölder’s Inequality via Schur Convexity of Functions
Journal of Function SpacesIn this paper, we introduce a class of functions associated with Hölder’s inequality and show the Schur convexities of these functions. With the help of Schur convexity, several improved versions of Hölder’s inequality are established.
Shanhe Wu
doaj +1 more source
International Journal of Robust and Nonlinear Control, Volume 36, Issue 7, Page 3896-3913, 10 May 2026.ABSTRACT This paper focuses on state estimation for a fairly general class of systems, involving nonlinear functions and disturbances in both the process dynamics and output equations. A nonlinear observer that satisfies a H∞$$ {\boldsymbol{H}}_{\boldsymbol{\infty}} $$ disturbance attenuation constraint in addition to providing asymptotic stability in ...
Hamidreza Movahedi +2 more
wiley +1 more source
Generalized Dunkl-Lipschitz Spaces [PDF]
, 2014 This paper deals with generalized Lipschitz spaces $\wedge^k_{\alpha,p,q}(\R)$ in the context of Dunkl harmonic analysis on $\R$, for all real $\alpha$. It also introduces a generalized Dunkl-Lipschitz spaces ${\cal T}\wedge^k_{\alpha,p,q}(\R^2_+)$ of $k$
Kallel, Samir
core
In‐and‐Out: Algorithmic Diffusion for Sampling Convex Bodies
Random Structures &Algorithms, Volume 68, Issue 3, May 2026.ABSTRACT We present a new random walk for uniformly sampling high‐dimensional convex bodies. It achieves state‐of‐the‐art runtime complexity with stronger guarantees on the output than previously known, namely in Rényi divergence (which implies TV, 𝒲2, KL, χ2$$ {\chi}^2 $$).
Yunbum Kook +2 more
wiley +1 more source
Tensor Changepoint Detection and Eigenbootstrap
Journal of Time Series Analysis, Volume 47, Issue 3, Page 557-578, May 2026.ABSTRACT Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross‐sectional dependence are considered. A completely distribution‐free and tweaking‐parameter‐free detection procedure for changepoints at different locations is designed, which does not require training data.
Michal Pešta +2 more
wiley +1 more source
Journal of Inequalities and Applications, 2019 In this paper, based on the existing Hölder’s inequality, some new three-tuple diamond-alpha integral Hölder’s inequalities on time scales are proposed and the related theorems and corollaries are given.
Fei Yan, Jianfeng Wang
doaj +1 more source
Robust Inverse Material Design With Physical Guarantees Using the Voigt‐Reuss Net
International Journal for Numerical Methods in Engineering, Volume 127, Issue 7, 15 April 2026.ABSTRACT We apply the Voigt‐Reuss net, a spectrally normalized neural surrogate introduced in [38], for forward and inverse mechanical homogenization with a key guarantee that all predicted effective stiffness tensors satisfy Voigt‐Reuss bounds in the Löwner sense during training, inference, and gradient‐driven optimization.
Sanath Keshav, Felix Fritzen
wiley +1 more source