Results 51 to 60 of about 11,230,834 (184)
IET Smart Grid, Volume 9, Issue 1, January/December 2026.The proposed LA‐DROPF framework integrates graph neural network surrogates with Wasserstein distributionally robust optimisation and CVaR tail‐risk control for coordinated transmission—distribution dispatch under deep renewable uncertainty. A hybrid Benders—ADMM decomposition enables privacy‐preserving multi‐area coordination with formal convergence ...
Aamir Nawaz +2 more
wiley +1 more source
Journal of Applied Mathematics, Volume 2026, Issue 1, 2026.The work is aimed at proposing a novel iterative technique based on the combination of two powerful Taylor′s and Bernoulli polynomials to solve nonlinear Volterra–Fredholm integral equations of the 2nd kind. The main contribution of this work is the development of a Bernoulli–Taylor iterative framework in which Bernoulli projection is combined with ...
Heba A. Abd-Alrazak +4 more
wiley +1 more source
Identification and estimation of continuous‐time dynamic discrete choice games
Quantitative Economics, Volume 17, Issue 1, Page 254-296, January 2026.This paper considers the theoretical, computational, and econometric properties of continuous‐time dynamic discrete choice games with stochastically sequential moves, introduced by Arcidiacono, Bayer, Blevins, and Ellickson (2016). We consider identification of the rate of move arrivals, which was assumed to be known in previous work, as well as a ...
Jason R. Blevins
wiley +1 more source
International Journal for Numerical Methods in Engineering, Volume 126, Issue 24, 30 December 2025.ABSTRACT This study presents a new optimized block hybrid method and spectral simple iteration method (OBHM‐SSIM) for solving nonlinear evolution equations. In this method, we employed a combination of the spectral collocation method in space and the optimized block hybrid method in time, along with a simple iteration scheme to linearize the equations.
Salma Ahmedai +4 more
wiley +1 more source
Approximate solution of a nonlinear system of integral equations using modified Newton-Kantorovich method [PDF]
, 2010 Modified Newton-Kantorovich method is developed to obtain an approximate solution for a system of nonlinear integral equations. The system of nonlinear integral equations is reduced to find the roots of nonlinear integral operator.
Ahmedov, Anvarjon +3 more
core +2 more sources
Multivariate Neural Network Operators: Simultaneous Approximation and Voronovskaja‐Type Theorem
Mathematical Methods in the Applied Sciences, Volume 48, Issue 11, Page 10669-10677, 30 July 2025.ABSTRACT In this paper, the simultaneous approximation and a Voronoskaja‐type theorem for the multivariate neural network operators of the Kantorovich type have been proved. In order to establish such results, a suitable multivariate Strang–Fix type condition has been assumed.
Marco Cantarini, Danilo Costarelli
wiley +1 more source
A Newton-Kantorovich method for a functional equation relative to the conformal representation
Analysis, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
LANZA DE CRISTOFORIS, MASSIMO +1 more
openaire +3 more sources
Using decomposition of the nonlinear operator for solving non‐differentiable problems
Mathematical Methods in the Applied Sciences, Volume 48, Issue 7, Page 7987-8006, 15 May 2025.Starting from the decomposition method for operators, we consider Newton‐like iterative processes for approximating solutions of nonlinear operators in Banach spaces. These iterative processes maintain the quadratic convergence of Newton's method.
Eva G. Villalba +3 more
wiley +1 more source
Journal of Computational and Applied Mathematics, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ioannis K. Argyros, Saïd Hilout
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Neural‐network‐based regularization methods for inverse problems in imaging
GAMM-Mitteilungen, Volume 47, Issue 4, November 2024.Abstract This review provides an introduction to—and overview of—the current state of the art in neural‐network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for ...
Andreas Habring, Martin Holler
wiley +1 more source