A Computer Algebra System for R: Macaulay2 and the M2r Package
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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. -
Redberry: a Computer Algebra System Designed for Tensor Manipulation
Redberry: a computer algebra system designed for tensor manipulation Stanislav Poslavsky Institute for High Energy Physics, Protvino, Russia SRRC RF ITEP of NRC Kurchatov Institute, Moscow, Russia E-mail: [email protected] Dmitry Bolotin Institute of Bioorganic Chemistry of RAS, Moscow, Russia E-mail: [email protected] Abstract. In this paper we focus on the main aspects of computer-aided calculations with tensors and present a new computer algebra system Redberry which was specifically designed for algebraic tensor manipulation. We touch upon distinctive features of tensor software in comparison with pure scalar systems, discuss the main approaches used to handle tensorial expressions and present the comparison of Redberry performance with other relevant tools. 1. Introduction General-purpose computer algebra systems (CASs) have become an essential part of many scientific calculations. Focusing on the area of theoretical physics and particularly high energy physics, one can note that there is a wide area of problems that deal with tensors (or more generally | objects with indices). This work is devoted to the algebraic manipulations with abstract indexed expressions which forms a substantial part of computer aided calculations with tensors in this field of science. Today, there are many packages both on top of general-purpose systems (Maple Physics [1], xAct [2], Tensorial etc.) and standalone tools (Cadabra [3,4], SymPy [5], Reduce [6] etc.) that cover different topics in symbolic tensor calculus. However, it cannot be said that current demand on such a software is fully satisfied [7]. The main difference of tensorial expressions (in comparison with ordinary indexless) lies in the presence of contractions between indices. -
Utilizing MATHEMATICA Software to Improve Students' Problem Solving
International Journal of Education and Research Vol. 7 No. 11 November 2019 Utilizing MATHEMATICA Software to Improve Students’ Problem Solving Skills of Derivative and its Applications Hiyam, Bataineh , Jordan University of Science and Technology, Jordan Ali Zoubi, Yarmouk University, Jordan Abdalla Khataybeh, Yarmouk University, Jordan Abstract Traditional methods of teaching calculus (1) at most Jordanian universities are usually used. This paper attempts to introduce an innovative approach for teaching Calculus (1) using Computerized Algebra Systems (CAS). This paper examined utilizing Mathematica as a supporting tool for the teaching learning process of Calculus (1), especially for derivative and its applications. The research created computerized educational materials using Mathematica, which was used as an approach for teaching a (25) students as experimental group and another (25) students were taught traditionally as control group. The understandings of both samples were tested using problem solving test of 6 questions. The results revealed the experimental group outscored the control group significantly on the problem solving test. The use of Mathematica not just improved students’ abilities to interpret graphs and make connection between the graph of a function and its derivative, but also motivate students’ thinking in different ways to come up with innovative solutions for unusual and non routine problems involving the derivative and its applications. This research suggests that CAS tools should be integrated to teaching calculus (1) courses. Keywords: Mathematica, Problem solving, Derivative, Derivative’s applivations. INTRODUCTION The major technological advancements assisted decision makers and educators to pay greater attention on making the learner as the focus of the learning-teaching process. The use of computer algebra systems (CAS) in teaching mathematics has proved to be more effective compared to traditional teaching methods (Irving & Bell, 2004; Dhimar & Petal, 2013; Abdul Majid, huneiti, Al- Nafa & Balachander, 2012). -
Performance Analysis of Effective Symbolic Methods for Solving Band Matrix Slaes
Performance Analysis of Effective Symbolic Methods for Solving Band Matrix SLAEs Milena Veneva ∗1 and Alexander Ayriyan y1 1Joint Institute for Nuclear Research, Laboratory of Information Technologies, Joliot-Curie 6, 141980 Dubna, Moscow region, Russia Abstract This paper presents an experimental performance study of implementations of three symbolic algorithms for solving band matrix systems of linear algebraic equations with heptadiagonal, pen- tadiagonal, and tridiagonal coefficient matrices. The only assumption on the coefficient matrix in order for the algorithms to be stable is nonsingularity. These algorithms are implemented using the GiNaC library of C++ and the SymPy library of Python, considering five different data storing classes. Performance analysis of the implementations is done using the high-performance computing (HPC) platforms “HybriLIT” and “Avitohol”. The experimental setup and the results from the conducted computations on the individual computer systems are presented and discussed. An analysis of the three algorithms is performed. 1 Introduction Systems of linear algebraic equations (SLAEs) with heptadiagonal (HD), pentadiagonal (PD) and tridiagonal (TD) coefficient matrices may arise after many different scientific and engineering prob- lems, as well as problems of the computational linear algebra where finding the solution of a SLAE is considered to be one of the most important problems. On the other hand, special matrix’s char- acteristics like diagonal dominance, positive definiteness, etc. are not always feasible. The latter two points explain why there is a need of methods for solving of SLAEs which take into account the band structure of the matrices and do not have any other special requirements to them. One possible approach to this problem is the symbolic algorithms. -
The Yacas Book of Algorithms
