Solving a set of truncated Dyson-Schwinger equations with a globally converging method

A globally converging numerical method to solve coupled sets of non-linear integral equations is presented. Such systems occur, e.g., in the study of Dyson-Schwinger equations of Yang-Mills theory and QCD. The method is based on the knowledge of the qualitative properties of the solution functions in the far infrared and ultraviolet. Using this input, the full solutions are constructed using a globally convergent modified Newton iteration. Two different systems will be treated as examples: The Dyson-Schwinger equations of 3-dimensional Yang-Mills-Higgs theory provide a system of finite integrals, while those of 4-dimensional Yang-Mills theory at high temperatures are only finite after renormalization.     

An iterative algorithm for least squares problem in quaternionic quantum theory

Quaternionic least squares (QLS) problem is one method of solving overdetermined sets of quaternion linear equations AXB=E that is appropriate when there is error in the matrix E. In this paper, by means of real representation of a quaternion matrix, we introduce a concept of norm of quaternion matrices, which is different from that in [T. Jiang, L. Chen, Algebraic algorithms for least squares problem in quaternionic quantum theory, Comput. Phys. Comm. 176 (2007) 481-485; T. Jiang, M. Wei, Equality constrained least squares problem over quaternion field, Appl. Math. Lett. 16 (2003) 883-888], and derive an iterative method for finding the minimum-norm solution of the QLS problem in quaternionic quantum theory.     

Algebraic algorithms for least squares problem in quaternionic quantum theory

Quaternionic least squares (QLS) problem is one method of solving overdetermined sets of quaternion linear equations AX≈B that is appropriate when there is error in the matrix B. In this paper, by means of complex representation of a quaternion matrix, we introduce a concept of norm of quaternion matrices, discuss singular values and generalized inverses of a quaternion matrix, study the QLS problem and derive two algebraic methods for finding solutions of the QLS problem in quaternionic quantum theory.     

Successive derivatives of Whittaker functions with respect to the first parameter

Algorithms are given for the computation of the nth derivatives of the Whittaker functions M_κ,μ(z) and W_κ,μ(z) with respect to the parameter κ. The algorithms are based on a convergent expansion, due to Buchholz, of M_κ,μ(z) in series of Bessel functions. Properties of the Buchholz polynomials and algorithms for evaluating the n-derivative of the reciprocal Gamma function are discussed in two appendices.     

PROCRUSTES: A computer algebra package for post-Newtonian calculations in General Relativity

We report on a package of routines for the computer algebra system Maple which supports the explicit determination of the geometric quantities, field equations, equations of motion, and conserved quantities of General Relativity in the post-Newtonian approximation. The package structure is modular and allows for an easy modification by the user. The set of routines can be used to verify hand calculations or to generate the input for further numerical investigations.

Program summary

Title of the program:ProcrustesCatalogue identifier:ADYH_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/ADYH_v1_0Program obtainable from:CPC Program Library, Queen's University of Belfast, N. IrelandComputers:Platforms supported by the Maple computer algebra system (program was written under Maple 8, but also tested with Maple 9, 9.5, 10)Operating systems under which the program has been tested:Linux, Unix, Windows XPProgramming language used:Maple internal languageMemory required to execute typical problem:Dependent on problem (small ∼ couple of MBytes, large ∼ several GBytes)Classification:1.5 Relativity and Gravitation, 5 Computer AlgebraNo. bits in a word:Dependent on Maple distribution (supports 32 bit and 64 bit platforms)No. of processors used:1No. of lines in distributed program, including test data, etc.: 10 881No. of bytes in distributed program, including test data, etc.:47 743Distribution format:tar.gzNature of the physical problem:The post-Newtonian approximation represents an approximative scheme frequently used in General Relativity in which the gravitational potential is expanded into a series in inverse powers of the speed of light. Depending on the desired approximation level the field equations and equations of motion have to be determined up to given orders in the speed of light. This usually requires large algebraic computations due to the geometrical quantities entering the field equations and equations of motion.Method of solution:Automated computation using computer algebra techniques. Program has modular structure and only makes use of basic features of Maple to guarantee maximum compatibility and to allow for rapid extensions/modifications by the user.Typical running time:Dependent on problem (small ∼ couple of minutes, large ∼ couple of hours).Restrictions on the complexity of the problem:Sufficient amount of memory is the limiting factor for these calculations. The structure of the program allows one to handle large scale problems in an iterative manner to minimize the amount of memory required.     

