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In this paper, we present a dimension reduction method to reduce the dimension of parameter space and state space and efficiently solve inverse problems. To this end, proper orthogonal decomposition (POD) and radial basis function (RBF) are…

Numerical Analysis · Mathematics 2016-10-18 Fuchen Chen , Lijian Jiang , Guanghui Zheng

Solving inverse problems requires appropriate regularization techniques to ensure well-posedness and stability. In recent years, denoiser-driven methods have emerged as effective regularization strategies, achieving state-of-the-art…

Numerical Analysis · Mathematics 2026-04-23 Harshit Bajpai , Ankik Kumar Giri , Tim Jahn , Abhinav Jha

This paper focuses on regularisation methods using models up to the third order to search for up to second-order critical points of a finite-sum minimisation problem. The variant presented belongs to the framework of [3]: it employs random…

Numerical Analysis · Mathematics 2021-04-05 Stefania Bellavia , Gianmarco Gurioli , Benedetta Morini , Philippe L. Toint

Dimensional regularization is applied to the Lippmann-Schwinger equation for a separable potential which gives rise to logarithmic singularities in the Born series. For this potential a subtraction at a fixed energy can be used to…

Nuclear Theory · Physics 2009-04-17 D. R. Phillips , I. R. Afnan , A. G. Henry-Edwards

A method is introduced to calculate the UV-divergent parts at one-loop level in dimensional regularization. The method is based on the recursion, and the basic integrals are just the scaleless integrals after the recursive reduction, which…

High Energy Physics - Phenomenology · Physics 2012-02-21 Feng Feng

In the naive form of most resummations we get into conflict with order-by-order renormalization. We present a method that is capable to ensure UV consistency of any resummations satisfying certain conditions. The method is based on the…

High Energy Physics - Phenomenology · Physics 2007-05-23 A. Jakovac

Recent developments in higher order calculations within the framework of Dimensional Reduction, the preferred regularization scheme for supersymmetric theories, are reported on. Special emphasis is put on the treatment of evanescent…

High Energy Physics - Phenomenology · Physics 2007-06-21 Robert Harlander , Philipp Kant , Luminita Mihaila , Matthias Steinhauser

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

The global R* operation is a powerful method for computing renormalisation group functions. This technique, based on the principle of infrared rearrangement, allows to express all the ultraviolet counterterms in terms of massless propagator…

High Energy Physics - Phenomenology · Physics 2018-01-10 K. G. Chetyrkin , G. Falcioni , F. Herzog , J. A. M. Vermaseren

Recent advances in the theoretical description of radiative Upsilon decays are reviewed, including the calculation of next-to-leading order QCD corrections to the photon spectrum.

High Energy Physics - Phenomenology · Physics 2007-05-23 Michael Krämer

We use convex relaxation techniques to provide a sequence of solutions to the matrix completion problem. Using the nuclear norm as a regularizer, we provide simple and very efficient algorithms for minimizing the reconstruction error…

Machine Learning · Statistics 2009-06-12 Rahul Mazumder , Trevor Hastie , Rob Tibshirani

Image deep features extracted by pre-trained networks are known to contain rich and informative representations. In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Juncheng Wu , Zhangkai Ni , Hanli Wang , Wenhan Yang , Yuyin Zhou , Shiqi Wang

Natural images tend to mostly consist of smooth regions with individual pixels having highly correlated spectra. This information can be exploited to recover hyperspectral images of natural scenes from their incomplete and noisy…

Computer Vision and Pattern Recognition · Computer Science 2016-11-03 Reza Arablouei , Frank de Hoog

A tentative proposal is demonstrated that there is a natural strategy to get rid of unphysical (UV) infinities in QFTs if one adopts the modern standard point of view that a fundamental theory that is complete and well-defined in all…

High Energy Physics - Theory · Physics 2007-05-23 Jifeng Yang

The problem of reconstructing a two-dimensional (2D) current distribution in a superconductor from a 2D magnetic field measurement is recognized as a first-kind integral equation and resolved using the method of Regularization.…

Superconductivity · Physics 2009-11-10 D. M. Feldmann

This paper focuses on the regularization of backward time-fractional diffusion problem on unbounded domain. This problem is well-known to be ill-posed, whence the need of a regularization method in order to recover stable approximate…

Numerical Analysis · Mathematics 2022-01-03 Walter Simo Tao Lee

The main goal of this paper is to propose a new quaternion total variation regularization model for solving linear ill-posed quaternion inverse problems, which arise from three-dimensional signal filtering or color image processing. The…

Numerical Analysis · Mathematics 2024-08-07 Xuan Liu , Zhigang Jia , Xiaoqing Jin

Loop-tree duality (LTD) allows to express virtual contributions in terms of phase-space integrals, thus leading to a direct mapping with real radiation terms. We review the basis of the method and describe its application to regularize…

High Energy Physics - Phenomenology · Physics 2015-10-19 German F. R. Sborlini , Roger Hernandez-Pinto , German Rodrigo

In this paper, we study unirational differential curves and the corresponding differential rational parametrizations. We first investigate basic properties of proper differential rational parametrizations for unirational differential…

Algebraic Geometry · Mathematics 2020-01-27 Lei Fu , Wei Li

Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant…

Machine Learning · Computer Science 2022-05-02 Avraam Bardos , Ioannis Mollas , Nick Bassiliades , Grigorios Tsoumakas