The Origins of Sagemath – Creating a Viable Open Source Alternative to Magma, Maple, Mathematica, and Matlab
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Sagemath and Sagemathcloud
Viviane Pons Ma^ıtrede conf´erence,Universit´eParis-Sud Orsay [email protected] { @PyViv SageMath and SageMathCloud Introduction SageMath SageMath is a free open source mathematics software I Created in 2005 by William Stein. I http://www.sagemath.org/ I Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 2 / 7 SageMath Source and language I the main language of Sage is python (but there are many other source languages: cython, C, C++, fortran) I the source is distributed under the GPL licence. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 3 / 7 SageMath Sage and libraries One of the original purpose of Sage was to put together the many existent open source mathematics software programs: Atlas, GAP, GMP, Linbox, Maxima, MPFR, PARI/GP, NetworkX, NTL, Numpy/Scipy, Singular, Symmetrica,... Sage is all-inclusive: it installs all those libraries and gives you a common python-based interface to work on them. On top of it is the python / cython Sage library it-self. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 4 / 7 SageMath Sage and libraries I You can use a library explicitly: sage: n = gap(20062006) sage: type(n) <c l a s s 'sage. interfaces .gap.GapElement'> sage: n.Factors() [ 2, 17, 59, 73, 137 ] I But also, many of Sage computation are done through those libraries without necessarily telling you: sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]]) sage : G . g a p () Group( [ (3,4), (1,2,3)(4,5) ] ) Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 5 / 7 SageMath Development model Development model I Sage is developed by researchers for researchers: the original philosophy is to develop what you need for your research and share it with the community. -
A Fast Dynamic Language for Technical Computing
Julia A Fast Dynamic Language for Technical Computing Created by: Jeff Bezanson, Stefan Karpinski, Viral B. Shah & Alan Edelman A Fractured Community Technical work gets done in many different languages ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... Different optimal choices for different tasks ‣ statistics ➞ R ‣ linear algebra ➞ Matlab ‣ string processing ➞ Perl ‣ general programming ➞ Python, Java ‣ performance, control ➞ C, C++, Fortran Larger projects commonly use a mixture of 2, 3, 4, ... One Language We are not trying to replace any of these ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... What we are trying to do: ‣ allow developing complete technical projects in a single language without sacrificing productivity or performance This does not mean not using components in other languages! ‣ Julia uses C, C++ and Fortran libraries extensively “Because We Are Greedy.” “We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy.” Collapsing Dichotomies Many of these are just a matter of design and focus ‣ stats vs. linear algebra vs. strings vs. -
WEEK 10: FAREWELL to PYTHON... WELCOME to MAPLE! 1. a Review of PYTHON's Features During the Past Few Weeks, We Have Explored
WEEK 10: FAREWELL TO PYTHON... WELCOME TO MAPLE! 1. A review of PYTHON’s features During the past few weeks, we have explored the following aspects of PYTHON: • Built-in types: integer, long integer, float, complex, boolean, list, se- quence, string etc. • Operations on built-in types: +, −, ∗, abs, len, append, etc. • Loops: N = 1000 i=1 q = [] while i <= N q.append(i*i) i += 1 • Conditional statements: if x < y: min = x else: min = y • Functions: def fac(n): if n==0 then: return 1 else: return n*fac(n-1) • Modules: math, turtle, etc. • Classes: Rational, Vector, Polynomial, Polygon, etc. With these features, PYTHON is a powerful tool for doing mathematics on a computer. In particular, the use of modules makes it straightforward to add greater functionality, e.g. GL (3D graphics), NumPy (numerical algorithms). 