An efficient analytical approach for solving fourth order boundary value problems

Based on the homotopy analysis method (HAM), an efficient approach is proposed for obtaining approximate series solutions to fourth order two-point boundary value problems. We apply the approach to a linear problem which involves a parameter c and cannot be solved by other analytical methods for large values of c, and obtain convergent series solutions which agree very well with the exact solution, no matter how large the value of c is. Consequently, we give an affirmative answer to the open problem proposed by Momani and Noor in 2007 [S. Momani, M.A. Noor, Numerical comparison of methods for solving a special fourth-order boundary value problem, Appl. Math. Comput. 191 (2007) 218-224]. We also apply the approach to a nonlinear problem, and obtain convergent series solutions which agree very well with the numerical solution given by the Runge-Kutta-Fehlberg 4-5 technique.     

Numerical realizations of solutions of the stochastic KdV equation

We investigate simulations of exact solutions of the stochastic Korteweg–deVries equation under additive noise. We compare the expectation values of the exact solutions to theoretical expectation values and to the numerical simulations of the stochastic Korteweg–deVries equation with and without damping. We find on average the diffused soliton vanishes long before the typically reported asymptotic limit.     

Quasilinearization approach to computations with singular potentials

We pioneered the application of the quasilinearization method (QLM) to the numerical solution of the Schrödinger equation with singular potentials. The spiked harmonic oscillator r²+λr−α is chosen as the simplest example of such potential. The QLM has been suggested recently for solving the Schrödinger equation after conversion into the nonlinear Riccati form. In the quasilinearization approach the nonlinear differential equation is treated by approximating the nonlinear terms by a sequence of linear expressions. The QLM is iterative but not perturbative and gives stable solutions to nonlinear problems without depending on the existence of a smallness parameter. The choice of zero iteration is based on general features of solutions near the boundaries.We show that the energies of bound state levels in the spiked harmonic oscillator potential which are notoriously difficult to compute for small couplings λ, are easily calculated with the help of QLM for any λ and α with accuracy of twenty significant figures.     

TaylUR, an arbitrary-order diagonal automatic differentiation package for Fortran 95

We present TaylUR, a Fortran 95 module to automatically compute the numerical values of a complex-valued function's derivatives with respect to several variables up to an arbitrary order in each variable, but excluding mixed derivatives. Arithmetic operators and Fortran intrinsics are overloaded to act correctly on objects of a defined type taylor, which encodes a function along with its first few derivatives with respect to the user-defined independent variables. Derivatives of products and composite functions are computed using Leibniz's rule and Faà di Bruno's formula. TaylUR makes heavy use of operator overloading and other Fortran 95 features such as elemental functions.

Program summary

Program title: TaylURCatalogue identifier:ADXR_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXR_v1_0Program obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandLicensing provisions:noneProgramming language:Fortran 95Computer:Any computer with a conforming Fortran 95 compilerOperating system:Any system with a conforming Fortran 95 compilerNo. of lines in distributed program, including test data, etc.:6286No. of bytes in distributed program, including test data, etc:14 994Distribution format:tar.gzNature of problem:Problems that require potentially high orders of derivatives with respect to some variables, such as e.g. expansions of Feynman diagrams in particle masses in perturbative Quantum Field Theory, and which cannot be treated using existing Fortran modules for automatic differentiation [C.W. Straka, ADF95: Tool for automatic differentiation of a FORTRAN code designed for large numbers of independent variables, Comput. Phys. Comm. 168 (2005) 123-139, arXiv:cs.MS/0503014; S. Stamatiadis, R. Prosmiti, S.C. Farantos, auto_deriv: Tool for automatic differentiation of a FORTRAN code, Comput. Phys. Comm. 127 (2000) 343-355].Solution method:Arithmetic operators and Fortran intrinsics are overloaded to act correctly on objects of a defined type taylor, which encodes a function along with its first few derivatives with respect to the user-defined independent variables. Derivatives of products and composite functions are computed using Leibniz's rule and Faà di Bruno's formula.Restrictions:Memory and CPU time constraints may restrict the number of variables and Taylor expansion order that can be achieved. Loss of numerical accuracy due to cancellation may become an issue at very high orders.Unusual features:No mixed higher-order derivatives are computed. The complex conjugation operation assumes all independent variables to be real.Running time:The running time of TaylUR operations depends linearly on the number of variables. Its dependence on the Taylor expansion order varies from linear (for linear operations) through quadratic (for multiplication) to exponential (for elementary function calls).     

Precise numerical evaluation of the two loop sunrise graph Master Integrals in the equal mass case

We present a double precision routine in Fortran for the precise and fast numerical evaluation of the two Master Integrals (MIs) of the equal mass two-loop sunrise graph for arbitrary momentum transfer in d=2 and d=4 dimensions. The routine implements the accelerated power series expansions obtained by solving the corresponding differential equations for the MIs at their singular points. With a maximum of 22 terms for the worst case expansion a relative precision of better than a part in 10¹⁵ is achieved for arbitrary real values of the momentum transfer.

