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We establish the optimal nonergodic sublinear convergence rate of the proximal point algorithm for maximal monotone inclusion problems. First, the optimal bound is formulated by the performance estimation framework, resulting in an infinite…

Optimization and Control · Mathematics 2019-07-15 Guoyong Gu , Junfeng Yang

Let $M$ be an $n(>2)$-dimensional closed orientable submanifold in an $(n+p)$-dimensional space form $\mathbb{R}^{n+p}(c)$. We obtain an optimal upper bound for the second eigenvalue of a class of elliptic operators on $M$ defined by…

Differential Geometry · Mathematics 2018-06-29 Hang Chen , Xianfeng Wang

We study homotopy-coherent commutative multiplicative structures on equivariant spaces and spectra. We define N-infinity operads, equivariant generalizations of E-infinity operads. Algebras in equivariant spectra over an N-infinity operad…

Algebraic Topology · Mathematics 2015-07-01 Andrew J. Blumberg , Michael A. Hill

The paper presents a systematic study and implementation of a reconfigurable combinatorial multi-operand adder for use in Deep Learning systems. The size of carry changes with the number of operands and hence a reliable algorithm to…

Hardware Architecture · Computer Science 2020-08-10 Shilpa Mayannavar , Uday Wali

New ladder operators are constructed for a rational extension of the harmonic oscillator associated with type III Hermite exceptional orthogonal polynomials and characterized by an even integer $m$. The eigenstates of the Hamiltonian…

Mathematical Physics · Physics 2015-06-15 I. Marquette , C. Quesne

We consider a linear ill-posed equation in the Hilbert space setting under white noise. Known convergence results for the discrepancy principle are either restricted to Hilbert-Schmidt operators (and they require a self-similarity condition…

Numerical Analysis · Mathematics 2021-04-14 Tim Jahn

Hyperparameter transfer allows extrapolating optimal optimization hyperparameters from small to large scales, making it critical for training large language models (LLMs). This is done either by fitting a scaling law to the hyperparameters…

Machine Learning · Computer Science 2026-05-21 Dayal Singh Kalra , Maissam Barkeshli

We study the numerical integration problem for functions with infinitely many variables. The function spaces of integrands we consider are weighted reproducing kernel Hilbert spaces with norms related to the ANOVA decomposition of the…

Numerical Analysis · Mathematics 2021-09-21 Josef Dick , Michael Gnewuch

We propose a new random sketching approach for embedding high-dimensional Hilbert-Schmidt operators, using random input-output pairs. Such operator can then be approximated in a low-dimensional subspace of operators by solving a small…

Numerical Analysis · Mathematics 2026-04-15 Oleg Balabanov , Anthony Nouy , Alexandre Pasco

For $2a$-order strongly elliptic operators $P$ generalizing $(-\Delta )^a$, $0<a<1$, the treatment of the homogeneous Dirichlet problem on a bounded open set $\Omega \subset R^n$ by pseudodifferential methods, has been extended in a recent…

Analysis of PDEs · Mathematics 2022-12-23 Gerd Grubb

Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requirement limits its performance in practical problems. To…

Machine Learning · Computer Science 2024-04-24 Yikun Bai , Ivan Medri , Rocio Diaz Martin , Rana Muhammad Shahroz Khan , Soheil Kolouri

Several unitarily invariant norm inequalities and numerical radius inequalities for Hilbert space operators are studied. We investigate some necessary and sufficient conditions for the parallelism of two bounded operators. For a finite rank…

Functional Analysis · Mathematics 2024-04-03 Pintu Bhunia

A general classification of linear differential and finite-difference operators possessing a finite-dimensional invariant subspace with a polynomial basis is given. The main result is that any operator with the above property must have a…

High Energy Physics - Theory · Physics 2008-02-03 Alexander Turbiner

This paper proposes the method to optimize restriction and prolongation operators in the two-grid method. The proposed method is straightforwardly extended to the geometric multigrid method (GMM). GMM is used in solving discretized partial…

Numerical Analysis · Mathematics 2018-06-18 Alexandr Katrutsa , Talgat Daulbaev , Ivan Oseledets

The current standard for a variety of computer vision tasks using smaller numbers of labelled training examples is to fine-tune from weights pre-trained on a large image classification dataset such as ImageNet. The application of transfer…

Computer Vision and Pattern Recognition · Computer Science 2021-08-31 Jo Plested , Xuyang Shen , Tom Gedeon

The N = 2, 4 superconformal symmetry constraints in d = 4 for four point functions of chiral primary 1/2-BPS operators are derived. The operators are described by symmetric traceless tensors of the internal R-symmetry group. A substantial…

High Energy Physics - Theory · Physics 2017-06-06 Michael Nirschl

We construct and analyze approximation rates of deep operator networks (ONets) between infinite-dimensional spaces that emulate with an exponential rate of convergence the coefficient-to-solution map of elliptic second-order partial…

Numerical Analysis · Mathematics 2023-11-27 Carlo Marcati , Christoph Schwab

In many contexts one encounters Hermitian operators $M$ on a Hilbert space whose dimension is so large that it is impossible to write down all matrix entries in an orthonormal basis. How does one determine whether such $M$ is positive…

Algebraic Geometry · Mathematics 2020-04-17 Gemma de las Cuevas , Tobias Fritz , Tim Netzer

The criticality problem in nuclear engineering asks for the principal eigenpair of a Boltzmann operator describing neutron transport in a reactor core. Being able to reliably design, and control such reactors requires assessing these…

Numerical Analysis · Mathematics 2024-12-20 Wolfgang Dahmen , Olga Mula

This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an optimal synthesis operator, denoted as $B$, which regularizes…

Machine Learning · Statistics 2026-03-03 Giovanni S. Alberti , Ernesto De Vito , Tapio Helin , Matti Lassas , Luca Ratti , Matteo Santacesaria