Sage 9.4 Reference Manual: Constants Release 9.4
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Introduction to IDL®
Introduction to IDL® Revised for Print March, 2016 ©2016 Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation. All rights reserved. ENVI and IDL are registered trademarks of Harris Corporation. All other marks are the property of their respective owners. This document is not subject to the controls of the International Traffic in Arms Regulations (ITAR) or the Export Administration Regulations (EAR). Contents 1 Introduction To IDL 5 1.1 Introduction . .5 1.1.1 What is ENVI? . .5 1.1.2 ENVI + IDL, ENVI, and IDL . .6 1.1.3 ENVI Resources . .6 1.1.4 Contacting Harris Geospatial Solutions . .6 1.1.5 Tutorials . .6 1.1.6 Training . .7 1.1.7 ENVI Support . .7 1.1.8 Contacting Technical Support . .7 1.1.9 Website . .7 1.1.10 IDL Newsgroup . .7 2 About This Course 9 2.1 Manual Organization . .9 2.1.1 Programming Style . .9 2.2 The Course Files . 11 2.2.1 Installing the Course Files . 11 2.3 Starting IDL . 11 2.3.1 Windows . 11 2.3.2 Max OS X . 11 2.3.3 Linux . 12 3 A Tour of IDL 13 3.1 Overview . 13 3.2 Scalars and Arrays . 13 3.3 Reading Data from Files . 15 3.4 Line Plots . 15 3.5 Surface Plots . 17 3.6 Contour Plots . 18 3.7 Displaying Images . 19 3.8 Exercises . 21 3.9 References . 21 4 IDL Basics 23 4.1 IDL Directory Structure . 23 4.2 The IDL Workbench . 24 4.3 Exploring the IDL Workbench . -
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Sage 9.4 Reference Manual: Finite Rings Release 9.4
Sage 9.4 Reference Manual: Finite Rings Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Finite Rings 1 1.1 Ring Z=nZ of integers modulo n ....................................1 1.2 Elements of Z=nZ ............................................ 15 2 Finite Fields 39 2.1 Finite Fields............................................... 39 2.2 Base Classes for Finite Fields...................................... 47 2.3 Base class for finite field elements.................................... 61 2.4 Homset for Finite Fields......................................... 69 2.5 Finite field morphisms.......................................... 71 3 Prime Fields 77 3.1 Finite Prime Fields............................................ 77 3.2 Finite field morphisms for prime fields................................. 79 4 Finite Fields Using Pari 81 4.1 Finite fields implemented via PARI’s FFELT type............................ 81 4.2 Finite field elements implemented via PARI’s FFELT type....................... 83 5 Finite Fields Using Givaro 89 5.1 Givaro Finite Field............................................ 89 5.2 Givaro Field Elements.......................................... 94 5.3 Finite field morphisms using Givaro................................... 102 6 Finite Fields of Characteristic 2 Using NTL 105 6.1 Finite Fields of Characteristic 2..................................... 105 6.2 Finite Fields of characteristic 2...................................... 107 7 Miscellaneous 113 7.1 Finite residue fields........................................... -
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 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. -
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. -
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. -
SNC: a Cloud Service Platform for Symbolic-Numeric Computation Using Just-In-Time Compilation
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TCC.2017.2656088, IEEE Transactions on Cloud Computing IEEE TRANSACTIONS ON CLOUD COMPUTING 1 SNC: A Cloud Service Platform for Symbolic-Numeric Computation using Just-In-Time Compilation Peng Zhang1, Member, IEEE, Yueming Liu1, and Meikang Qiu, Senior Member, IEEE and SQL. Other types of Cloud services include: Software as a Abstract— Cloud services have been widely employed in IT Service (SaaS) in which the software is licensed and hosted in industry and scientific research. By using Cloud services users can Clouds; and Database as a Service (DBaaS) in which managed move computing tasks and data away from local computers to database services are hosted in Clouds. While enjoying these remote datacenters. By accessing Internet-based services over lightweight and mobile devices, users deploy diversified Cloud great services, we must face the challenges raised up by these applications on powerful machines. The key drivers towards this unexploited opportunities within a Cloud environment. paradigm for the scientific computing field include the substantial Complex scientific computing applications are being widely computing capacity, on-demand provisioning and cross-platform interoperability. To fully harness the Cloud services for scientific studied within the emerging Cloud-based services environment computing, however, we need to design an application-specific [4-12]. Traditionally, the focus is given to the parallel scientific platform to help the users efficiently migrate their applications. In HPC (high performance computing) applications [6, 12, 13], this, we propose a Cloud service platform for symbolic-numeric where substantial effort has been given to the integration of computation– SNC. -
