A Symbolic Computer Language for Multibody Systems Michael W
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The Sagemanifolds Project
Tensor calculus with free softwares: the SageManifolds project Eric´ Gourgoulhon1, Micha l Bejger2 1Laboratoire Univers et Th´eories (LUTH) CNRS / Observatoire de Paris / Universit´eParis Diderot 92190 Meudon, France http://luth.obspm.fr/~luthier/gourgoulhon/ 2Centrum Astronomiczne im. M. Kopernika (CAMK) Warsaw, Poland http://users.camk.edu.pl/bejger/ Encuentros Relativistas Espa~noles2014 Valencia 1-5 September 2014 Eric´ Gourgoulhon, Micha l Bejger (LUTH, CAMK) SageManifolds ERE2014, Valencia, 2 Sept. 2014 1 / 44 Outline 1 Differential geometry and tensor calculus on a computer 2 Sage: a free mathematics software 3 The SageManifolds project 4 SageManifolds at work: the Mars-Simon tensor example 5 Conclusion and perspectives Eric´ Gourgoulhon, Micha l Bejger (LUTH, CAMK) SageManifolds ERE2014, Valencia, 2 Sept. 2014 2 / 44 Differential geometry and tensor calculus on a computer Outline 1 Differential geometry and tensor calculus on a computer 2 Sage: a free mathematics software 3 The SageManifolds project 4 SageManifolds at work: the Mars-Simon tensor example 5 Conclusion and perspectives Eric´ Gourgoulhon, Micha l Bejger (LUTH, CAMK) SageManifolds ERE2014, Valencia, 2 Sept. 2014 3 / 44 In 1969, during his PhD under Pirani supervision at King's College, Ray d'Inverno wrote ALAM (Atlas Lisp Algebraic Manipulator) and used it to compute the Riemann tensor of Bondi metric. The original calculations took Bondi and his collaborators 6 months to go. The computation with ALAM took 4 minutes and yield to the discovery of 6 errors in the original paper [J.E.F. Skea, Applications of SHEEP (1994)] In the early 1970's, ALAM was rewritten in the LISP programming language, thereby becoming machine independent and renamed LAM The descendant of LAM, called SHEEP (!), was initiated in 1977 by Inge Frick Since then, many softwares for tensor calculus have been developed.. -
Using Macaulay2 Effectively in Practice
Using Macaulay2 effectively in practice Mike Stillman ([email protected]) Department of Mathematics Cornell 22 July 2019 / IMA Sage/M2 Macaulay2: at a glance Project started in 1993, Dan Grayson and Mike Stillman. Open source. Key computations: Gr¨obnerbases, free resolutions, Hilbert functions and applications of these. Rings, Modules and Chain Complexes are first class objects. Language which is comfortable for mathematicians, yet powerful, expressive, and fun to program in. Now a community project Journal of Software for Algebra and Geometry (started in 2009. Now we handle: Macaulay2, Singular, Gap, Cocoa) (original editors: Greg Smith, Amelia Taylor). Strong community: including about 2 workshops per year. User contributed packages (about 200 so far). Each has doc and tests, is tested every night, and is distributed with M2. Lots of activity Over 2000 math papers refer to Macaulay2. History: 1976-1978 (My undergrad years at Urbana) E. Graham Evans: asked me to write a program to compute syzygies, from Hilbert's algorithm from 1890. Really didn't work on computers of the day (probably might still be an issue!). Instead: Did computation degree by degree, no finishing condition. Used Buchsbaum-Eisenbud \What makes a complex exact" (by hand!) to see if the resulting complex was exact. Winfried Bruns was there too. Very exciting time. History: 1978-1983 (My grad years, with Dave Bayer, at Harvard) History: 1978-1983 (My grad years, with Dave Bayer, at Harvard) I tried to do \real mathematics" but Dave Bayer (basically) rediscovered Groebner bases, and saw that they gave an algorithm for computing all syzygies. I got excited, dropped what I was doing, and we programmed (in Pascal), in less than one week, the first version of what would be Macaulay. -
Some Reflections About the Success and Impact of the Computer Algebra System DERIVE with a 10-Year Time Perspective
