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User's Guide to Pari-GP
User's Guide to PARI / GP C. Batut, K. Belabas, D. Bernardi, H. Cohen, M. Olivier Laboratoire A2X, U.M.R. 9936 du C.N.R.S. Universit´eBordeaux I, 351 Cours de la Lib´eration 33405 TALENCE Cedex, FRANCE e-mail: [email protected] Home Page: http://www.parigp-home.de/ Primary ftp site: ftp://megrez.math.u-bordeaux.fr/pub/pari/ last updated 5 November 2000 for version 2.1.0 Copyright c 2000 The PARI Group Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions, or translations, of this manual under the conditions for verbatim copying, provided also that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. PARI/GP is Copyright c 2000 The PARI Group PARI/GP is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation. It is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY WHATSOEVER. Table of Contents Chapter 1: Overview of the PARI system . 5 1.1 Introduction . 5 1.2 The PARI types . 6 1.3 Operations and functions . 9 Chapter 2: Specific Use of the GP Calculator . 13 2.1 Defaults and output formats . 14 2.2 Simple metacommands . 20 2.3 Input formats for the PARI types . 23 2.4 GP operators . -
Plotting Direction Fields in Matlab and Maxima – a Short Tutorial
Plotting direction fields in Matlab and Maxima – a short tutorial Luis Carvalho Introduction A first order differential equation can be expressed as dx x0(t) = = f(t; x) (1) dt where t is the independent variable and x is the dependent variable (on t). Note that the equation above is in normal form. The difficulty in solving equation (1) depends clearly on f(t; x). One way to gain geometric insight on how the solution might look like is to observe that any solution to (1) should be such that the slope at any point P (t0; x0) is f(t0; x0), since this is the value of the derivative at P . With this motivation in mind, if you select enough points and plot the slopes in each of these points, you will obtain a direction field for ODE (1). However, it is not easy to plot such a direction field for any function f(t; x), and so we must resort to some computational help. There are many computational packages to help us in this task. This is a short tutorial on how to plot direction fields for first order ODE’s in Matlab and Maxima. Matlab Matlab is a powerful “computing environment that combines numeric computation, advanced graphics and visualization” 1. A nice package for plotting direction field in Matlab (although resourceful, Matlab does not provide such facility natively) can be found at http://math.rice.edu/»dfield, contributed by John C. Polking from the Dept. of Mathematics at Rice University. To install this add-on, simply browse to the files for your version of Matlab and download them. -
A Note on Integral Points on Elliptic Curves 3
Journal de Th´eorie des Nombres de Bordeaux 00 (XXXX), 000–000 A note on integral points on elliptic curves par Mark WATKINS Abstract. We investigate a problem considered by Zagier and Elkies, of finding large integral points on elliptic curves. By writ- ing down a generic polynomial solution and equating coefficients, we are led to suspect four extremal cases that still might have non- degenerate solutions. Each of these cases gives rise to a polynomial system of equations, the first being solved by Elkies in 1988 using the resultant methods of Macsyma, with there being a unique rational nondegenerate solution. For the second case we found that resultants and/or Gr¨obner bases were not very efficacious. Instead, at the suggestion of Elkies, we used multidimensional p-adic Newton iteration, and were able to find a nondegenerate solution, albeit over a quartic number field. Due to our methodol- ogy, we do not have much hope of proving that there are no other solutions. For the third case we found a solution in a nonic number field, but we were unable to make much progress with the fourth case. We make a few concluding comments and include an ap- pendix from Elkies regarding his calculations and correspondence with Zagier. Resum´ e.´ A` la suite de Zagier et Elkies, nous recherchons de grands points entiers sur des courbes elliptiques. En ´ecrivant une solution polynomiale g´en´erique et en ´egalisant des coefficients, nous obtenons quatre cas extr´emaux susceptibles d’avoir des so- lutions non d´eg´en´er´ees. Chacun de ces cas conduit `aun syst`eme d’´equations polynomiales, le premier ´etant r´esolu par Elkies en 1988 en utilisant les r´esultants de Macsyma; il admet une unique solution rationnelle non d´eg´en´er´ee. -
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. -
Arxiv:Cs/0608005V2 [Cs.SC] 12 Jun 2007
