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We propose a framework for computing, optimizing and integrating with respect to a smooth marginal likelihood in statistical models that involve high-dimensional parameters/latent variables and continuous low-dimensional hyperparameters.…

Methodology · Statistics 2026-02-10 Omiros Papaspiliopoulos , Timothée Stumpf-Fétizon , Jonathan Weare

Consider the set of solutions to a system of polynomial equations in many variables. An algebraic manifold is an open submanifold of such a set. We introduce a new method for computing integrals and sampling from distributions on algebraic…

Algebraic Geometry · Mathematics 2020-03-10 Paul Breiding , Orlando Marigliano

We present a comprehensive extension of the latent position network model known as the random dot product graph to accommodate multiple graphs -- both undirected and directed -- which share a common subset of nodes, and propose a method for…

Machine Learning · Statistics 2021-01-26 Andrew Jones , Patrick Rubin-Delanchy

Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow…

Machine Learning · Statistics 2021-11-17 Patrick Rubin-Delanchy , Joshua Cape , Minh Tang , Carey E. Priebe

Differentiable systems in this paper means systems of equations that are described by differentiable real functions in real matrix variables. This paper proposes algorithms for finding minimal rank solutions to such systems over (arbitrary…

Optimization and Control · Mathematics 2017-05-30 Thanh Hieu Le

In this paper, we develop a new technique which we call representation theory of the real hyperrectangle, which describes how to compute the eigenvectors and eigenvalues of certain matrices arising from hyperrectangles. We show that these…

Computational Geometry · Computer Science 2021-08-06 Josh Alman , Timothy Chu , Gary Miller , Shyam Narayanan , Mark Sellke , Zhao Song

Random Ising systems on a general hierarchical lattice with both, random fields and random bonds, are considered. Rigorous inequalities between eigenvalues of the Jacobian renormalization matrix at the pure fixed point are obtained. These…

Statistical Mechanics · Physics 2018-09-19 Avishay Efrat , Moshe Schwartz

Effective Hamiltonians can explain in a much simpler way the physics behind a scattering process. Chaotic scattering is directly related to Lorentzian Hamiltonians which, because of their properties, can be reduced to a $2\times 2$ matrix…

Disordered Systems and Neural Networks · Physics 2013-04-03 Adel Abbout , Peng Mei

We present a new solution of the asymmetric two-matrix model in the large $N$ limit which only involves a saddle point analysis. The model can be interpreted as Ising in the presence of a magnetic field, on random dynamical lattices with…

Statistical Mechanics · Physics 2007-05-23 P. Zinn-Justin

Several mean-field theories predict that Hessian matrices of amorphous solids can be written by using the random matrix in the limit of the large spatial dimensions $d\to\infty$. Motivated by these results, we here propose a way to map a…

Disordered Systems and Neural Networks · Physics 2022-08-31 Harukuni Ikeda , Masanari Shimada

A fundamental step in the rational design of vascular targeted particles is the firm adhesion at the blood vessel walls. Here, a combined Lattice Boltzmann Immersed Boundary model is presented for predicting the near wall dynamics of…

Fluid Dynamics · Physics 2018-08-07 A. Coclite , H. Mollica , S. Ranaldo , G. Pascazio , M. D. De Tullio , P. Decuzzi

We consider products of $n$ random Hermitian matrices which generalize the one-matrix model and show its relation to Hurwitz numbers which count ramified coverings of certain type. Namely, these Hurwitz numbers count $2k$-fold ramified…

Mathematical Physics · Physics 2026-01-01 Ch. Li , A. Yu. Orlov

For a random matrix of entries sampled independently from a fairly general distribution in Z we study the probability that the cokernel is isomorphic to a given finite abelian group, or when it is cyclic. This includes the probability that…

Probability · Mathematics 2018-06-05 Hoi H. Nguyen , Melanie Matchett Wood

We discuss a geometrical interpretation of the Z-invariant Ising model in terms of isoradial embeddings of planar lattices. The Z-invariant Ising model can be defined on an arbitrary planar lattice if and only if certain paths on the…

Statistical Mechanics · Physics 2007-05-23 Ruben Costa-Santos

We construct ensembles of random integrable matrices with any prescribed number of nontrivial integrals and formulate integrable matrix theory (IMT) -- a counterpart of random matrix theory (RMT) for quantum integrable models. A type-M…

Mesoscale and Nanoscale Physics · Physics 2016-05-20 Emil A. Yuzbashyan , B. Sriram Shastry , Jasen A. Scaramazza

The space of deformations of the integer Heisenberg group under the action of $\textrm{Aut}(H(\mathbb{R}))$ is a homogeneous space for a non-reductive group. We analyze its structure as a measurable dynamical system and obtain mean and…

Number Theory · Mathematics 2016-04-19 Jayadev S. Athreya , Ioannis Konstantoulas

What is the connection of random matrices with integrable systems? Is this connection really useful? Introducing apprpriate times in the distribution of the ensemble of matrices, one shows that the corresponding distribution of the…

solv-int · Physics 2008-02-03 Pierre van Moerbeke

Hex systems were recently introduced [A. P. Kels. Integrable systems on hexagonal lattices and consistency on polytopes with quadrilateral and hexagonal faces. 2022. arXiv:2205.02720 [math-ph]] as systems of equations defined on…

Exactly Solvable and Integrable Systems · Physics 2025-05-01 Giorgio Gubbiotti , Andrew P. Kels , Claude-M. Viallet

Many models for chaotic systems consist of joining two integrable systems with incompatible constants of motion. The quantum counterparts of such models have a propagator which factorizes into two integrable parts. Each part can be…

Chaotic Dynamics · Physics 2009-10-31 Tomaz Prosen , Thomas H. Seligman , Hans A. Weidenmueller

The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to…

Machine Learning · Computer Science 2018-06-20 Guido Montufar