Program summary

Title of program:sunemVersion: 1.0Release: 1Catalogue identifier: ADYC_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADYC_v1_0Program obtainable from:http://www-ttp.physik.uni-karlsruhe.de/Progdata/Computers: allOperating system: allProgram language used:FORTRAN77No. of lines in distributed program, including test data, etc.:1080No. of bytes in distributed program, including test data, etc.: 11 835Memory required to execute: Size: 1532KNo. of bits in a word: up to 32No. of processors used: 1Distribution format: tar.gzOther programs called: noneExternal files needed: noneNature of the physical problem: Numerical evaluation of the two Master Integrals of the equal mass two-loop sunrise Feynman graph for arbitrary momentum transfer in d=2 and d=4 dimensions.Method of solution: Accelerated power series expansions obtained by solving the differential equations for the MIs at their singular points. With a maximum of 22 terms for the worse case expansion a relative precision of better than a part in 10¹⁵ is achieved for arbitrary real values of the momentum transfer.Restrictions on complexity of the problem: Limited to real momentum transfer and equal internal masses.Typical running time: Approximately 1 μs to evaluate the four Master integrals for a fixed momentum transfer value on a Pentium IV/3 GHz Linux PC.     

Algorithmic derivation of Dyson-Schwinger equations

We present an algorithm for the derivation of Dyson-Schwinger equations of general theories that is suitable for an implementation within a symbolic programming language. Moreover, we introduce the Mathematica package DoDSE¹ which provides such an implementation. It derives the Dyson-Schwinger equations graphically once the interactions of the theory are specified. A few examples for the application of both the algorithm and the DoDSE package are provided.

Program summary

Program title: DoDSECatalogue identifier: AECT_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AECT_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 105 874No. of bytes in distributed program, including test data, etc.: 262 446Distribution format: tar.gzProgramming language: Mathematica 6 and higherComputer: all on which Mathematica is availableOperating system: all on which Mathematica is availableClassification: 11.1, 11.4, 11.5, 11.6Nature of problem: Derivation of Dyson-Schwinger equations for a theory with given interactions.Solution method: Implementation of an algorithm for the derivation of Dyson-Schwinger equations.Unusual features: The results can be plotted as Feynman diagrams in Mathematica.Running time: Less than a second to minutes for Dyson-Schwinger equations of higher vertex functions.     

A recursive algorithm for finding HDMR terms for sensitivity analysis

High Dimensional Model Representation (HDMR) is an efficient technique which decomposes a multivariate function into a constant, univariate, bivariate functions and so on. These functions are forced to be mutually orthogonal by means of an orthogonality condition. The technique which is generally used for high-dimensional input-output systems can be applied to various disciplines including sensitivity analysis, differential equations, inversion of data and so on. In this article we present a computer program that computes individual components of HDMR resolution of a given multivariate function. The program also calculates the global sensitivity indices. Lastly the results of the numerical experiments for different set of functions are introduced.     

Solution of Hallen's integral equation using multiwavelets

An effective method based upon Alpert multiwavelets is proposed for the solution of Hallen's integral equation. The properties of Alpert multiwavelets are first given. These wavelets are utilized to reduce the solution of Hallen's integral equation to the solution of sparse algebraic equations. In order to save memory requirement and computation time, a threshold procedure is applied to obtain algebraic equations. Through numerical examples, performance of the present method is investigated concerning the convergence and the sparseness of resulted matrix equation.     

Reliability conditions in quadrature algorithms

The detection of insufficiently resolved or ill-conditioned integrand structures is critical for the reliability assessment of the quadrature rule outputs. We discuss a method of analysis of the profile of the integrand at the quadrature knots which allows inferences approaching the theoretical 100% rate of success, under error estimate sharpening. The proposed procedure is of the highest interest for the solution of parametric integrals arising in complex physical models.     

Optimized implementations of rational approximations—a case study on the Voigt and complex error function

Rational functions are frequently used as efficient yet accurate numerical approximations for real and complex valued special functions. For the complex error function w(x+iy), whose real part is the Voigt function K(x,y), the rational approximation developed by Hui, Armstrong, and Wray [Rapid computation of the Voigt and complex error functions, J. Quant. Spectrosc. Radiat. Transfer 19 (1978) 509-516] is investigated. Various optimizations for the algorithm are discussed. In many applications, where these functions have to be calculated for a large x grid with constant y, an implementation using real arithmetic and factorization of invariant terms is especially efficient.     

GDF v2.0, an enhanced version of GDF

An improved version of the function estimation program GDF is presented. The main enhancements of the new version include: multi-output function estimation, capability of defining custom functions in the grammar and selection of the error function. The new version has been evaluated on a series of classification and regression datasets, that are widely used for the evaluation of such methods. It is compared to two known neural networks and outperforms them in 5 (out of 10) datasets.