2. What about MAPLE? MAPLE is really designed to be an interactive tool. Nevertheless, it can also be used as a programming language. It has some of the basic facilities available in PYTHON: Loops; conditional statements; functions (in the form of “procedures”); good graphics and some very useful additional modules called “packages”: • Built-in types: long integer, float (arbitrary precision), complex, rational, boolean, array, sequence, string etc. • Operations on built-in types: +, −, ∗, mathematical functions, etc. • Loops: 1 2 WEEK 10: FAREWELL TO PYTHON... WELCOME TO MAPLE! Table 1. Some of the most useful MAPLE packages Name Description Example DEtools Tools for differential equations exactsol(ode,y) linalg Linear Algebra gausselim(A) plots Graphics package polygonplot(p,axes=none) stats Statistics package random[uniform](10) N := 1000 : q := array(1..N) : for i from 1 to N do q[i] := i*i : od : • Conditional statements: if x < y then min := x else min := y fi: • Functions: fac := proc(n) if n=0 then return 1 else return n*fac(n-1) fi : end proc : • Packages: See Table 1. -
Rapid Research with Computer Algebra Systems
doi: 10.21495/71-0-109 25th International Conference ENGINEERING MECHANICS 2019 Svratka, Czech Republic, 13 – 16 May 2019 RAPID RESEARCH WITH COMPUTER ALGEBRA SYSTEMS C. Fischer* Abstract: Computer algebra systems (CAS) are gaining popularity not only among young students and schol- ars but also as a tool for serious work. These highly complicated software systems, which used to just be regarded as toys for computer enthusiasts, have reached maturity. Nowadays such systems are available on a variety of computer platforms, starting from freely-available on-line services up to complex and expensive software packages. The aim of this review paper is to show some selected capabilities of CAS and point out some problems with their usage from the point of view of 25 years of experience. Keywords: Computer algebra system, Methodology, Wolfram Mathematica 1. Introduction The Wikipedia page (Wikipedia contributors, 2019a) defines CAS as a package comprising a set of algo- rithms for performing symbolic manipulations on algebraic objects, a language to implement them, and an environment in which to use the language. There are 35 different systems listed on the page, four of them discontinued. The oldest one, Reduce, was publicly released in 1968 (Hearn, 2005) and is still available as an open-source project. Maple (2019a) is among the most popular CAS. It was first publicly released in 1984 (Maple, 2019b) and is still popular, also among users in the Czech Republic. PTC Mathcad (2019) was published in 1986 in DOS as an engineering calculation solution, and gained popularity for its ability to work with typeset mathematical notation in combination with automatic computations. -
Running Sagemath (With Or Without Installation)
Running SageMath (with or without installation) http://www.sagemath.org/ Éric Gourgoulhon Running SageMath 9 Feb. 2017 1 / 5 Various ways to install/access SageMath 7.5.1 Install on your computer: 2 options: install a compiled binary version for Linux, MacOS X or Windows1 from http://www.sagemath.org/download.html compile from source (Linux, MacOS X): check the prerequisites (see here for Ubuntu) and run git clone git://github.com/sagemath/sage.git cd sage MAKE=’make -j8’ make Run on your computer without installation: Sage Debian Live http://sagedebianlive.metelu.net/ Bootable USB flash drive with SageMath (boosted with octave, scilab), Geogebra, LaTeX, gimp, vlc, LibreOffice,... Open a (free) account on SageMathCloud https://cloud.sagemath.com/ Run in SageMathCell Single cell mode: http://sagecell.sagemath.org/ 1requires VirtualBox; alternatively, a full Windows installer is in pre-release stage at https://github.com/embray/sage-windows/releases Éric Gourgoulhon Running SageMath 9 Feb. 2017 2 / 5 Example 1: installing on Ubuntu 16.04 1 Download the archive sage-7.5.1-Ubuntu_16.04-x86_64.tar.bz2 from one the mirrors listed at http://www.sagemath.org/download-linux.html 2 Run the following commands in a terminal: bunzip2 sage-7.5.1-Ubuntu_16.04-x86_64.tar.bz2 tar xvf sage-7.5.1-Ubuntu_16.04-x86_64.tar cd SageMath ./sage -n jupyter A Jupyter home page should then open in your browser. Click on New and select SageMath 7.5.1 to open a Jupyter notebook with a SageMath kernel. Éric Gourgoulhon Running SageMath 9 Feb. 2017 3 / 5 Example 2: using the SageMathCloud 1 Open a free account on https://cloud.sagemath.com/ 2 Create a new project 3 In the second top menu, click on New to create a new file 4 Select Jupyter Notebook for the file type 5 In the Jupyter menu, click on Kernel, then Change kernel and choose SageMath 7.5 Éric Gourgoulhon Running SageMath 9 Feb. -
Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models
Practical Estimation of High Dimensional Stochastic Differential Mixed-Effects Models Umberto Picchinia,b,∗, Susanne Ditlevsenb aDepartment of Mathematical Sciences, University of Durham, South Road, DH1 3LE Durham, England. Phone: +44 (0)191 334 4164; Fax: +44 (0)191 334 3051 bDepartment of Mathematical Sciences, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark Abstract Stochastic differential equations (SDEs) are established tools to model physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE intrin- sic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects mod- elling allows for the distinction of between and within experiment variability. A framework to model dynamics within a population using SDEs is proposed, representing simultane- ously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics using SDEs. These stochastic differ- ential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models. Keywords: automatic differentiation, closed-form transition density expansion, maximum likelihood estimation, population estimation, stochastic differential equation, Cox-Ingersoll-Ross process arXiv:1004.3871v2 [stat.CO] 3 Oct 2010 1. INTRODUCTION Models defined through stochastic differential equations (SDEs) allow for the represen- tation of random variability in dynamical systems. -
Introduction to GNU Octave
Introduction to GNU Octave Hubert Selhofer, revised by Marcel Oliver updated to current Octave version by Thomas L. Scofield 2008/08/16 line 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 8 6 4 2 -8 -6 0 -4 -2 -2 0 -4 2 4 -6 6 8 -8 Contents 1 Basics 2 1.1 What is Octave? ........................... 2 1.2 Help! . 2 1.3 Input conventions . 3 1.4 Variables and standard operations . 3 2 Vector and matrix operations 4 2.1 Vectors . 4 2.2 Matrices . 4 1 2.3 Basic matrix arithmetic . 5 2.4 Element-wise operations . 5 2.5 Indexing and slicing . 6 2.6 Solving linear systems of equations . 7 2.7 Inverses, decompositions, eigenvalues . 7 2.8 Testing for zero elements . 8 3 Control structures 8 3.1 Functions . 8 3.2 Global variables . 9 3.3 Loops . 9 3.4 Branching . 9 3.5 Functions of functions . 10 3.6 Efficiency considerations . 10 3.7 Input and output . 11 4 Graphics 11 4.1 2D graphics . 11 4.2 3D graphics: . 12 4.3 Commands for 2D and 3D graphics . 13 5 Exercises 13 5.1 Linear algebra . 13 5.2 Timing . 14 5.3 Stability functions of BDF-integrators . 14 5.4 3D plot . 15 5.5 Hilbert matrix . 15 5.6 Least square fit of a straight line . 16 5.7 Trapezoidal rule . 16 1 Basics 1.1 What is Octave? Octave is an interactive programming language specifically suited for vectoriz- able numerical calculations. -
Books About Computing Tools
Books about Programming and Computing Tools Separate Section of: Books about Computing, Programming, Algorithms, and Software Development Collection of References edited by Stanislav Sýkora Permalink via DOI: 10.3247/SL6Refs16.001 Stan's LIBRARY and its Programming Section Extra Byte | Stan's HUB Free online texts Forward a missing book reference Site Plan & SEARCH This growing compilation includes titles yet to be released (they have a month specified in the release date). The entries are sorted by publication year and the first Author. Green-color titles indicate educational texts. You can download a PDF version of this document for off-line use. But keep coming back, the list is growing! Many of the books are available from Amazon. Entering Amazon from here helps this site at no cost to you. F Other Lists: Popular Science F Mathematics F Physics F Chemistry Visitor # Patents+IP F Electronics | DSP | Tinkering F Computing Spintronics F Materials ADVERTISE with us WWW issues F Instruments / Measurements Quantum Computing F NMR | ESR | MRI F Spectroscopy Extra Byte Hint: the F symbols above, where present, are links to free online texts (books, courses, theses, ...) Advance notices (years ≥ 2016) and, at page bottom, Related Works: Link Directories: SCIENCE | Edu+Fun 1. Garvan Frank, MATH | COMPUTING The Maple Book, PHYSICS | CHEMISTRY 2nd Edition, Chapman and Hall/CRC, February 2016. ISBN 978-1439898286. Hardcover >>. NMR-MRI-ESR-NQR 2. Green Dale, ELECTRONICS Procedural Content Generation for C++ Game Development, PATENTS+IP Packt Publishing, March 2016. Kindle >>. WWW stuff 3. Guido Sarah, Introduction to Machine Learning with Python, Other resources: O'Reilly Media, January 2016. -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
