Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Hegland, Markus"'
A highly accurate and efficient method to compute the expected values of the count and sum of the centre vectors of a random maximal sized collection of non-overlapping unit diameter disks touching a fixed unit-diameter disk is presented. This extend
Externí odkaz:
http://arxiv.org/abs/2301.12115
Autor:
de Hoog, Frank, Hegland, Markus
Publikováno v:
Lin. Alg. Apps, 2023
Due to their importance in both data analysis and numerical algorithms, low rank approximations have recently been widely studied. They enable the handling of very large matrices. Tight error bounds for the computationally efficient Gaussian eliminat
Externí odkaz:
http://arxiv.org/abs/2107.01819
Autor:
Hegland, Markus, deHoog, Frank
Positive semi-definite matrices commonly occur as normal matrices of least squares problems in statistics or as kernel matrices in machine learning and approximation theory. They are typically large and dense. Thus algorithms to solve systems with su
Externí odkaz:
http://arxiv.org/abs/2011.14626
Many data-rich industries are interested in the efficient discovery and modelling of structures underlying large data sets, as it allows for the fast triage and dimension reduction of large volumes of data embedded in high dimensional spaces. The mod
Externí odkaz:
http://arxiv.org/abs/1909.12474
Autor:
de Hoog, Frank, Hegland, Markus
Publikováno v:
In Linear Algebra and Its Applications 15 July 2023 669:102-117
Publikováno v:
In Computer Physics Communications February 2022 271
We introduce the concept of fractels for functions and discuss their analytic and algebraic properties. We also consider the representation of polynomials and analytic functions using fractels, and the consequences of these representations in numeric
Externí odkaz:
http://arxiv.org/abs/1610.01369
Theorems and explicit examples are used to show how transformations between self-similar sets (general sense) may be continuous almost everywhere with respect to stationary measures on the sets and may be used to carry well known flows and spectral a
Externí odkaz:
http://arxiv.org/abs/1409.3309
This paper continues to develop a fault tolerant extension of the sparse grid combination technique recently proposed in [B. Harding and M. Hegland, ANZIAM J., 54 (CTAC2012), pp. C394-C411]. The approach is novel for two reasons, first it provides se
Externí odkaz:
http://arxiv.org/abs/1404.2670
Autor:
Flemming, Jens, Hegland, Markus
Sparsity promoting regularization is an important technique for signal reconstruction and several other ill-posed problems. Theoretical investigation typically bases on the assumption that the unknown solution has a sparse representation with respect
Externí odkaz:
http://arxiv.org/abs/1311.1923