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Nadezda Sukhorukova
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2020 – today
- 2024
- [j13]Vinesha Peiris
, Vera Roshchina, Nadezda Sukhorukova:
Artificial neural networks with uniform norm-based loss functions. Adv. Comput. Math. 50(3): 31 (2024) - [j12]Vera Roshchina, Nadezda Sukhorukova
Uniqueness of solutions in multivariate Chebyshev approximation problems. Optim. Lett. 18(1): 33-55 (2024) - 2023
- [i4]Vinesha Peiris
A comparison of rational and neural network based approximations. CoRR abs/2303.04436 (2023) - 2022
- [j11]Nadezda Sukhorukova
A generalisation of de la Vallée-Poussin procedure to multivariate approximations. Adv. Comput. Math. 48(1): 5 (2022) - 2021
- [j10]Vinesha Peiris
Generalised rational approximation and its application to improve deep learning classifiers. Appl. Math. Comput. 389: 125560 (2021) - [i3]Vinesha Peiris, Nadezda Sukhorukova, Vera Roshchina:
Deep Learning with Nonsmooth Objectives. CoRR abs/2107.08800 (2021) - [i2]Elior Kalfon, Vinesha Peiris, Nir Sharon, Nadia Sukhorukova, Julien Ugon:
Flexible rational approximation for matrix functions. CoRR abs/2108.09357 (2021) - 2020
- [i1]R. Díaz Millán, Nadezda Sukhorukova, Julien Ugon:
An algorithm for best generalised rational approximation of continuous functions. CoRR abs/2011.02721 (2020)
2010 – 2019
- 2019
- [j9]Nadezda Sukhorukova
Two curve Chebyshev approximation and its application to signal clustering. Appl. Math. Comput. 356: 42-49 (2019) - 2017
- [j8]Jean-Pierre Crouzeix, Nadezda Sukhorukova
Characterization Theorem for Best Polynomial Spline Approximation with Free Knots, Variable Degree and Fixed Tails. J. Optim. Theory Appl. 172(3): 950-964 (2017) - 2016
- [j7]Z. Roshan Zamir, Nadezda Sukhorukova
Linear least squares problems involving fixed knots polynomial splines and their singularity study. Appl. Math. Comput. 282: 204-215 (2016) - [j6]Nadezda Sukhorukova
Chebyshev Approximation by Linear Combinations of Fixed Knot Polynomial Splines with Weighting Functions. J. Optim. Theory Appl. 171(2): 536-549 (2016) - 2015
- [j5]Z. Roshan Zamir, Nadezda Sukhorukova
Convex optimisation-based methods for K-complex detection. Appl. Math. Comput. 268: 947-956 (2015) - 2012
- [j4]Huy Quan Vu, Gang Li
K-Complex Detection Using a Hybrid-Synergic Machine Learning Method. IEEE Trans. Syst. Man Cybern. Part C 42(6): 1478-1490 (2012) - 2010
- [j3]Nadezda Sukhorukova
Uniform Approximation by the Highest Defect Continuous Polynomial Splines: Necessary and Sufficient Optimality Conditions and Their Generalisations. J. Optim. Theory Appl. 147(2): 378-394 (2010)
2000 – 2009
- 2009
- [j2]Nadezda Sukhorukova
Workload coverage through nonsmooth optimization. Optim. Methods Softw. 24(2): 285-298 (2009) - 2008
- [j1]Nadezda Sukhorukova
A generalization of the Remez algorithm to a class of linear spline approximation problems with constraints on spline parameters. Optim. Methods Softw. 23(5): 793-810 (2008)
Coauthor Index
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