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Many interesting functions arising in applications map into Riemannian manifolds. We present an algorithm, using the manifold exponential and logarithm, for approximating such functions. Our approach extends approximation techniques for…

Numerical Analysis · Mathematics 2026-01-27 Simon Jacobsson , Raf Vandebril , Joeri van der Veken , Nick Vannieuwenhoven

We propose an algorithm for solving of the graph isomorphism problem. Also, we introduce the new class of graphs for which the graph isomorphism problem can be solved polynomially using the algorithm.

Combinatorics · Mathematics 2007-05-23 Rashit T. Faizullin , Alexander V. Prolubnikov

Several techniques were proposed to model the Piecewise linear (PWL) functions, including convex combination, incremental and multiple choice methods. Although the incremental method was proved to be very efficient, the attention of the…

Optimization and Control · Mathematics 2018-02-13 Mutaz Tuffaha , Jan Tommy Gravdahl

This study reexamines diffusive representations for fractional integrals with the goal of pioneering new variants of such representations. These variants aim to offer highly efficient numerical algorithms for the approximate computation of…

Numerical Analysis · Mathematics 2025-07-08 Renu Chaudhary , Kai Diethelm

Piecewise Polynomials (PPs) are utilized in several engineering disciplines, like trajectory planning, to approximate position profiles given in the form of a set of points. While the approximation target along with domain-specific…

Machine Learning · Computer Science 2024-05-09 Hannes Waclawek , Stefan Huber

In this paper, we present a new type of $\alpha-$Bernstein-P\u{a}lt\u{a}nea operators having a better order of approximation than itself. We establish some approximation results concerning the rate of convergence, error estimation and…

Classical Analysis and ODEs · Mathematics 2020-06-05 Jaspreet Kaur , Meenu Goyal

The theory of Chebyshev approximation has been extensively studied. In most cases, the optimality conditions are based on the notion of alternance or alternating sequence (that is, maximal deviation points with alternating deviation signs).…

Functional Analysis · Mathematics 2025-01-30 Nadezda Sukhorukova , Julien Ugon

The purpose of this text is to provide an accessible introduction to a set of recently developed algorithms for factorizing matrices. These new algorithms attain high practical speed by reducing the dimensionality of intermediate…

Numerical Analysis · Mathematics 2019-02-08 Per-Gunnar Martinsson

The aim of this article is to study the role of piecewise implementation of Pad\'e-Chebyshev type approximation in minimising Gibbs phenomena in approximating piecewise smooth functions. A piecewise Pad\'e-Chebyshev type (PiPCT) algorithm…

Numerical Analysis · Mathematics 2019-10-24 S. Akansha , S. Baskar

We present effective numerical algorithms for locally recovering unknown governing differential equations from measurement data. We employ a set of standard basis functions, e.g., polynomials, to approximate the governing equation with high…

Numerical Analysis · Mathematics 2020-05-05 Kailiang Wu , Dongbin Xiu

We provide a polynomial time 4/3 approximation algorithm for TSP on metrics arising from the metric completion of cubic 3-edge connected graphs.

Data Structures and Algorithms · Computer Science 2011-01-31 Nishita Aggarwal , Naveen Garg , Swati Gupta

We combine the known methods for univariate polynomial root-finding and for computations in the Frobenius matrix algebra with our novel techniques to advance numerical solution of a univariate polynomial equation, and in particular…

Numerical Analysis · Mathematics 2013-11-26 Victor Y. Pan , Ai-Long Zheng

In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tucker format. We develop novel randomized techniques to…

Numerical Analysis · Mathematics 2021-07-29 Arvind K. Saibaba , Rachel Minster , Misha E. Kilmer

We present new results on Boolean matrix factorization and a new algorithm based on these results. The results emphasize the significance of factorizations that provide from-below approximations of the input matrix. While the previously…

Numerical Analysis · Computer Science 2015-06-26 Radim Belohlavek , Martin Trnecka

The construction of a multilayer perceptron (MLP) as a piecewise low-order polynomial approximator using a signal processing approach is presented in this work. The constructed MLP contains one input, one intermediate and one output layers.…

Machine Learning · Computer Science 2020-10-16 Ruiyuan Lin , Suya You , Raghuveer Rao , C. -C. Jay Kuo

Schemes for exact multiplication of small matrices have a large symmetry group. This group defines an equivalence relation on the set of multiplication schemes. There are algorithms to decide whether two schemes are equivalent. However, for…

Computational Complexity · Computer Science 2022-06-02 Manuel Kauers , Jakob Moosbauer

In this paper, we revisit approximation properties of piecewise polynomial spaces, which contain more than ${\cal P}_{r-1}$ but not ${\cal P}_r$. We develop more accurate upper and lower error bounds that are sharper than those used in…

Numerical Analysis · Mathematics 2015-02-17 Hehu Xie , Zhimin Zhang

An algorithm is presented for numerical computation of choreographies in the plane in a Newtonian potential and on the sphere in a cotangent potential. It is based on stereographic projection, approximation by trigonometric polynomials, and…

Numerical Analysis · Mathematics 2015-11-19 Hadrien Montanelli , Nikola Ivanov Gushterov

It is a classical result in rational approximation theory that certain non-smooth or singular functions, such as $|x|$ and $x^{1/p}$, can be efficiently approximated using rational functions with root-exponential convergence in terms of…

Numerical Analysis · Mathematics 2025-06-27 Kingsley Yeon , Steven B. Damelin

Stochastic approximation techniques have been used in various contexts in data science. We propose a stochastic version of the forward-backward algorithm for minimizing the sum of two convex functions, one of which is not necessarily…

Optimization and Control · Mathematics 2016-02-26 Patrick L. Combettes , Jean-Christophe Pesquet
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