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Perturbation theory can be reformulated as dynamical theory. Then a sequence of perturbative approximations is bijective to a trajectory of dynamical system with discrete time, called the approximation cascade. Here we concentrate our…

Statistical Mechanics · Physics 2015-06-25 V. I. Yukalov , E. P. Yukalova

Quantum measurements are inherently probabilistic and quantum theory often forbids to precisely predict the outcomes of simultaneous measurements. This phenomenon is captured and quantified through uncertainty relations. Although studied…

Quantum Physics · Physics 2023-10-30 Carlos de Gois , Kiara Hansenne , Otfried Gühne

Smooth parametrization consists in a subdivision of the mathematical objects under consideration into simple pieces, and then parametric representation of each piece, while keeping control of high order derivatives. The main goal of the…

Computational Geometry · Computer Science 2014-07-14 Y. Yomdin

The degree of a CSP instance is the maximum number of times that any variable appears in the scopes of constraints. We consider the approximate counting problem for Boolean CSP with bounded-degree instances, for constraint languages…

Computational Complexity · Computer Science 2011-09-19 Martin Dyer , Leslie Ann Goldberg , Markus Jalsenius , David Richerby

String theory and supersymmetry are theoretical ideas that go beyond the standard model of particle physics and show promise for unifying all forces. After a brief introduction to supersymmetry, we discuss the prospects for its experimental…

High Energy Physics - Theory · Physics 2008-11-26 John H. Schwarz , Nathan Seiberg

On a weakly Blackwell space we show how to define a Markov chain approximating problem, for the target problem. The approximating problem is proved to converge to the optimal reduced problem under different pseudometrics. A computational…

Probability · Mathematics 2009-01-14 Giacomo Aletti , Diane Saada

Variational analysis provides the theoretical foundations and practical tools for constructing optimization algorithms without being restricted to smooth or convex problems. We survey the central concepts in the context of a concrete but…

Optimization and Control · Mathematics 2025-04-08 Johannes O. Royset

The dynamics of physical theories is usually described by differential equations. Difference equations then appear mainly as an approximation which can be used for a numerical analysis. As such, they have to fulfill certain conditions to…

General Relativity and Quantum Cosmology · Physics 2009-11-10 Martin Bojowald , Ghanashyam Date

In some estimation problems, especially in applications dealing with information theory, signal processing and biology, theory provides us with additional information allowing us to restrict the parameter space to a finite number of points.…

Methodology · Statistics 2012-07-25 Christine Choirat , Raffaello Seri

The speed of light is usually taken as one of the fundamental constants. String, and field, theories appear to require the altercation of this constant into a functional form $E(m,c)$ which is not $E=mc^2$. The analysis requires the…

General Physics · Physics 2007-05-23 Gordon Chalmers

The `strong-coupling' perturbation theory over the inverse interaction constant $1/g$ near the nontrivial solution of Lagrange equation is formulated. The ordinary `week-coupling' perturbation theory over $g$ is described also to compare…

High Energy Physics - Theory · Physics 2007-05-23 J. Manjavidze , A. Sissakian

Approximate computing is a research area where we investigate a wide spectrum of techniques to trade off computation accuracy for better performance or energy consumption. In this work, we provide a general introduction to approximate…

Programming Languages · Computer Science 2017-12-12 M. Ammar Ben Khadra

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We firstly define a purely structural…

Disordered Systems and Neural Networks · Physics 2013-05-30 James West , Lucas Lacasa , Simone Severini , Andrew Teschendorff

The main ideas behind nuclear supersymmetry are presented, starting from the basic concepts of symmetry and the methods of group theory in physics. We propose new, more stringent experimental tests that probe the supersymmetry…

Nuclear Theory · Physics 2017-11-02 A. Frank , J. Barea , R. Bijker

The two-dimensional case occupies a special position in the theory of critical phenomena due to the exact results provided by lattice solutions and, directly in the continuum, by the infinite-dimensional character of the conformal algebra.…

Statistical Mechanics · Physics 2021-04-01 Gesualdo Delfino

Stochastic dynamical systems consisting of non-invertible continuous maps on an interval are studied. It is proved that if they satisfy the recently introduced so-called $\mu$-injectivity and some mild assumptions, then proximality,…

Dynamical Systems · Mathematics 2025-12-11 Sander C. Hille , Katarzyna Horbacz , Hanna Oppelmayer , Tomasz Szarek

Following (Shevtsova, 2010) we introduce detailed classification of the asymptotically exact constants in natural estimates of the rate of convergence in the Lindeberg central limit theorem, namely in Esseen's, Rozovskii's, and Wang-Ahmad's…

Probability · Mathematics 2021-01-13 Ruslan Gabdullin , Vladimir Makarenko , Irina Shevtsova

In this paper, we present a comprehensive system for the treatment of the topic of limits--conceptually, computationally, and formally. The system addresses fundamental linguistic flaws in the standard presentation of limits, which attempts…

General Mathematics · Mathematics 2007-05-23 Frank Swenton

Building a consistent Quantum Theory of Gravity is one of the most challenging aspects of modern theoretical physics. In the past couple of years, new attempts have been made along the path of ``asymptotic safety'' through the use of Exact…

High Energy Physics - Theory · Physics 2009-11-10 Daniele Perini

A central problem in machine learning is often formulated as follows: Given a dataset $\{(x_j, y_j)\}_{j=1}^M$, which is a sample drawn from an unknown probability distribution, the goal is to construct a functional model $f$ such that…

Machine Learning · Computer Science 2026-03-05 Hrushikesh N. Mhaskar , Efstratios Tsoukanis , Ameya D. Jagtap
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