2 citations · 5 across the 4 of their papers we have counts for
7 papers
Deep Learning with Nonsmooth Objectives
Vinesha Peiris, Nadezda Sukhorukova, Vera Roshchina
We explore the potential for using a nonsmooth loss function based on the max-norm in the training of an artificial neural network. We hypothesise that this may lead to superior cl…
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…
Uniqueness of solutions in multivariate Chebyshev approximation problems
Vera Roshchina, Nadia Sukhorukova, Julien Ugon
We study the solution set to multivariate Chebyshev approximation problem, focussing on the ill-posed case when the uniqueness of solutions can not be established via strict polyno…
Solving multi-resource allocation and location problems in disaster management through linear programming
Behrooz Bodaghi, Nadezda Sukhorukova
In this paper we propose a new efficient linear programming based approach for multi-resource allocation and location problems in disaster management. Such problems require an inte…
Two curve Chebyshev approximation and its application to signal clustering
Nadezda Sukhorukova
In this paper we extend a number of important results of the classical Chebyshev approximation theory to the case of simultaneous approximation of two or more functions. The need f…