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We consider an incremental approximation method for solving variational problems in infinite-dimensional Hilbert spaces, where in each step a randomly and independently selected subproblem from an infinite collection of subproblems is…

Numerical Analysis · Mathematics 2018-03-06 Michael Griebel , Peter Oswald

A space-filling function is a bijection from the unit line segment to the unit square, cube, or hypercube. The function from the unit line segment is continuous. The inverse function, while well-defined, is not continuous. Space-filling…

Computational Geometry · Computer Science 2015-04-21 Aubrey Jaffer

We study a relation between brick $n$-tuples of subspaces of a finite dimensional linear space, and irreducible $n$-tuples of subspaces of a finite dimensional Hilbert (unitary) space such that a linear combination, with positive…

Functional Analysis · Mathematics 2008-07-15 Yu. S. Samoilenko , D. Y. Yakymenko

We study canonical and affine versions of the quantized covariant Euclidean free real scalar field-theory on four dimensional lattices through the Monte Carlo method. We calculate the two-point function at small values of the bare coupling…

High Energy Physics - Lattice · Physics 2021-10-28 Riccardo Fantoni , John R. Klauder

In this paper, we will consider matrices with entries in the space of operators $\mathcal{B}(H)$, where $H$ is a separable Hilbert space and consider the class of matrices that can be approached in the operator norm by matrices with a…

Functional Analysis · Mathematics 2018-10-19 O. Blasco , I. García-Bayona

The aim of this paper is to investigate, which infinite dimensional consequences follow from the main results of recently published paper of the authors (2009) (see Theorems 2 and 3). We show that the finite dimensional Theorem 3 implies…

Probability · Mathematics 2012-03-27 Friedrich Götze , Andrei Yu. Zaitsev

We investigate the attractive Hubbard model in infinite spatial dimensions by combining dynamical mean-field theory with a strong-coupling continuous-time quantum Monte Carlo method. By calculating the superfluid order parameter and the…

Quantum Gases · Physics 2011-09-30 Akihisa Koga , Philipp Werner

In this short letter, I present a powerful application in dimensionality reduction of the lesser-used Jacobi-Davidson algorithm for the generalized eigenvalue decomposition. When combined with matrix-free implementations of relevant…

Quantum Physics · Physics 2025-01-08 Patrick Cook

Quantum Monte Carlo is used to investigate the possibility of d_{x^2-y^2} superconductivity in the two-dimensional repulsive Hubbard model. A small energy scale relevant to possible pairing requires a care (i.e., sufficiently small level…

Strongly Correlated Electrons · Physics 2009-10-30 Kazuhiko Kuroki , Hideo Aoki

Optimization techniques are at the core of many scientific and engineering disciplines. The steepest descent methods play a foundational role in this area. In this paper we studied a generalized steepest descent method on Riemannian…

Optimization and Control · Mathematics 2025-02-28 Rashid A. , Amal A Samad

Parameter--elliptic pseudodifferential operators given on a closed smooth manifold are investigated on the extended Sobolev scale. This scale consists of all Hilbert spaces that are interpolation spaces with respect to the Hilbert Sobolev…

Analysis of PDEs · Mathematics 2013-11-06 Aleksandr A. Murach , Tetiana Zinchenko

We express the number of anti-invariant subspaces for a linear operator on a finite vector space in terms of the number of its invariant subspaces. When the operator is diagonalizable with distinct eigenvalues, our formula gives a…

Combinatorics · Mathematics 2024-01-18 Amritanshu Prasad , Samrith Ram

We introduce a new method to jointly reduce the dimension of the input and output space of a function between high-dimensional spaces. Choosing a reduced input subspace influences which output subspace is relevant and vice versa.…

Machine Learning · Statistics 2025-04-01 Qiao Chen , Elise Arnaud , Ricardo Baptista , Olivier Zahm

The analytic aspects of the operator realization of Wick power series of infrared singular free fields are considered. Taking advantage of the holomorphy properties of the two-point correlation function and its Hilbert majorant in x-space,…

Mathematical Physics · Physics 2007-05-23 A. G. Smirnov , M. A. Soloviev

This article aims to explore the most recent developments in the study of the Hilbert matrix, acting as an operator on spaces of analytic functions and sequence spaces. We present the latest advances in this area, aiming to provide a…

Functional Analysis · Mathematics 2024-11-04 Carlo Bellavita , Vassilis Daskalogiannis , Georgios Stylogiannis

We develop a linear-algebraic framework for dimensional analysis in systems with constraints, particularly when variables are numerous or related by implicit relations so that direct elimination is impractical. By expressing both…

Mathematical Physics · Physics 2026-03-31 Umpei Miyamoto

Matrix valued inner functions on the bidisk have a number of natural subspaces of the Hardy space on the torus associated to them. We study their relationship to Agler decompositions, regularity up to the boundary, and restriction maps into…

Functional Analysis · Mathematics 2017-01-20 Kelly Bickel , Greg Knese

Kuelbs-Steadman spaces are introduced in this article on a separable metric space with finite diameter and finite positive Borel measure. Kuelbs-Steadman spaces of the Lipschitz type are also discussed. Various inclusion properties are also…

Functional Analysis · Mathematics 2024-05-20 Parthapratim Saha Bipan Hazarika , Hemanta Kalita

A subspace arrangement in a vector space is a finite collection of vector subspaces. Similarly, a configuration of linear spaces in a projective space is a finite collection of linear subspaces. In this paper we study the degree 2 part of…

Algebraic Geometry · Mathematics 2009-10-07 E. Carlini , M. V. Catalisano , A. V. Geramita

This work puts forth low-complexity Riemannian subspace descent algorithms for the minimization of functions over the symmetric positive definite (SPD) manifold. Different from the existing Riemannian gradient descent variants, the proposed…

Machine Learning · Statistics 2023-12-19 Yogesh Darmwal , Ketan Rajawat