Noname manuscript No. (will be inserted by the editor) Some reflections about the success and impact of the computer algebra system DERIVE with a 10-year time perspective Eugenio Roanes-Lozano · Jose Luis Gal´an-Garc´ıa · Carmen Solano-Mac´ıas Received: date / Accepted: date Abstract The computer algebra system DERIVE had a very important im- pact in teaching mathematics with technology, mainly in the 1990's. The au- thors analyze the possible reasons for its success and impact and give personal conclusions based on the facts collected. More than 10 years after it was discon- tinued it is still used for teaching and several scientific papers (most devoted to educational issues) still refer to it. A summary of the history, journals and conferences together with a brief bibliographic analysis are included. Keywords Computer algebra systems · DERIVE · educational software · symbolic computation · technology in mathematics education Eugenio Roanes-Lozano Instituto de Matem´aticaInterdisciplinar & Departamento de Did´actica de las Ciencias Experimentales, Sociales y Matem´aticas, Facultad de Educaci´on,Universidad Complutense de Madrid, c/ Rector Royo Villanova s/n, 28040-Madrid, Spain Tel.: +34-91-3946248 Fax: +34-91-3946133 E-mail: [email protected] Jose Luis Gal´an-Garc´ıa Departamento de Matem´atica Aplicada, Universidad de M´alaga, M´alaga, c/ Dr. Ortiz Ramos s/n. Campus de Teatinos, E{29071 M´alaga,Spain Tel.: +34-952-132873 Fax: +34-952-132766 E-mail: [email protected] Carmen Solano-Mac´ıas Departamento de Informaci´ony Comunicaci´on,Universidad de Extremadura, Plaza de Ibn Marwan s/n, 06001-Badajoz, Spain Tel.: +34-924-286400 Fax: +34-924-286401 E-mail: [email protected] 2 Eugenio Roanes-Lozano et al. -
Fortran 90 Overview
1 Fortran 90 Overview J.E. Akin, Copyright 1998 This overview of Fortran 90 (F90) features is presented as a series of tables that illustrate the syntax and abilities of F90. Frequently comparisons are made to similar features in the C++ and F77 languages and to the Matlab environment. These tables show that F90 has significant improvements over F77 and matches or exceeds newer software capabilities found in C++ and Matlab for dynamic memory management, user defined data structures, matrix operations, operator definition and overloading, intrinsics for vector and parallel pro- cessors and the basic requirements for object-oriented programming. They are intended to serve as a condensed quick reference guide for programming in F90 and for understanding programs developed by others. List of Tables 1 Comment syntax . 4 2 Intrinsic data types of variables . 4 3 Arithmetic operators . 4 4 Relational operators (arithmetic and logical) . 5 5 Precedence pecking order . 5 6 Colon Operator Syntax and its Applications . 5 7 Mathematical functions . 6 8 Flow Control Statements . 7 9 Basic loop constructs . 7 10 IF Constructs . 8 11 Nested IF Constructs . 8 12 Logical IF-ELSE Constructs . 8 13 Logical IF-ELSE-IF Constructs . 8 14 Case Selection Constructs . 9 15 F90 Optional Logic Block Names . 9 16 GO TO Break-out of Nested Loops . 9 17 Skip a Single Loop Cycle . 10 18 Abort a Single Loop . 10 19 F90 DOs Named for Control . 10 20 Looping While a Condition is True . 11 21 Function definitions . 11 22 Arguments and return values of subprograms . 12 23 Defining and referring to global variables . -
Quick Overview: Complex Numbers
Quick Overview: Complex Numbers February 23, 2012 1 Initial Definitions Definition 1 The complex number z is defined as: z = a + bi (1) p where a, b are real numbers and i = −1. Remarks about the definition: • Engineers typically use j instead of i. • Examples of complex numbers: p 5 + 2i; 3 − 2i; 3; −5i • Powers of i: i2 = −1 i3 = −i i4 = 1 i5 = i i6 = −1 i7 = −i . • All real numbers are also complex (by taking b = 0). 2 Visualizing Complex Numbers A complex number is defined by it's two real numbers. If we have z = a + bi, then: Definition 2 The real part of a + bi is a, Re(z) = Re(a + bi) = a The imaginary part of a + bi is b, Im(z) = Im(a + bi) = b 1 Im(z) 4i 3i z = a + bi 2i r b 1i θ Re(z) a −1i Figure 1: Visualizing z = a + bi in the complex plane. Shown are the modulus (or length) r and the argument (or angle) θ. To visualize a complex number, we use the complex plane C, where the horizontal (or x-) axis is for the real part, and the vertical axis is for the imaginary part. That is, a + bi is plotted as the point (a; b). In Figure 1, we can see that it is also possible to represent the point a + bi, or (a; b) in polar form, by computing its modulus (or size), and angle (or argument): p r = jzj = a2 + b2 θ = arg(z) We have to be a bit careful defining φ, since there are many ways to write φ (and we could add multiples of 2π as well). -