The Yacas Book of Algorithms by the Yacas team 1 Yacas version: 1.3.6 generated on November 25, 2014 This book is a detailed description of the algorithms used in the Yacas system for exact symbolic and arbitrary-precision numerical computations. Very few of these algorithms are new, and most are well-known. The goal of this book is to become a compendium of all relevant issues of design and implementation of these algorithms. 1This text is part of the Yacas software package. Copyright 2000{2002. Principal documentation authors: Ayal Zwi Pinkus, Serge Winitzki, Jitse Niesen. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts and no Back-Cover Texts. A copy of the license is included in the section entitled \GNU Free Documentation License". Contents 1 Symbolic algebra algorithms 3 1.1 Sparse representations . 3 1.2 Implementation of multivariate polynomials . 4 1.3 Integration . 5 1.4 Transforms . 6 1.5 Finding real roots of polynomials . 7 2 Number theory algorithms 10 2.1 Euclidean GCD algorithms . 10 2.2 Prime numbers: the Miller-Rabin test and its improvements . 10 2.3 Factorization of integers . 11 2.4 The Jacobi symbol . 12 2.5 Integer partitions . 12 2.6 Miscellaneous functions . 13 2.7 Gaussian integers . 13 3 A simple factorization algorithm for univariate polynomials 15 3.1 Modular arithmetic . 15 3.2 Factoring using modular arithmetic . -
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. -
Persistence in Algebraic Specifications
Persistence in Algebraic Specifications Freek Wiedijk Persistence in Algebraic Specifications Persistence in Algebraic Specifications Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam, op gezag van de Rector Magnificus prof. dr. P.W.M. de Meijer, in het openbaar te verdedigen in de Aula der Universiteit (Oude Lutherse Kerk, ingang Singel 411, hoek Spui), op donderdag 12 december 1991 te 12.00 uur door Frederik Wiedijk geboren te Haarlem Promotores: prof. dr. P. Klint & prof. dr. J.A. Bergstra Faculteit Wiskunde en Informatica Het hier beschreven onderzoek werd mede mogelijk gemaakt door steun van de Ne- derlandse organisatie voor Wetenschappelijk Onderzoek (voorheen de nederlandse organisatie voor Zuiver-Wetenschappelijk Onderzoek) binnen project nummer 612- 317-013 getiteld Executeerbaar maken van algebraïsche specificaties. voor Jan Truijens Contents Contents Acknowledgements 1 Introduction 1 1.1 Motivation and general overview 2 1.2 Examples of erroneous specifications 13 1.3 Background and related work 22 2. Theory 25 2.1 Basic notions 27 2.1.1. Equational specifications 28 2.1.2 Term rewriting systems 29 2.2 Termination 30 2.2.1 Weak and strong termination 33 2.2.2 Path orderings 34 2.2.3 Decidability 40 2.3 Confluence 41 2.3.1 Weak and strong confluence 42 2.3.2 Overlapping 44 2.3.3 Decidability 45 2.4 Reduction strategies 46 2.5 Persistence 48 2.5.1 Weak and strong persistence 52 2.5.2 Term approximations and bases 54 2.5.3 Normal form analysis 56 2.5.4 Decidability 60 2.6 Primitive recursive -
Computer Algebra Tems
short and expensive. Consequently, computer centre managers were not easily persuaded to install such sys Computer Algebra tems. This is another reason why many specialized CAS have been developed in many different institutions. From the Jacques Calmet, Grenoble * very beginning it was felt that the (UFIA / INPG) breakthrough of Computer Algebra is linked to the availability of adequate computers: several megabytes of main As soon as computers became avai handling of very large pieces of algebra memory and very large capacity disks. lable the wish to manipulate symbols which would be hopeless without com Since the advent of the VAX's and the rather than numbers was considered. puter aid, the possibility to get insight personal workstations, this era is open This resulted in what is called symbolic into a calculation, to experiment ideas ed and indeed the use of CAS is computing. The sub-field of symbolic interactively and to save time for less spreading very quickly. computing which deals with the sym technical problems. When compared to What are the main CAS of possible bolic solution of mathematical problems numerical computing, it is also much interest to physicists? A fairly balanced is known today as Computer Algebra. closer to human reasoning. answer is to quote MACSYMA, RE The discipline was rapidly organized, in DUCE, SMP, MAPLE among the general 1962, by a special interest group on Computer Algebra Systems purpose ones and SCHOONSHIP, symbolic and algebraic manipulation, Computer Algebra systems may be SHEEP and CAYLEY among the specia SIGSAM, of the Association for Com classified into two different categories: lized ones. -
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. -
Research in Algebraic Geometry with Macaulay2
Research in Algebraic Geometry with Macaulay2 1 Macaulay2 a software system for research in algebraic geometry resultants the community, packages, the journal, and workshops 2 Tutorial the ideal of a monomial curve elimination: projecting the twisted cubic quotients and saturation 3 the package PHCpack.m2 solving polynomial systems numerically MCS 507 Lecture 39 Mathematical, Statistical and Scientific Software Jan Verschelde, 25 November 2019 Scientific Software (MCS 507) Macaulay2 L-39 25November2019 1/32 Research in Algebraic Geometry with Macaulay2 1 Macaulay2 a software system for research in algebraic geometry resultants the community, packages, the journal, and workshops 2 Tutorial the ideal of a monomial curve elimination: projecting the twisted cubic quotients and saturation 3 the package PHCpack.m2 solving polynomial systems numerically Scientific Software (MCS 507) Macaulay2 L-39 25November2019 2/32 Macaulay2 D. R. Grayson and M. E. Stillman: Macaulay2, a software system for research in algebraic geometry, available at https://faculty.math.illinois.edu/Macaulay2. Funded by the National Science Foundation since 1992. Its source is at https://github.com/Macaulay2/M2; licence: GNU GPL version 2 or 3; try it online at http://habanero.math.cornell.edu:3690. Several workshops are held each year. Packages extend the functionality. The Journal of Software for Algebra and Geometry (at https://msp.org/jsag) started its first volume in 2009. The SageMath interface to Macaulay2 requires its binary M2 to be installed on your computer. Scientific