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Approximation techniques have been historically important for solving differential equations, both as initial value problems and boundary value problems. The integration of numerical, analytic and perturbation methods and techniques can…

Classical Analysis and ODEs · Mathematics 2025-02-25 J. Nathan Kutz

Renormalization group method is one of the most powerful tool to obtain approximate solutions to differential equations. We apply the renormalization group method to Hamiltonian systems whose integrable parts linearly depend on action…

chao-dyn · Physics 2007-05-23 Yoshiyuki Y. Yamaguchi , Yasusada Nambu

We apply a recently proposed approximation method to the evaluation of non-Gaussian integral and anharmonic oscillator. The method makes use of the truncated perturbation series by recasting it via the modified Laplace integral…

Mathematical Physics · Physics 2009-10-30 Naoki Mizutani , Hirofumi Yamada

In this paper, we introduce the new optimal perturbation iteration method based on the perturbation iteration algorithms for the approximate solutions of nonlinear differential equations of many types. The proposed method is illustrated by…

Numerical Analysis · Mathematics 2016-09-21 Sinan Deniz , Necdet Bildik

A method for sequential inference of the fixed parameters of a dynamic latent Gaussian models is proposed and evaluated that is based on the iterated Laplace approximation. The method provides a useful trade-off between computational…

Methodology · Statistics 2015-09-29 Tiep Mai , Simon Wilson

We present an alternative pathway in the application of the variation improvement of ordinary perturbation theory exposed in [1] which can preserve the internal symmetries of a model by means of a time compactification.

High Energy Physics - Theory · Physics 2009-10-28 B. Bellet , P. Garcia , A. Neveu

Scalable Gaussian process (GP) inference is essential for sequential decision-making tasks, yet improving GP scalability remains a challenging problem with many open avenues of research. This paper focuses on iterative GPs, where iterative…

Machine Learning · Computer Science 2025-11-21 Alan Yufei Dong , Jihao Andreas Lin , José Miguel Hernández-Lobato

Approximate resolution of linear systems of differential equations with varying coefficients is a recurrent problem shared by a number of scientific and engineering areas, ranging from Quantum Mechanics to Control Theory. When formulated in…

Mathematical Physics · Physics 2009-04-11 S. Blanes , F. Casas , J. A. Oteo , J. Ros

A method is suggested for treating those complicated physical problems for which exact solutions are not known but a few approximation terms of a calculational algorithm can be derived. The method permits one to answer the following rather…

High Energy Physics - Phenomenology · Physics 2009-10-31 V. I. Yukalov , E. P. Yukalova

We consider an incremental approximation method for solving variational problems in infinite-dimensional Hilbert spaces, where in each step a randomly and independently selected subproblem from an infinite collection of subproblems is…

Numerical Analysis · Mathematics 2018-03-06 Michael Griebel , Peter Oswald

A non-perturbative method which can go beyond the weak coupling perturbation theory is introduced. Essential idea is to formulate a set of exact differential equations as a function of the coupling strength $g$. Unlike other resummation in…

Quantum Physics · Physics 2011-03-21 Tomoya Hayata

Variational inference has become one of the most widely used methods in latent variable modeling. In its basic form, variational inference employs a fully factorized variational distribution and minimizes its KL divergence to the posterior.…

Machine Learning · Statistics 2020-01-29 Robert Bamler , Cheng Zhang , Manfred Opper , Stephan Mandt

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

Inference methods are often formulated as variational approximations: these approximations allow easy evaluation of statistics by marginalization or linear response, but these estimates can be inconsistent. We show that by introducing…

Machine Learning · Statistics 2017-04-27 Jack Raymond , Federico Ricci-Tersenghi

An efficient proximal-gradient-based method, called proximal extrapolated gradient method, is designed for solving monotone variational inequality in Hilbert space. The proposed method extends the acceptable range of parameters to obtain…

Optimization and Control · Mathematics 2019-12-05 Xiaokai Chang , Sanyang Liu , Jianchao Bai , Jun Yang

This paper deals with symbolic time series representation. It builds up on the popular mapping technique Symbolic Aggregate approXimation algorithm (SAX), which is extensively utilized in sequence classification, pattern mining, anomaly…

Machine Learning · Computer Science 2022-05-27 Matej Kloska , Viera Rozinajova

A recently developed linear algebraic method for the computation of perturbation expansion coefficients to large order is applied to the problem of a hydrogenic atom in a magnetic field. We take as the zeroth order approximation the $D…

chem-ph · Physics 2009-10-22 Timothy C. Germann , Dudley R. Herschbach , Bruce M. Boghosian

An outline is given of an extended perturbative solution of Euclidean QCD which systematically accounts for a class of nonperturbative effects, while allowing renormalization by the perturbative counterterms. Proper vertices Gamma are…

High Energy Physics - Theory · Physics 2014-11-18 M. Stingl

Recently it has been pointed out that diagonal Pad\'e approximants to truncated perturbative series in gauge theories have the remarkable property of being independent of the choice of the renormalization scale as long as the gauge coupling…

High Energy Physics - Phenomenology · Physics 2009-10-30 G. Cvetic

We study an ensemble of random matrices (the Rosenzweig-Porter model) which, in contrast to the standard Gaussian ensemble, is not invariant under changes of basis. We show that a rather complete understanding of its level correlations can…

Mesoscale and Nanoscale Physics · Physics 2009-10-30 Alexander Altland , Martin Janssen , Boris Shapiro