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This paper deals with classical solutions of the modified chiral model on $R^{2+1}$. Such solutions are shown to correspond to products of various factor which we call time-dependent unitons. Then the problem of solving the system of…

High Energy Physics - Theory · Physics 2015-06-26 Theodora Ioannidou , Wojtek Zakrzewski

We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that…

Methodology · Statistics 2014-04-01 Alexandros Beskos

Many combinatorial problems can be formulated as a polynomial optimization problem that can be solved by state-of-the-art methods in real algebraic geometry. In this paper we explain many important methods from real algebraic geometry, we…

Combinatorics · Mathematics 2014-11-11 Erik Sjöland

We discuss a numerical scheme to solve the continuum Kardar-Parisi-Zhang equation in generic spatial dimensions. It is based on a momentum-space discretization of the continuum equation and on a pseudo-spectral approximation of the…

Statistical Mechanics · Physics 2009-11-07 Lorenzo Giada , Achille Giacometti , Maurice Rossi

In this paper, we propose two iterative methods for finding a common solution of a finite family of equilibrium problems for pseudomonotone bifunctions. The first is a parallel hybrid extragradient-cutting algorithm which is extended from…

Optimization and Control · Mathematics 2015-10-28 Dang Van Hieu

Science about optimization methods is rapidly developing today. In machine learning, computer vision, biology, medicine, construction and in many other different areas optimization methods have vast popularity and they appear as important…

Optimization and Control · Mathematics 2017-05-30 Alexander Tyurin

Manifold learning techniques have become increasingly valuable as data continues to grow in size. By discovering a lower-dimensional representation (embedding) of the structure of a dataset, manifold learning algorithms can substantially…

Neural and Evolutionary Computing · Computer Science 2020-01-31 Andrew Lensen , Mengjie Zhang , Bing Xue

There is growing interest in developing mathematical models and appropriate numerical methods for problems involving networks formed by, essentially, one-dimensional (1D) domains joined by junctions. Examples include hyperbolic equations in…

Numerical Analysis · Mathematics 2017-08-08 Francesca Bellamoli , Lucas Omar Müller , Eleuterio Francisco Toro

A full multigrid finite element method is proposed for semilinear elliptic equations. The main idea is to transform the solution of the semilinear problem into a series of solutions of the corresponding linear boundary value problems on the…

Numerical Analysis · Mathematics 2017-03-29 Hehu Xie , Fei Xu

Employing two distinct types of regularization terms, we propose two regularized extragradient methods for solving equilibrium problems on Hadamard manifolds. The sequences generated by these extragradient algorithms converge to a solution…

Optimization and Control · Mathematics 2026-01-06 Shikher Sharmaa , Pankaj Gautam , Simeon Reich

In the article we discuss the notion of the generalized invariant manifold introduced in our previous study. In the literature the method of the differential constraints is well known as a tool for constructing particular solutions for the…

Exactly Solvable and Integrable Systems · Physics 2021-07-08 I. T. Habibullin , A. R. Khakimova , A. O. Smirnov

The notion of singular reduction modules, i.e., of singular modules of nonclassical (conditional) symmetry, of differential equations is introduced. It is shown that the derivation of nonclassical symmetries for differential equations can…

Mathematical Physics · Physics 2017-12-05 Vaycheslav M. Boyko , Michael Kunzinger , Roman O. Popovych

We propose and show the efficacy of a new method to address generic inverse problems. Inverse modeling is the task whereby one seeks to determine the control parameters of a natural system that produce a given set of observed measurements.…

Machine Learning · Computer Science 2023-08-15 Gregory P. Spell , Simiao Ren , Leslie M. Collins , Jordan M. Malof

Linear dimensionality reduction methods are a cornerstone of analyzing high dimensional data, due to their simple geometric interpretations and typically attractive computational properties. These methods capture many data features of…

Machine Learning · Statistics 2016-03-22 John P. Cunningham , Zoubin Ghahramani

An iterative formula based on Newton Method alone is presented for the iterative solutions of equations that ensures convergence in cases where the traditional Newton Method may fail to converge to the desired root. In addition, the method…

Numerical Analysis · Mathematics 2012-10-30 Ababu Teklemariam Tiruneh

There are many numerical methods for solving partial different equations (PDEs) on manifolds such as classical implicit, finite difference, finite element, and isogeometric analysis methods which aim at improving the interoperability…

Numerical Analysis · Mathematics 2023-11-17 Wenrui Hao , Jonathan D. Hauenstein , Margaret H. Regan , Tingting Tang

We describe a method for reconstructing multi-scale entangled states from a small number of efficiently-implementable measurements and fast post-processing. The method only requires single particle measurements and the total number of…

Quantum Physics · Physics 2014-12-02 Olivier Landon-Cardinal , David Poulin

The isomonodromy deformation method is applied to the scaling limits in the linear NxN matrix equations with rational coefficients to obtain the deformation equations for the algebraic curves which describe the local behavior of the reduced…

Exactly Solvable and Integrable Systems · Physics 2009-11-07 Andrei A. Kapaev

The techniques and analysis presented in this paper provide new methods to solve optimization problems posed on Riemannian manifolds. A new point of view is offered for the solution of constrained optimization problems. Some classical…

Optimization and Control · Mathematics 2018-04-12 Steven Thomas Smith

Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice. However, this requires that the data manifold admits a global parameterization. In order to represent…

Machine Learning · Computer Science 2024-08-13 Giovanni S. Alberti , Johannes Hertrich , Matteo Santacesaria , Silvia Sciutto