AEI-2006-037 cs.SC/0608005 A field-theory motivated approach to symbolic computer algebra Kasper Peeters Max-Planck-Institut f¨ur Gravitationsphysik, Albert-Einstein-Institut Am M¨uhlenberg 1, 14476 Golm, GERMANY Abstract Field theory is an area in physics with a deceptively compact notation. Although general pur- pose computer algebra systems, built around generic list-based data structures, can be used to represent and manipulate field-theory expressions, this often leads to cumbersome input formats, unexpected side-effects, or the need for a lot of special-purpose code. This makes a direct trans- lation of problems from paper to computer and back needlessly time-consuming and error-prone. A prototype computer algebra system is presented which features TEX-like input, graph data structures, lists with Young-tableaux symmetries and a multiple-inheritance property system. The usefulness of this approach is illustrated with a number of explicit field-theory problems. 1. Field theory versus general-purpose computer algebra For good reasons, the area of general-purpose computer algebra programs has histor- ically been dominated by what one could call “list-based” systems. These are systems which are centred on the idea that, at the lowest level, mathematical expressions are nothing else but nested lists (or equivalently: nested functions, trees, directed acyclic graphs, . ). There is no doubt that a lot of mathematics indeed maps elegantly to problems concerning the manipulation of nested lists, as the success of a large class of LISP-based computer algebra systems illustrates (either implemented in LISP itself or arXiv:cs/0608005v2 [cs.SC] 12 Jun 2007 in another language with appropriate list data structures). -
On the Computation of the HNF of a Module Over the Ring of Integers of A
On the computation of the HNF of a module over the ring of integers of a number field Jean-Fran¸cois Biasse Department of Mathematics and Statistics University of South Florida Claus Fieker Fachbereich Mathematik Universit¨at Kaiserslautern Postfach 3049 67653 Kaiserslautern - Germany Tommy Hofmann Fachbereich Mathematik Universit¨at Kaiserslautern Postfach 3049 67653 Kaiserslautern - Germany Abstract We present a variation of the modular algorithm for computing the Hermite normal form of an K -module presented by Cohen [7], where K is the ring of integers of a numberO field K. An approach presented in [7] basedO on reductions modulo ideals was conjectured to run in polynomial time by Cohen, but so far, no such proof was available in the literature. In this paper, we present a modification of the approach of [7] to prevent the coefficient swell and we rigorously assess its complexity with respect to the size of the input and the invariants of the field K. Keywords: Number field, Dedekind domain, Hermite normal form, relative extension of number fields arXiv:1612.09428v1 [math.NT] 30 Dec 2016 2000 MSC: 11Y40 1. Introduction Algorithms for modules over the rational integers such as the Hermite nor- mal form algorithm are at the core of all methods for computations with rings Email addresses: [email protected] (Jean-Fran¸cois Biasse), [email protected] (Claus Fieker), [email protected] (Tommy Hofmann) Preprint submitted to Elsevier April 22, 2018 and ideals in finite extensions of the rational numbers. Following the growing interest in relative extensions, that is, finite extensions of number fields, the structure of modules over Dedekind domains became important. -
Lisp: Program Is Data
LISP: PROGRAM IS DATA A HISTORICAL PERSPECTIVE ON MACLISP Jon L White Laboratory for Computer Science, M.I.T.* ABSTRACT For over 10 years, MACLISP has supported a variety of projects at M.I.T.'s Artificial Intelligence Laboratory, and the Laboratory for Computer Science (formerly Project MAC). During this time, there has been a continuing development of the MACLISP system, spurred in great measure by the needs of MACSYMAdevelopment. Herein are reported, in amosiac, historical style, the major features of the system. For each feature discussed, an attempt will be made to mention the year of initial development, andthe names of persons or projectsprimarily responsible for requiring, needing, or suggestingsuch features. INTRODUCTION In 1964,Greenblatt and others participated in thecheck-out phase of DigitalEquipment Corporation's new computer, the PDP-6. This machine had a number of innovative features that were thought to be ideal for the development of a list processing system, and thus it was very appropriate that thefirst working program actually run on thePDP-6 was anancestor of thecurrent MACLISP. This earlyLISP was patterned after the existing PDP-1 LISP (see reference l), and was produced by using the text editor and a mini-assembler on the PDP-1. That first PDP-6 finally found its way into M.I.T.'s ProjectMAC for use by theArtificial lntelligence group (the A.1. grouplater became the M.I.T. Artificial Intelligence Laboratory, and Project MAC became the Laboratory for Computer Science). By 1968, the PDP-6 wasrunning the Incompatible Time-sharing system, and was soon supplanted by the PDP-IO.Today, the KL-I 0, anadvanced version of thePDP-10, supports a variety of time sharing systems, most of which are capable of running a MACLISP. -
