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In this work we begin a theoretical and numerical investigation on the spectra of evolution operators of neutral renewal equations, with the stability of equilibria and periodic orbits in mind. We start from the simplest form of linear…

Numerical Analysis · Mathematics 2025-04-18 Dimitri Breda , Davide Liessi , Sjoerd M. Verduyn Lunel

We consider nonlinear delay differential and renewal equations with infinite delay. We extend the work of Gyllenberg et al, Appl. Math. Comput. (2018) by introducing a unifying abstract framework, and derive a finite-dimensional…

Numerical Analysis · Mathematics 2024-05-16 Francesca Scarabel , Rossana Vermiglio

This paper deals with the approximation of the spectrum of linear and nonautonomous delay differential equations through the reduction of the relevant evolution semigroup from infinite to finite dimension. The focus is placed on classic…

Numerical Analysis · Mathematics 2010-01-27 Dimitri Breda , Stefano Maset , Rossana Vermiglio

We introduce a new general framework for the approximation of evolution equations at low regularity and develop a new class of schemes for a wide range of equations under lower regularity assumptions than classical methods require. In…

Numerical Analysis · Mathematics 2021-02-16 Frédéric Rousset , Katharina Schratz

We establish spectral convergence results of approximations of unbounded non-selfadjoint linear operators with compact resolvents by operators that converge in generalized strong resolvent sense. The aim is to establish general assumptions…

Spectral Theory · Mathematics 2016-04-27 Sabine Bögli

The article addresses the convergence of implicit and semi-implicit, fully discrete approximations of a class of nonlinear parabolic evolution problems. Such schemes are popular in the numerical solution of evolutions defined with the…

Numerical Analysis · Mathematics 2019-02-22 Sören Bartels , Michael Růžička

Stability and convergence of full discretizations of various surface evolution equations are studied in this paper. The proposed discretization combines a higher-order evolving-surface finite element method (ESFEM) for space discretization…

Numerical Analysis · Mathematics 2018-02-08 Balázs Kovács , Christian Lubich

We address in this paper the approximation problem of distributed delays. Such elements are convolution operators with kernel having bounded support, and appear in the control of time-delay systems. From the rich literature on this topic,…

Optimization and Control · Mathematics 2010-09-10 Hao Lu , Michael Di Loreto , Damien Eberard , Jean-Pierre Simon

This paper considers a large class of linear operator equations, including linear boundary value problems for partial differential equations, and treats them as linear recovery problems for objects from their data. Well-posedness of the…

Numerical Analysis · Mathematics 2014-03-17 Robert Schaback

A general framework for the numerical approximation of evolution problems is presented that allows to preserve exactly an underlying Hamiltonian- or gradient structure. The approach relies on rewriting the evolution problem in a particular…

Numerical Analysis · Mathematics 2018-12-12 Herbert Egger

Using the theory of evolutionary equations, we consider abstract differential equations including non-local integral operators. After providing a condition for the well-posedness of the addressed equation we consider a numerical method of…

Numerical Analysis · Mathematics 2026-01-19 Sebastian Franz , Sascha Trostorff

We provide a solution to the heretofore open problem of stabilization of systems with arbitrarily long delays at the input and output of a nonlinear system using output feedback only. The solution is global, employs the predictor approach…

Optimization and Control · Mathematics 2013-08-15 Iasson Karafyllis , Miroslav Krstic

Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overparameterization. As alternative approaches, techniques based on…

Machine Learning · Computer Science 2022-03-22 Jiaye Teng , Jianhao Ma , Yang Yuan

We study a conical extension of averaged nonexpansive operators and the role it plays in convergence analysis of fixed point algorithms. Various properties of conically averaged operators are systematically investigated, in particular, the…

Optimization and Control · Mathematics 2020-12-01 Sedi Bartz , Minh N. Dao , Hung M. Phan

We present a generalized version of the discretization-invariant neural operator and prove that the network is a universal approximation in the operator sense. Moreover, by incorporating additional terms in the architecture, we establish a…

Numerical Analysis · Mathematics 2023-07-20 Zecheng Zhang , Wing Tat Leung , Hayden Schaeffer

We prove stability and convergence of a full discretization for a class of stochastic evolution equations with super-linearly growing operators appearing in the drift term. This is done using the recently developed tamed Euler method, which…

Probability · Mathematics 2015-08-14 István Gyöngy , Sotirios Sabanis , David Šiška

We establish a discrete operator--theoretic framework for the analysis of implicit Euler and Lie--Trotter splitting schemes for delay differential equations (DDEs). Both schemes are formulated in terms of discrete resolvent operators acting…

Optimization and Control · Mathematics 2026-03-03 Hideki Kawahara

In the empirical study of evolutionary algorithms, the solution quality is evaluated by either the fitness value or approximation error. The latter measures the fitness difference between an approximation solution and the optimal solution.…

Neural and Evolutionary Computing · Computer Science 2018-10-30 Jun He , Yu Chen , Yuren Zhou

Functional evolution equations are used in the modeling of numerous physical processes. In this work, our main tool is perturbation theory of strongly continuous semigroups. The advantage of this technique is that one can provide functional…

Functional Analysis · Mathematics 2022-06-28 Ismail T. Huseynov , Nazim I. Mahmudov

Identifying differential operators from data is essential for the mathematical modeling of complex physical and biological systems where massive datasets are available. These operators must be stable for accurate predictions for dynamics…

Numerical Analysis · Mathematics 2024-05-02 Aviral Prakash , Yongjie Jessica Zhang
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