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We study spectra and localization properties of Euclidean random matrices. The problem is approximately mapped onto that of a matrix defined on a random graph. We introduce a powerful method to find the density of states and the…

Statistical Mechanics · Physics 2009-11-10 S. Ciliberti , T. S. Grigera , V. Martin-Mayor , G. Parisi , P. Verrocchio

Measurement system analysis aims to quantify the variability in data attributable to the measurement system and evaluate its contribution to overall data variability. This paper conducts a rigorous theoretical investigation of the…

Applications · Statistics 2025-01-31 Banafsheh Lashkari , Shojaeddin Chenouri

Singular equations with rank-deficient Jacobians arise frequently in algebraic computing applications. As shown in case studies in this paper, direct and intuitive modeling of algebraic problems often results in nonisolated singular…

Numerical Analysis · Mathematics 2021-02-19 Zhonggang Zeng

Understanding NP-complete problems is a central topic in computer science. This is why adiabatic quantum optimization has attracted so much attention, as it provided a new approach to tackle NP-complete problems using a quantum computer.…

Quantum Physics · Physics 2010-12-13 Boris Altshuler , Hari Krovi , Jeremie Roland

In this article, we analyse a stabilised equal-order finite element approximation for the Stokes equations on anisotropic meshes. In particular, we allow arbitrary anisotropies in a sub-domain, for example along the boundary of the domain,…

Numerical Analysis · Mathematics 2018-10-12 Stefan Frei

More than 30 years ago Edwards and co-authors proposed a model to describe the statistics of granular packings by an ensemble of equiprobable jammed states. Experimental tests of this model remained scarce so far. We introduce a simple…

The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means…

Methodology · Statistics 2019-05-28 Anja Janßen , Phyllis Wan

In statistical mechanics, evaluating finite-size macroscopic fluctuations typically relies on Edgeworth expansions. However, these perturbative methods append additive polynomial corrections that inevitably break down in the large deviation…

Information Theory · Computer Science 2026-04-15 Hiroki Suyari

Finding a maximum independent set is a fundamental NP-hard problem that is used in many real-world applications. Given an unweighted graph, this problem asks for a maximum cardinality set of pairwise non-adjacent vertices. Some of the most…

Data Structures and Algorithms · Computer Science 2021-03-30 Demian Hespe , Sebastian Lamm , Christian Schorr

The choice of the parameter value for regularized inverse problems is critical to the results and remains a topic of interest. This article explores a criterion for selecting a good parameter value by maximizing the probability of the data,…

Numerical Analysis · Mathematics 2020-02-11 Toby Sanders , Rodrigo B. Platte , Robert D. Skeel

We consider the dynamics of an arbitrary quantum system coupled to a large arbitrary and fully quantum mechanical environment through a random interaction. We establish analytically and check numerically the typicality of this dynamics, in…

Quantum Physics · Physics 2017-07-26 Grégoire Ithier , Florent Benaych-Georges

Geometric modeling by constraints leads to large systems of algebraic equations. This paper studies bipartite graphs underlaid by systems of equations. It shows how these graphs make possible to polynomially decompose these systems into…

Discrete Mathematics · Computer Science 2014-05-26 Samy Ait-Aoudia , Roland Jegou , Dominique Michelucci

As machine learning applications grow increasingly ubiquitous and complex, they face an increasing set of requirements beyond accuracy. The prevalent approach to handle this challenge is to aggregate a weighted combination of requirement…

Machine Learning · Computer Science 2026-01-07 Aneesh Barthakur , Luiz F. O. Chamon

We consider a Statistical Mechanics approach to granular systems by following the original ideas developed by Edwards. We use the concept of ``inherent states'', defined as the stable configurations in the potential energy landscape,…

Condensed Matter · Physics 2016-08-31 Antonio Coniglio , Mario Nicodemi

Stochastic constraints, which incorporate both deterministic parameters and random variables, extend classical deterministic constraints by explicitly accounting for uncertainty. These constraints are increasingly prevalent in data science,…

Logic in Computer Science · Computer Science 2026-04-21 Xiakun Li , Hao Wu , Bican Xia , Tengshun Yang , Naijun Zhan

The mathematical objects employed in physical theories do not always behave well. Einstein's theory of space and time allows for spacetime singularities and Van Hove singularities arise in condensed matter physics, while intensity, phase…

Quantum Physics · Physics 2023-07-10 C. A. Downing , A. Vidiella-Barranco

Parameterized algorithms have been subject to extensive research of recent years and allow to solve hard problems by exploiting a parameter of the corresponding problem instances. There, one goal is to devise algorithms, where the runtime…

Logic in Computer Science · Computer Science 2019-05-07 Markus Hecher

This paper proposes a simple unified inference approach on moment restrictions in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters and…

Methodology · Statistics 2025-12-19 Xingyu Li , Xiaojun Song , Zhenting Sun

We develop a new theory for treating boundary problems for linear ordinary differential equations whose fundamental system may have a singularity at one of the two endpoints of the given interval. Our treatment follows an algebraic…

Classical Analysis and ODEs · Mathematics 2015-05-11 M. Rosenkranz , J. Liu , A. Maletzky , B. Buchberger

Periodic boundary conditions are not always used in the study of disordered systems, but it can be advantageous to apply them to mimick thermodynamically large systems. In this case, polarization and its cumulants can not be obtained…

Disordered Systems and Neural Networks · Physics 2026-04-27 Balázs Hetényi , Luís Miguel Martelo , András Lászlóffy