General Relativity Computations with Sagemanifolds
General relativity computations with SageManifolds Eric´ Gourgoulhon Laboratoire Univers et Th´eories (LUTH) CNRS / Observatoire de Paris / Universit´eParis Diderot 92190 Meudon, France http://luth.obspm.fr/~luthier/gourgoulhon/ NewCompStar School 2016 Neutron stars: gravitational physics theory and observations Coimbra (Portugal) 5-9 September 2016 Eric´ Gourgoulhon (LUTH) GR computations with SageManifolds NewCompStar, Coimbra, 6 Sept. 2016 1 / 29 Outline 1 Computer differential geometry and tensor calculus 2 The SageManifolds project 3 Let us practice! 4 Other examples 5 Conclusion and perspectives Eric´ Gourgoulhon (LUTH) GR computations with SageManifolds NewCompStar, Coimbra, 6 Sept. 2016 2 / 29 Computer differential geometry and tensor calculus Outline 1 Computer differential geometry and tensor calculus 2 The SageManifolds project 3 Let us practice! 4 Other examples 5 Conclusion and perspectives Eric´ Gourgoulhon (LUTH) GR computations with SageManifolds NewCompStar, Coimbra, 6 Sept. 2016 3 / 29 In 1965, J.G. Fletcher developed the GEOM program, to compute the Riemann tensor of a given metric In 1969, during his PhD under Pirani supervision, 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 yielded to the discovery of 6 errors in the original paper [J.E.F. Skea, Applications of SHEEP (1994)] Since then, many softwares for tensor calculus have been developed... Computer differential geometry and tensor calculus Introduction Computer algebra system (CAS) started to be developed in the 1960's; for instance Macsyma (to become Maxima in 1998) was initiated in 1968 at MIT Eric´ Gourgoulhon (LUTH) GR computations with SageManifolds NewCompStar, Coimbra, 6 Sept. -
A Survey of User Interfaces for Computer Algebra Systems
J. Symbolic Computation (1998) 25, 127–159 A Survey of User Interfaces for Computer Algebra Systems NORBERT KAJLER† AND NEIL SOIFFER‡§ †Ecole des Mines de Paris, 60 Bd. St-Michel, 75006 Paris, France ‡Wolfram Research, Inc., 100 Trade Center Drive, Champaign, IL 61820, U.S.A. This paper surveys work within the Computer Algebra community (and elsewhere) di- rected towards improving user interfaces for scientific computation during the period 1963–1994. It is intended to be useful to two groups of people: those who wish to know what work has been done and those who would like to do work in the field. It contains an extensive bibliography to assist readers in exploring the field in more depth. Work related to improving human interaction with computer algebra systems is the main focus of the paper. However, the paper includes additional materials on some closely related issues such as structured document editing, graphics, and communication protocols. c 1998 Academic Press Limited 1. Introduction There are several problems with current computer algebra systems (CASs) that are interface-related. These problems include: the use of an unnatural linear notation to enter and edit expressions, the inherent difficulty of selecting and modifying subexpressions with commands, and the display of large expressions that run off the screen. These problems may intimidate novice users and frustrate experienced users. The more natural and intuitive the interface (the closer it corresponds to pencil and paper manipulations), the more likely it is that people will want to take advantage of the CAS for its ability to do tedious computations and to verify derivations. -
Computer Algebra and Operators