Program summary

Title of program: GDF v2.0Catalogue identifier: ADXC_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXC_v2_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 98 147No. of bytes in distributed program, including test data, etc.: 2 040 684Distribution format: tar.gzProgramming language: GNU C++Computer: The program is designed to be portable in all systems running the GNU C++ compilerOperating system: Linux, Solaris, FreeBSDRAM: 200000 bytesClassification: 4.9Does the new version supersede the previous version?: YesNature of problem: The technique of function estimation tries to discover from a series of input data a functional form that best describes them. This can be performed with the use of parametric models, whose parameters can adapt according to the input data.Solution method: Functional forms are being created by genetic programming which are approximations for the symbolic regression problem.Reasons for new version: The GDF package was extended in order to be more flexible and user customizable than the old package. The user can extend the package by defining his own error functions and he can extend the grammar of the package by adding new functions to the function repertoire. Also, the new version can perform function estimation of multi-output functions and it can be used for classification problems.Summary of revisions: The following features have been added to the package GDF:

•

Multi-output function approximation. The package can now approximate any function . This feature gives also to the package the capability of performing classification and not only regression.

•

User defined function can be added to the repertoire of the grammar, extending the regression capabilities of the package. This feature is limited to 3 functions, but easily this number can be increased.

•

Capability of selecting the error function. The package offers now to the user apart from the mean square error other error functions such as: mean absolute square error, maximum square error. Also, user defined error functions can be added to the set of error functions.

•

More verbose output. The main program displays more information to the user as well as the default values for the parameters. Also, the package gives to the user the capability to define an output file, where the output of the gdf program for the testing set will be stored after the termination of the process.

Additional comments: A technical report describing the revisions, experiments and test runs is packaged with the source code.Running time: Depending on the train data.     

On the numerical treatment of an ordinary differential equation arising in one-dimensional non-Fickian diffusion problems

In a recent study, Chen and Liu [Comput. Phys. Comm. 150 (2003) 31] considered a one-dimensional, linear non-Fickian diffusion problem with a potential field, which, upon application of the Laplace transform, resulted in a second-order linear ordinary differential equation which was solved by means of a control-volume finite difference method that employs exponential shape functions. It is first shown that this formulation does not properly account for the spatial dependence of the drift forces and results in oscillatory solutions near the left boundary when these forces are large. A piecewise linearized method that provides piecewise analytical solutions, is exact in exact arithmetic for constant coefficients, homogeneous, second-order linear ordinary differential equations and results in three-point finite difference equations is then proposed. Numerical simulations indicate that the piecewise linearized method is free from unphysical oscillations and more accurate than that of Chen and Liu, especially for large drift forces. The method is then applied to non-Fickian diffusion problems with non-constant drift forces in order to determine the effects of the potential field on the concentration distribution.     

GDF: A tool for function estimation through grammatical evolution

This article introduces a tool for data fitting that is based on genetic programming and especially on the grammatical evolution technique. The user needs to input a series of points and the accompanied dimensionality n and the tool will produce via the genetic programming paradigm a function which is an approximate solution to the symbolic regression problem. The tool is entirely written in ANSI C++ and it can be installed in any UNIX system.

Program summary

Title of program: GDFCatalogue identifier:ADXCProgram obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandProgram summary URL:http://cpc.cs.qub.ac.uk/summaries/ADXCComputer for which the program is designed and others on which is has been tested:The tool is designed to be portable in all systems running the GNU C++ compilerInstallation: University of Ioannina and University of Patras, GreeceProgramming language used:GNU-C++Memory required to execute with typical data:200 KBNo. of bits in a word: 32No. of processors used: 1Has the code been vectorized or parallelized?: NoNo. of bytes in distributed program, including test data, etc.: 33 469No. of lines in distributed program, including test data, etc.: 5704Distribution format: tar.gzSolution method: Functional forms are being created by genetic programming which are approximations for the symbolic regression problem.     

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.     

Piecewise quasilinearization techniques for singular boundary-value problems

Piecewise quasilinearization methods for singular boundary-value problems in second-order ordinary differential equations are presented. These methods result in linear constant-coefficients ordinary differential equations which can be integrated analytically, thus yielding piecewise analytical solutions. The accuracy of the globally smooth piecewise quasilinear method is assessed by comparisons with exact solutions of several Lane-Emden equations, a singular problem of non-Newtonian fluid dynamics and the Thomas-Fermi equation. It is shown that the smooth piecewise quasilinearization method provides accurate solutions even near the singularity and is more precise than (iterative) second-order accurate finite difference discretizations. It is also shown that the accuracy of the smooth piecewise quasilinear method depends on the kind of singularity, nonlinearity and inhomogeneities of singular ordinary differential equations. For the Thomas-Fermi equation, it is shown that the piecewise quasilinearization method that provides globally smooth solutions is more accurate than that which only insures global continuity, and more accurate than global quasilinearization techniques which do not employ local linearization.