Results 31 to 40 of about 610,785 (114)
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
On Newton‐Kantorovich Method for Solving the Nonlinear Operator Equation
Abstract and Applied Analysis, Volume 2015, Issue 1, 2015., 2015 We develop the Newton‐Kantorovich method to solve the system of 2 × 2 nonlinear Volterra integral equations where the unknown function is in logarithmic form. A new majorant function is introduced which leads to the increment of the convergence interval. The existence and uniqueness of approximate solution are proved and a numerical example is provided
Hameed Husam Hameed +4 more
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
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
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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
On the Kantorovich–Rubinstein theorem [PDF]
, 2011 The Kantorovich–Rubinstein theorem provides a formula for the Wasserstein metric W1 on the space of regular probability Borel measures on a compact metric space.
D.A. Edwards, Edwards, D.A.
core +1 more source
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
Solving Large‐Scale Unconstrained Optimization Problems with an Efficient Conjugate Gradient Class
Journal of Mathematics, Volume 2024, Issue 1, 2024.The main goal of this paper is to introduce an appropriate conjugate gradient class to solve unconstrained optimization problems. The presented class enjoys the benefits of having three free parameters, its directions are descent, and it can fulfill the Dai–Liao conjugacy condition.
Sanaz Bojari +2 more
wiley +1 more source