Computer Algebra and Operators Richard Fat eman* The University of California, Berkeley Robert Grossmant University of Illinois at Chicago January, 1989 (NASB-CR-185396) COMPUWB ALGEBRA BID N89-26625 OPERATORS [California Univ.) 14 p CSCL 12A Unclas G3/64 0217702 'supported in part by NSF grant number CCR-8812843, DARPA order #4871, contract N00039-84-C-0089, the IBM Corporation; the State of California MICRO program. 'supported in part by grant NAG2-513 from the NASA-Ames Research Center. 1 . .- 1 The Symbolic Computation of Operators After defining the twc ...pansions i=l a computer algebr:. -+cm such as MACSYMA or Maple will quickly com- pu te xp(A)exp(B)) = A + B + O(N + 1). 2 ORIGINAL PAGE IS OF POOR QUACITY Here O(N + 1) denotes terms containing a product of N + 1 or more A’s and/or B’s. This computation depends crucially upon the fact that AB = BA; for o’bjects for which this not true, certain correction terms enter. For example, if A and B are matrices, then in general AB # BA and 1 1 log(exp(A)exp(B)) = A + B - -AB + 5BA + . 2 The symbolic computation of operator expansions in a number of ways from the symbolic computation of expressions in com- muting variables. The papers in this volume consider various aspects of such computations. In this introduction, we first discuss some of the capabilities that prove useful when performing computer algebra computations involving operators. These capabilities may be broadly divided into three areas: the algebraic manipulation of expressions from the algebra generated by opera- tors; the algebraic manipulation of the actions of the operators upon other mathematical objects; and the development of appropriate normal forms and simplification algorithms for operators and their actions. -
Introduction Computer Algebra Systems
Introduction Reading Problems Computer Algebra Systems Computer Algebra Systems (CAS) are software packages used in the manipulation of math- ematical formulae in order to automate tedious and sometimes difficult algebraic tasks. The principal difference between a CAS and a traditional calculator is the ability of the CAS to deal with equations symbolically rather than numerically. Specific uses and capabilities of CAS vary greatly from one system to another, yet the purpose remains the same: ma- nipulation of symbolic equations. In addition to performing mathematical operations, CAS often include facilities for graphing equations and programming capabilities for user-defined procedures. Computer Algebra Systems were originally conceived in the early 1970’s by researchers work- ing in the area of artificial intelligence. The first popular systems were Reduce, Derive and Macsyma. Commercial versions of these programs are still available. The two most commercially successful CAS programs are Maple and Mathematica. Both programs have a rich set of routines for performing a wide range of problems found in engineering applications associated with research and teaching. Several other software packages, such as MathCAD and MATLAB include a Maple kernal for performing symbolic-based calcu- lation. In addition to these popular commercial CAS tools, a host of other less popular or more focused software tools are availalbe, including Axiom and MuPAD. The following is a brief overview of the origins of several of these popular CAS tools along with a summary of capabilities and availability. Axiom Axiom is a powerful computer algebra package that was originally developed as a research tool by Richard Jenks’ Computer Mathematics Group at the IBM Research Laboratory in New York. -
An Application of the Hermite Normal Form in Integer Programming
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector An Application of the Hermite Normal Form in Integer Programming Ming S. Hung Graduate School of Management Kent State University Kent, Ohio 44242 and Walter 0. Rom School of Business Administration Cleveland State University Cleveland, Ohio 44115 Submitted by Richard A. Brualdi ABSTRACT A new algorithm for solving integer programming problems is developed. It is based on cuts which are generated from the Hermite normal form of the basis matrix at a continuous extreme point. INTRODUCTION We present a new cutting plane algorithm for solving an integer program. The cuts are derived from a modified version of the usual column tableau used in integer programming: Rather than doing a complete elimination on the rows corresponding to the nonbasic variables, these rows are transformed into the Her-mite normal form. There are two major advantages with our cutting plane algorithm: First, it uses all integer operations in the computa- tions. Second, the cuts are deep, since they expose vertices of the integer hull. The Her-mite normal form (HNF), along with the basic properties of the cuts, is developed in Section I. Sections II and III apply the HNF to systems of equations and integer programs. A proof of convergence for the algorithm is presented in Section IV. Section V discusses some preliminary computa- tional experience. LINEAR ALGEBRA AND ITS APPLlCATlONS 140:163-179 (1990) 163 0 Else&r Science Publishing Co., Inc., 1990 655 Avenue of the Americas, New York, NY 10010 0024-3795/90/$3.50 164 MING S.