An Introduction to Maple
A little bit of help with Maple School of Mathematics and Applied Statistics University of Wollongong 2008 A little bit of help with Maple Welcome! The aim of this manual is to provide you with a little bit of help with Maple. Inside you will find brief descriptions on a lot of useful commands and packages that are commonly needed when using Maple. Full worked examples are presented to show you how to use the Maple commands, with different options given, where the aim is to teach you Maple by example – not by showing the full technical detail. If you want the full detail, you are encouraged to look at the Maple Help menu. To use this manual, a basic understanding of mathematics and how to use a computer is assumed. While this manual is based on Version 10.01 of Maple on the PC platform on Win- dows XP, most of it should be applicable to other versions, platforms and operating systems. This handbook is built upon the original version of this handbook by Maureen Edwards and Alex Antic. Further information has been gained from the Help menu in Maple itself. If you have any suggestions or comments, please email them to Grant Cox ([email protected]). Table of contents : Index 1/81 Contents 1 Introduction 4 1.1 What is Maple? ................................... 4 1.2 Getting started ................................... 4 1.3 Operators ...................................... 7 1.3.1 The Ditto Operator ............................. 9 1.4 Assign and unassign ................................ 9 1.5 Special constants .................................. 11 1.6 Evaluation ...................................... 11 1.6.1 Digits ................................... -
Some Effective Methods for Teaching Mathematics Courses in Technological Universities
International Journal of Education and Information Studies. ISSN 2277-3169 Volume 6, Number 1 (2016), pp. 11-18 © Research India Publications http://www.ripublication.com Some Effective Methods for Teaching Mathematics Courses in Technological Universities Dr. D. S. Sankar Professor, School of Applied Sciences and Mathematics, Universiti Teknologi Brunei, Jalan Tungku Link, BE1410, Brunei Darussalam E-mail: [email protected] Dr. Rama Rao Karri Principal Lecturer, Petroleum and Chemical Engineering Programme Area, Faculty of Engineering, Universiti Teknologi Brunei, Jalan Tungku Link, Gadong BE1410, Brunei Darussalam E-mail: [email protected] Abstract This article discusses some effective and useful methods for teaching various mathematics topics to the students of undergraduate and post-graduate degree programmes in technological universities. These teaching methods not only equip the students to acquire knowledge and skills for solving real world problems efficiently, but also these methods enhance the teacher’s ability to demonstrate the mathematical concepts effectively along with suitable physical examples. The exposure to mathematical softwares like MATLAB, SCILAB, MATHEMATICA, etc not only increases the students confidential level to solve variety of typical problems which they come across in their respective disciplines of study, but also it enables them to visualize the surfaces of the functions of several variable. Peer learning, seminar based learning and project based learning are other methods of learning environment to the students which makes the students to learn mathematics by themselves. These are higher level learning methods which enhances the students understanding on the mathematical concepts and it enables them to take up research projects. It is noted that the teaching and learning of mathematics with the support of mathematical softwares is believed to be more effective when compared to the effects of other methods of teaching and learning of mathematics. -
Gretl User's Guide
Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 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 (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases ..........................................