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Difference of Convex (DC) approach for neural network approximation with uniform loss function
Vinesha Peiris, Nadezda Sukhorukova
Neural networks (NNs) can be viewed as approximation tools. Traditionally, NNs are relying on gradient and stochastic gradient (SG) methods. There are a number of available computa…
KKT-based optimality conditions for neural network approximation
Vinesha Peiris, Nadezda Sukhorukova, Julien Ugon
In this paper, we obtain necessary optimality conditions for neural network approximation. We consider neural networks in Manhattan ( norm) and Chebyshev ( norm). The op…
Nonsmooth Optimisation and neural networks
Vinesha Peiris, Nadezda Sukhorukova
In this paper, we study neural networks from the point of view of nonsmooth optimisation, namely, quasidifferential calculus. We restrict ourselves to the case of uniform approxima…
Bivariate rational approximations of the general temperature integral
Alireza Aghili, Nadezda Sukhorukova, Julien Ugon
The non-isothermal analysis of materials with the application of the Arrhenius equation involves temperature integration. If the frequency factor in the Arrhenius equation depends…
The extension of linear inequality method for generalised rational Chebyshev approximation to approximation by general quasilinear functions
Vinesha Peiris, Nadezda Sukhorukova
In this paper we demonstrate that a well known linear inequality method developed for rational Chebyshev approximation is equivalent to the application of the bisection method used…
An algorithm for best generalised rational approximation of continuous functions
R. Díaz Millán, Nadezda Sukhorukova, Julien Ugon
The motivation of this paper is the development of an optimisation method for solving optimisation problems appearing in Chebyshev rational and generalised rational approximation p…