Zobrazeno 1 - 10
of 3 629
pro vyhledávání: '"low-discrepancy sequences"'
Autor:
Weiß, Christian
The classic example of a low-discrepancy sequence in $\mathbb{Z}_p$ is $(x_n) = an+b$ with $a \in \mathbb{Z}_p^x$ and $b \in \mathbb{Z}_p$. Here we address the non-linear case and show that a polynomial $f$ generates a low-discrepancy sequence in $\m
Externí odkaz:
http://arxiv.org/abs/2406.09114
Autor:
Ortiz-Tapia, Arturo
The Paul Erd\H{o}s-Tur\'an inequality is used as a quantitative form of Weyl' s criterion, together with other criteria to asses equidistribution properties on some patterns of sequences that arise from indexation of prime numbers, Jumping Champions
Externí odkaz:
http://arxiv.org/abs/2306.00161
Autor:
Schmiedt, Anja, Weiß, Christian
In any dimension $d \geq 2$, there is no known example of a low-discrepancy sequence which possess Poisssonian pair correlations. This is in some sense rather surprising, because low-discrepancy sequences always have $\beta$-Poissonian pair correlati
Externí odkaz:
http://arxiv.org/abs/2211.09891
Autor:
Schmiedt, Anja, Weiß, Christian
Publikováno v:
In Journal of Number Theory June 2024 259:422-437
Heterogeneous comprehensive learning particle swarm optimization (HCLPSO) is a type of evolutionary algorithm with enhanced exploration and exploitation capabilities. The low-discrepancy sequence (LDS) is more uniform in covering the search space tha
Externí odkaz:
http://arxiv.org/abs/2209.09438
Publikováno v:
In Computer Communications 1 November 2023 211:24-36
Artificial neural networks can be represented by paths. Generated as random walks on a dense network graph, we find that the resulting sparse networks allow for deterministic initialization and even weights with fixed sign. Such networks can be train
Externí odkaz:
http://arxiv.org/abs/2103.03543
The paper addresses the problem of defining families of ordered sequences $\{x_i\}_{i\in N}$ of elements of a compact subset $X$ of $R^d$ whose prefixes $X_n=\{x_i\}_{i=1}^{n}$, for all orders $n$, have good space-filling properties as measured by th
Externí odkaz:
http://arxiv.org/abs/2106.05833
We propose a deep supervised learning algorithm based on low-discrepancy sequences as the training set. By a combination of theoretical arguments and extensive numerical experiments we demonstrate that the proposed algorithm significantly outperforms
Externí odkaz:
http://arxiv.org/abs/2005.12564
Conference
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