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In order to obtain stable and accurate general relativistic simulations, re-formulations of the Einstein equations are necessary. In a series of our works, we have proposed using eigenvalue analysis of constraint propagation equations for…

General Relativity and Quantum Cosmology · Physics 2017-08-23 Gen Yoneda , Hisa-aki Shinkai

Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, solving a long-standing open…

Machine Learning · Computer Science 2023-08-22 Marcel Wienöbst , Max Bannach , Maciej Liśkiewicz

We propose an efficient algorithm for computing a common eigenvector of a finite set of square matrices. As an immediate consequence we obtain an algorithm for determining whether the matrices admit a simultaneous triangulation, and, if so,…

Rings and Algebras · Mathematics 2023-09-27 Emanuel Malvetti

Probabilistic concurrent systems are foundational models for modern mobile computing. In this paper, a unifying approach to probabilistic testing equivalences is proposed. With the help of a new distribution-based semantics for…

Logic in Computer Science · Computer Science 2026-04-08 Weijun Chen , Yuxi Fu , Huan Long , Hao Wu

The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several…

Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper, we show that these tasks can be performed in polynomial time, solving a…

Machine Learning · Computer Science 2020-12-18 Marcel Wienöbst , Max Bannach , Maciej Liśkiewicz

Statistical Model Checking (SMC) is a trade-off between testing and formal verification. The core idea of the approach is to conduct some simulations of the system and verify if they satisfy some given property. In this paper we show that…

Software Engineering · Computer Science 2011-11-03 Peter Bulychev , Alexandre David , Kim Guldstrand Larsen , Marius Mikučionis , Axel Legay

We generalise the inference procedure for eigenvectors of symmetrizable matrices of Tyler (1981) to that of invariant and singular subspaces of non-diagonalizable matrices. Wald tests for invariant vectors and $t$-tests for their individual…

Statistics Theory · Mathematics 2025-10-13 Jérôme R. Simons

Motivated by the recent demonstration of its use as a tool for the detection and characterization of phase-shape correlations in multivariate time series, we show that eigenvalue decomposition can also be applied to a matrix of indices of…

Data Analysis, Statistics and Probability · Physics 2008-09-03 Carsten Allefeld , Markus Müller , Jürgen Kurths

We present a technique designed for parallelizing large rigid body simulations, capable of exploiting multiple CPU cores within a computer and across a network. Our approach can be applied to simulate both unilateral and bilateral…

Graphics · Computer Science 2024-03-27 Manas Kale , Paul G. Kry

The simulation of three dimensional magnetostatic problems plays an important role, for example when simulating synchronous electric machines. Building on prior work that developed a domain decomposition algorithm using isogeometric…

Computational Engineering, Finance, and Science · Computer Science 2025-01-09 Mario Mally , Melina Merkel

This note presents a summary and review of various conditions and characterizations for matrix stability (in particular diagonal matrix stability) and matrix stabilizability.

Systems and Control · Electrical Eng. & Systems 2023-01-04 Zhiyong Sun

We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. The key idea is simple, i.e., we first transform each…

Methodology · Statistics 2026-01-28 Jinyuan Chang , Yue Du , Jing He , Qiwei Yao

Many applications in network analysis require algorithms to sample uniformly at random from the set of all graphs with a prescribed degree sequence. We present a Markov chain based approach which converges to the uniform distribution of all…

Discrete Mathematics · Computer Science 2010-03-05 Annabell Berger , Matthias Müller-Hannemann

In this paper, we propose two novel approaches for hypergraph comparison. The first approach transforms the hypergraph into a graph representation for use of standard graph dissimilarity measures. The second approach exploits the…

Machine Learning · Computer Science 2021-06-16 Amit Surana , Can Chen , Indika Rajapakse

This paper proposes a novel framework to test for slope heterogeneity between time-varying coefficients in panel data models. Our test not only allows us to detect whether the coefficient functions are the same across all units or not, but…

Econometrics · Economics 2025-11-18 Marina Khismatullina , Bernhard van der Sluis

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view to the pros and cons of each algorithm mainly due to the intertwining…

Testing for independence between graphs is a problem that arises naturally in social network analysis and neuroscience. In this paper, we address independence testing for inhomogeneous Erd\H{o}s-R\'{e}nyi random graphs on the same vertex…

Methodology · Statistics 2023-04-19 Yukun Song , Carey E. Priebe , Minh Tang

Over the last decade, implementations of several desingularization algorithms have appeared in various contexts. These differ as widely in their methods and in their practical efficiency as they differ in the situations in which they may be…

Algebraic Geometry · Mathematics 2011-09-09 Rocio Blanco , Anne Frühbis-Krüger

A new method based on the rejection sampling for finding statistical tests is proposed. This method is conceptually intuitive, easy to implement, and applicable for arbitrary dimension. To illustrate its potential applicability, three…

Methodology · Statistics 2026-03-11 Markku Kuismin
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