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It was recently shown that the renormalization of quantum field theory is organized by the Hopf algebra of decorated rooted trees, whose coproduct identifies the divergences requiring subtraction and whose antipode achieves this. We…

High Energy Physics - Theory · Physics 2007-05-23 D. J. Broadhurst , D. Kreimer

Subspace recycling techniques have been used quite successfully for the acceleration of iterative methods for solving large-scale linear systems. These methods often work by augmenting a solution subspace generated iteratively by a known…

Numerical Analysis · Mathematics 2021-05-18 Ronny Ramlau , Kirk M. Soodhalter , Victoria Hutterer

We suggest a non-minimal renormalization scheme based on dimensional regularization that naturally incorporates threshold effects of heavy particles. By renormalizing couplings and masses to subtract all poles in $d \geq 4$, the resulting…

High Energy Physics - Theory · Physics 2026-04-28 Yannick Kluth

This work presents a geometric formulation for transforming nonconservative mechanical Hamiltonian systems and introduces a new method for regularizing and linearizing central force dynamics -- in particular, Kepler and Manev dynamics --…

Mathematical Physics · Physics 2025-07-15 Joseph T. A. Peterson , Manoranjan Majji , John L. Junkins

We extent the standard approach of dimensional regularization of Feynman diagrams: we replace the transition to lower dimensions by a 'natural' cut-off regulator. Introducing an external regulator of mass Lambda^(2e), we regain in the limit…

Nuclear Theory · Physics 2007-05-23 M. Dillig

On a compact manifold $M$, we consider the affine space $A$ of non self-adjoint perturbations of some invertible elliptic operator acting on sections of some Hermitian bundle, by some differential operator of lower order. We construct and…

Mathematical Physics · Physics 2022-03-23 Nguyen Viet Dang

L-infinity morphisms are studied from the point of view of perturbative quantum field theory, as generalizations of Feynman expansions. The connection with the Hopf algebra approach to renormalization is exploited. Using the coalgebra…

High Energy Physics - Theory · Physics 2007-05-23 Lucian M. Ionescu

We study the mathematical structure underlying the concept of locality which lies at the heart of classical and quantum field theory, and develop a machinery used to preserve locality during the renormalisation procedure. Viewing…

Mathematical Physics · Physics 2017-11-28 Pierre Clavier , Li Guo , Sylvie Paycha , Bin Zhang

Renormalization is a powerful technique in statistical physics to extract the large-scale behavior of interacting many-body models. These notes aim to give an introduction to perturbative methods that operate on the level of the stochastic…

Statistical Mechanics · Physics 2023-03-09 Nikos Papanikolaou , Thomas Speck

We consider the problem of nonlinear dimensionality reduction: given a training set of high-dimensional data whose ``intrinsic'' low dimension is assumed known, find a feature extraction map to low-dimensional space, a reconstruction map…

Information Theory · Computer Science 2007-07-13 Maxim Raginsky

Using the Green function integral representation the Dyson-Schwinger equations are solved directly in Minkowski space. Essential ideas of the spectral techniques are discussed and applied on two renormalizable models: the Yukawa theory with…

High Energy Physics - Phenomenology · Physics 2014-11-17 Vladimir Sauli

The long-standing problem of time in canonical quantum gravity is the source of several conceptual and technical issues. Here, recent mathematical results are used to provide a consistent algebraic formulation of dynamical symplectic…

General Relativity and Quantum Cosmology · Physics 2023-01-04 Martin Bojowald , Artur Tsobanjan

A model for quantum gravity in one (time) dimension is discussed, based on Regge's discrete formulation of gravity. The nature of exact continuous lattice diffeomorphisms and the implications for a regularized gravitational measure are…

High Energy Physics - Theory · Physics 2009-10-28 Herbert W. Hamber , Ruth M. Williams

This paper proposes a generalized framework with joint normalization which learns lower-dimensional subspaces with maximum discriminative power by making use of the Riemannian geometry. In particular, we model the similarity/dissimilarity…

Computer Vision and Pattern Recognition · Computer Science 2017-11-20 Tianci Liu , Zelin Shi , Yunpeng Liu

Dimension reduction techniques typically seek an embedding of a high-dimensional point cloud into a low-dimensional Euclidean space which optimally preserves the geometry of the input data. Based on expert knowledge, one may instead wish to…

Optimization and Control · Mathematics 2025-02-28 Ranthony A. Clark , Tom Needham , Thomas Weighill

We show within the Wilson renormalization group framework how the flow equation method can be used to prove the perturbative renormalizability of a relativistic massive selfinteracting scalar field. Furthermore we prove the regularity of…

High Energy Physics - Theory · Physics 2007-05-23 Georg Keller , Christoph Kopper , Clemens Schophaus

For some years there has been uncertainty over whether regularisation by dimensional reduction (DRED) is viable for non-supersymmetric theories. We resolve this issue by showing that DRED is entirely equivalent to standard dimensional…

High Energy Physics - Phenomenology · Physics 2009-10-28 I. Jack , D. R. T. Jones , K. L. Roberts

A practical approach is presented which allows the use of a non-invariant regularization scheme for the computation of quantum corrections in perturbative quantum field theory. The theoretical control of algebraic renormalization over…

High Energy Physics - Phenomenology · Physics 2014-11-17 P. A. Grassi , T. Hurth , M. Steinhauser

In this paper we are going to find a rooted tree representation from universal Hopf algebra of renormalization (in Connes-Marcolli's approach in the study of renormalizable Quantum Field Theories under the scheme minimal subtraction in…

Mathematical Physics · Physics 2009-09-18 Ali Shojaei-Fard

In this paper, we utilize stochastic optimization to reduce the space complexity of convex composite optimization with a nuclear norm regularizer, where the variable is a matrix of size $m \times n$. By constructing a low-rank estimate of…

Machine Learning · Computer Science 2015-12-08 Lijun Zhang , Tianbao Yang , Rong Jin , Zhi-Hua Zhou
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