CAS (Computer Algebra System) Mathematica
CAS (Computer Algebra System) Mathematica- UML students can download a copy for free as part of the UML site license; see the course website for details From: Wikipedia 2/9/2014 A computer algebra system (CAS) is a software program that allows [one] to compute with mathematical expressions in a way which is similar to the traditional handwritten computations of the mathematicians and other scientists. The main ones are Axiom, Magma, Maple, Mathematica and Sage (the latter includes several computer algebras systems, such as Macsyma and SymPy). Computer algebra systems began to appear in the 1960s, and evolved out of two quite different sources—the requirements of theoretical physicists and research into artificial intelligence. A prime example for the first development was the pioneering work conducted by the later Nobel Prize laureate in physics Martin Veltman, who designed a program for symbolic mathematics, especially High Energy Physics, called Schoonschip (Dutch for "clean ship") in 1963. Using LISP as the programming basis, Carl Engelman created MATHLAB in 1964 at MITRE within an artificial intelligence research environment. Later MATHLAB was made available to users on PDP-6 and PDP-10 Systems running TOPS-10 or TENEX in universities. Today it can still be used on SIMH-Emulations of the PDP-10. MATHLAB ("mathematical laboratory") should not be confused with MATLAB ("matrix laboratory") which is a system for numerical computation built 15 years later at the University of New Mexico, accidentally named rather similarly. The first popular computer algebra systems were muMATH, Reduce, Derive (based on muMATH), and Macsyma; a popular copyleft version of Macsyma called Maxima is actively being maintained. -
Fortran Math Special Functions Library
IMSL® Fortran Math Special Functions Library Version 2021.0 Copyright 1970-2021 Rogue Wave Software, Inc., a Perforce company. Visual Numerics, IMSL, and PV-WAVE are registered trademarks of Rogue Wave Software, Inc., a Perforce company. IMPORTANT NOTICE: Information contained in this documentation is subject to change without notice. Use of this docu- ment is subject to the terms and conditions of a Rogue Wave Software License Agreement, including, without limitation, the Limited Warranty and Limitation of Liability. ACKNOWLEDGMENTS Use of the Documentation and implementation of any of its processes or techniques are the sole responsibility of the client, and Perforce Soft- ware, Inc., assumes no responsibility and will not be liable for any errors, omissions, damage, or loss that might result from any use or misuse of the Documentation PERFORCE SOFTWARE, INC. MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THE DOCUMENTATION. THE DOCU- MENTATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. PERFORCE SOFTWARE, INC. HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THE DOCUMENTATION, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHERWISE, INCLUDING WITHOUT LIMITATION ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PAR- TICULAR PURPOSE, OR NONINFRINGEMENT. IN NO EVENT SHALL PERFORCE SOFTWARE, INC. BE LIABLE, WHETHER IN CONTRACT, TORT, OR OTHERWISE, FOR ANY SPECIAL, CONSEQUENTIAL, INDIRECT, PUNITIVE, OR EXEMPLARY DAMAGES IN CONNECTION WITH THE USE OF THE DOCUMENTATION. The Documentation is subject to change at any time without notice. IMSL https://www.imsl.com/ Contents Introduction The IMSL Fortran Numerical Libraries . 1 Getting Started . 2 Finding the Right Routine . 3 Organization of the Documentation . 4 Naming Conventions . -
Singular Value Decomposition and Its Numerical Computations
