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Soft extrapolation refers to the problem of recovering a function from its samples, multiplied by a fast-decaying window and perturbed by an additive noise, over an interval which is potentially larger than the essential support of the…

Numerical Analysis · Mathematics 2018-12-26 Dmitry Batenkov , Laurent Demanet , Hrushikesh N. Mhaskar

A method is presented which allows for the renormalization of the self-potential for a scalar point charge at rest in static curved space-time. The method is suitable for the scalar field with arbitrary mass $m$ and coupling to the scalar…

General Relativity and Quantum Cosmology · Physics 2011-11-16 Arkady A. Popov

Scanning tunneling microscope (STM) has presented a revolutionary methodology to the nanoscience and nanotechnology. It enables imaging the topography of surfaces, mapping the distribution of electronic density of states, and manipulating…

Mesoscale and Nanoscale Physics · Physics 2020-09-07 Wonhee Ko , Chuanxu Ma , Giang D. Nguyen , Marek Kolmer , An-Ping Li

To avoid instabilities in the continuum semi-classical limit of loop quantum cosmology models, refinement of the underlying lattice is necessary. The lattice refinement leads to new dynamical difference equations which, in general, do not…

General Relativity and Quantum Cosmology · Physics 2008-12-18 William Nelson , Mairi Sakellariadou

Most data processing techniques, applied to biomedical and sociological time series, are only valid for random fluctuations that are stationary in time. Unfortunately, these data are often non stationary and the use of techniques of…

Data Analysis, Statistics and Probability · Physics 2009-11-10 M. Ignaccolo , P. Allegrini , P. Grigolini , P. Hamilton , B. J. West

The field theoretic renormalization group (RG) and the operator product expansion (OPE) are applied to the model of a density field advected by a random turbulent velocity field. The latter is governed by the stochastic Navier-Stokes…

Statistical Mechanics · Physics 2017-03-27 N. V. Antonov , N. M. Gulitskiy , M. M. Kostenko , T. Lučivjanský

We recalculate four-loop renormalization group functions in 2-dimensional nonlinear O(n) {\sigma}-model using coordinate-space method. The high accuracy of calculation allow us to find the analytical form of {\beta}- and {\gamma}-function…

High Energy Physics - Phenomenology · Physics 2013-06-13 O. Veretin

We introduce an efficient lattice regularization scheme for quantum Monte Carlo calculations of realistic electronic systems. The kinetic term is discretized by a finite difference Laplacian with two mesh sizes, a and a', where a'/a is an…

Other Condensed Matter · Physics 2009-11-11 Michele Casula , Claudia Filippi , Sandro Sorella

A brief review is presented of the scaling of complex fluids, polymers and polyelectrolytes in solution and in confined geometry, in thermodynamical, structural and rheology properties using equilibrium and nonequilibrium dissipative…

Soft Condensed Matter · Physics 2016-12-06 Armando Gama Goicochea

We use the BLM procedure to eliminate the renormalization scale ambiguity in the evolution equation for the non-singlet deep-inelastic structure function $F_2^{\text NS}(x,Q).$ The scale of the QCD coupling in the $\overline{\text{MS}}$…

High Energy Physics - Phenomenology · Physics 2009-10-28 Wing Kai Wong

We present a simple scheme for restarting first-order methods for convex optimization problems. Restarts are made based only on achieving specified decreases in objective values, the specified amounts being the same for all optimization…

Optimization and Control · Mathematics 2020-10-22 James Renegar , Benjamin Grimmer

A careful analysis of differential renormalization shows that a distinguished choice of renormalization constants allows for a mathematically more fundamental interpretation of the scheme. With this set of a priori fixed integration…

High Energy Physics - Theory · Physics 2009-10-30 Oliver Schnetz

The renormalization group has proven to be a very powerful tool in physics for treating systems with many length scales. Here we show how it can be adapted to provide a new class of algorithms for discrete optimization. The heart of our…

Disordered Systems and Neural Networks · Physics 2009-10-31 J. Houdayer , O. C. Martin

The effect of different move sets on the folding kinetics of the Monte Carlo simulations is analysed based on the conformation-network and the temperature-dependent folding kinetics. A new scheme of implementing Metropolis algorithm is…

Soft Condensed Matter · Physics 2007-05-23 Yu-Pin Luo , Ming-Chang Huang , Yen-Liang Chou , Tsong-Ming Liaw

The one-loop effective action for a scalar field defined in the ultrastatic space-time where non standard logarithmic terms in the asymptotic heat-kernel expansion are present, is investigated by a generalisation of zeta-function…

High Energy Physics - Theory · Physics 2016-11-09 Guido Cognola , Sergio Zerbini

We introduce a new restarting scheme for a continuous inertial dynamics with Hessian driven-damping, and establish a linear convergence rate for the function values along the restarted trajectories. The proposed routine is implemented…

Optimization and Control · Mathematics 2026-04-13 Juan José Maulén , Huiyuan Guo , Juan Peypouquet

Methods for the reduction of the complexity of computational problems are presented, as well as their connections to renormalization, scaling, and irreversible statistical mechanics. Several statistically stationary cases are analyzed; for…

Numerical Analysis · Mathematics 2007-05-23 Alexandre J. Chorin , Panagiotis Stinis

A system of stochastic differential equations for the velocity and density of a classical self-gravitating matter is investigated by means of the field theoretic renormalization group. The existence of two types of large-scale scaling…

Astrophysics · Physics 2009-11-10 N. V. Antonov

Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available…

Machine Learning · Statistics 2022-08-02 Biplab Biswas , Nishith Kumar , Md Aminul Hoque , Md Ashad Alam

We employ the machinery of smooth scaling and coarse-graining of observables, developed recently by us in the context of so-called fluctuation operators (inspired by prior work of Verbeure et al) to make a rigorous renormalisation group…

Mathematical Physics · Physics 2007-05-23 Manfred Requardt