Singular Value Decomposition and its numerical computations Wen Zhang, Anastasios Arvanitis and Asif Al-Rasheed ABSTRACT The Singular Value Decomposition (SVD) is widely used in many engineering fields. Due to the important role that the SVD plays in real-time computations, we try to study its numerical characteristics and implement the numerical methods for calculating it. Generally speaking, there are two approaches to get the SVD of a matrix, i.e., direct method and indirect method. The first one is to transform the original matrix to a bidiagonal matrix and then compute the SVD of this resulting matrix. The second method is to obtain the SVD through the eigen pairs of another square matrix. In this project, we implement these two kinds of methods and develop the combined methods for computing the SVD. Finally we compare these methods with the built-in function in Matlab (svd) regarding timings and accuracy. 1. INTRODUCTION The singular value decomposition is a factorization of a real or complex matrix and it is used in many applications. Let A be a real or a complex matrix with m by n dimension. Then the SVD of A is: where is an m by m orthogonal matrix, Σ is an m by n rectangular diagonal matrix and is the transpose of n ä n matrix. The diagonal entries of Σ are known as the singular values of A. The m columns of U and the n columns of V are called the left singular vectors and right singular vectors of A, respectively. Both U and V are orthogonal matrices. -
IEEE Standard 754 for Binary Floating-Point Arithmetic
Work in Progress: Lecture Notes on the Status of IEEE 754 October 1, 1997 3:36 am Lecture Notes on the Status of IEEE Standard 754 for Binary Floating-Point Arithmetic Prof. W. Kahan Elect. Eng. & Computer Science University of California Berkeley CA 94720-1776 Introduction: Twenty years ago anarchy threatened floating-point arithmetic. Over a dozen commercially significant arithmetics boasted diverse wordsizes, precisions, rounding procedures and over/underflow behaviors, and more were in the works. “Portable” software intended to reconcile that numerical diversity had become unbearably costly to develop. Thirteen years ago, when IEEE 754 became official, major microprocessor manufacturers had already adopted it despite the challenge it posed to implementors. With unprecedented altruism, hardware designers had risen to its challenge in the belief that they would ease and encourage a vast burgeoning of numerical software. They did succeed to a considerable extent. Anyway, rounding anomalies that preoccupied all of us in the 1970s afflict only CRAY X-MPs — J90s now. Now atrophy threatens features of IEEE 754 caught in a vicious circle: Those features lack support in programming languages and compilers, so those features are mishandled and/or practically unusable, so those features are little known and less in demand, and so those features lack support in programming languages and compilers. To help break that circle, those features are discussed in these notes under the following headings: Representable Numbers, Normal and Subnormal, Infinite -
An Alternative Algorithm for Computing the Betti Table of a Monomial Ideal 3
AN ALTERNATIVE ALGORITHM FOR COMPUTING THE BETTI TABLE OF A MONOMIAL IDEAL MARIA-LAURA TORRENTE AND MATTEO VARBARO Abstract. In this paper we develop a new technique to compute the Betti table of a monomial ideal. We present a prototype implementation of the resulting algorithm and we perform numerical experiments suggesting a very promising efficiency. On the way of describing the method, we also prove new constraints on the shape of the possible Betti tables of a monomial ideal. 1. Introduction Since many years syzygies, and more generally free resolutions, are central in purely theo- retical aspects of algebraic geometry; more recently, after the connection between algebra and statistics have been initiated by Diaconis and Sturmfels in [DS98], free resolutions have also become an important tool in statistics (for instance, see [D11, SW09]). As a consequence, it is fundamental to have efficient algorithms to compute them. The usual approach uses Gr¨obner bases and exploits a result of Schreyer (for more details see [Sc80, Sc91] or [Ei95, Chapter 15, Section 5]). The packages for free resolutions of the most used computer algebra systems, like [Macaulay2, Singular, CoCoA], are based on these techniques. In this paper, we introduce a new algorithmic method to compute the minimal graded free resolution of any finitely generated graded module over a polynomial ring such that some (possibly non- minimal) graded free resolution is known a priori. We describe this method and we present the resulting algorithm in the case of monomial ideals in a polynomial ring, in which situ- ation we always have a starting nonminimal graded free resolution. -
Computations in Algebraic Geometry with Macaulay 2
Computations in algebraic geometry with Macaulay 2 Editors: D. Eisenbud, D. Grayson, M. Stillman, and B. Sturmfels Preface Systems of polynomial equations arise throughout mathematics, science, and engineering. Algebraic geometry provides powerful theoretical techniques for studying the qualitative and quantitative features of their solution sets. Re- cently developed algorithms have made theoretical aspects of the subject accessible to a broad range of mathematicians and scientists. The algorith- mic approach to the subject has two principal aims: developing new tools for research within mathematics, and providing new tools for modeling and solv- ing problems that arise in the sciences and engineering. A healthy synergy emerges, as new theorems yield new algorithms and emerging applications lead to new theoretical questions. This book presents algorithmic tools for algebraic geometry and experi- mental applications of them. It also introduces a software system in which the tools have been implemented and with which the experiments can be carried out. Macaulay 2 is a computer algebra system devoted to supporting research in algebraic geometry, commutative algebra, and their applications. The reader of this book will encounter Macaulay 2 in the context of concrete applications and practical computations in algebraic geometry. The expositions of the algorithmic tools presented here are designed to serve as a useful guide for those wishing to bring such tools to bear on their own problems. A wide range of mathematical scientists should find these expositions valuable. This includes both the users of other programs similar to Macaulay 2 (for example, Singular and CoCoA) and those who are not interested in explicit machine computations at all. -
Singular in Sage L-38 22November2019 1/40 Computing with Polynomials in Singular
Computing with Polynomials in Singular 1 Polynomials, Resultants, and Factorization computer algebra for polynomial computations resultants to eliminate variables 2 Gröbner Bases and Multiplication Matrices ideals of polynomials and Gröbner bases normal forms and multiplication matrices multiplicity as the dimension of the local quotient ring 3 Formulas for the 4-bar Coupler Point formulation of the problem processing a Gröbner basis MCS 507 Lecture 38 Mathematical, Statistical and Scientific Software Jan Verschelde, 22 November 2019 Scientific Software (MCS 507) Singular in Sage L-38 22November2019 1/40 Computing with Polynomials in Singular 1 Polynomials, Resultants, and Factorization computer algebra for polynomial computations resultants to eliminate variables 2 Gröbner Bases and Multiplication Matrices ideals of polynomials and Gröbner bases normal forms and multiplication matrices multiplicity as the dimension of the local quotient ring 3 Formulas for the 4-bar Coupler Point formulation of the problem processing a Gröbner basis Scientific Software (MCS 507) Singular in Sage L-38 22November2019 2/40 Singular Singular is a computer algebra system for polynomial computations, with special emphasis on commutative and non-commutative algebra, algebraic geometry, and singularity theory, under the GNU General Public License. Singular’s core algorithms handle polynomial factorization and resultants characteristic sets and numerical root finding Gröbner, standard bases, and free resolutions. Its development is directed by Wolfram Decker, Gert-Martin Greuel, Gerhard Pfister, and Hans Schönemann, within the Dept. of Mathematics at the University of Kaiserslautern. Scientific Software (MCS 507) Singular in Sage L-38 22November2019 3/40 Singular in Sage Advanced algorithms are contained in more than 90 libraries, written in a C-like programming language.