Introduction to Ggplot2
Introduction to ggplot2 Dawn Koffman Office of Population Research Princeton University January 2014 1 Part 1: Concepts and Terminology 2 R Package: ggplot2 Used to produce statistical graphics, author = Hadley Wickham "attempt to take the good things about base and lattice graphics and improve on them with a strong, underlying model " based on The Grammar of Graphics by Leland Wilkinson, 2005 "... describes the meaning of what we do when we construct statistical graphics ... More than a taxonomy ... Computational system based on the underlying mathematics of representing statistical functions of data." - does not limit developer to a set of pre-specified graphics adds some concepts to grammar which allow it to work well with R 3 qplot() ggplot2 provides two ways to produce plot objects: qplot() # quick plot – not covered in this workshop uses some concepts of The Grammar of Graphics, but doesn’t provide full capability and designed to be very similar to plot() and simple to use may make it easy to produce basic graphs but may delay understanding philosophy of ggplot2 ggplot() # grammar of graphics plot – focus of this workshop provides fuller implementation of The Grammar of Graphics may have steeper learning curve but allows much more flexibility when building graphs 4 Grammar Defines Components of Graphics data: in ggplot2, data must be stored as an R data frame coordinate system: describes 2-D space that data is projected onto - for example, Cartesian coordinates, polar coordinates, map projections, ... geoms: describe type of geometric objects that represent data - for example, points, lines, polygons, ... aesthetics: describe visual characteristics that represent data - for example, position, size, color, shape, transparency, fill scales: for each aesthetic, describe how visual characteristic is converted to display values - for example, log scales, color scales, size scales, shape scales, .. -
01 Introduction Lab.Pdf
AN INTRODUCTION TO R DEEPAYAN SARKAR Introduction and examples What is R?. R provides an environment in which you can perform statistical analysis and produce graphics. It is actually a complete programming language, although that is only marginally described in this book. |Peter Dalgaard, \Introductory Statistics with R", 2002 R can be viewed as a programming language that happens to come with a large library of pre-defined functions that can be used to perform various tasks. A major focus of these pre-defined functions is statistical data analysis, and these allow R to be used purely as a toolbox for standard statistical techniques. However, some knowledge of R programming is essential to use it well at any level. For advanced users in particular, the main appeal of R (as opposed to other data analysis software) is as a programming environment suited to data analysis. Our goal will be to learn R as a statistics toolbox, but with a fairly strong emphasis on its programming language aspects. In this tutorial, we will do some elementary statistics, learn to use the documentation system, and learn about common data structures and programming features in R. Some follow-up tutorials are available for self-study at http://www.isid.ac.in/~deepayan/R-tutorials/. For more resources, see the R Project homepage http://www.r-project.org, which links to various manuals and other user-contributed documentation. A list of books related to R is available at http://www.r-project.org/doc/bib/R-jabref.html. An excellent introductory book is Peter Dalgaard, \Introductory Statistics with R", Springer (2002). -
Performance Analysis of Effective Symbolic Methods for Solving Band Matrix Slaes
EPJ Web of Conferences 214, 05004 (2019) https://doi.org/10.1051/epjconf/201921405004 CHEP 2018 Performance Analysis of Effective Symbolic Methods for Solving Band Matrix SLAEs 1, 1, Milena Veneva ∗ and Alexander Ayriyan ∗∗ 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 imple- mentations of three symbolic algorithms for solving band matrix systems of linear algebraic equations with heptadiagonal, pentadiagonal, 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 engi- neering problems, 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 characteristics 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. -
Sage: Unifying Mathematical Software for Scientists, Engineers, and Mathematicians
Sage: Unifying Mathematical Software for Scientists, Engineers, and Mathematicians 1 Introduction The goal of this proposal is to further the development of Sage, which is comprehensive unified open source software for mathematical and scientific computing that builds on high-quality mainstream methodologies and tools. Sage [12] uses Python, one of the world's most popular general-purpose interpreted programming languages, to create a system that scales to modern interdisciplinary prob- lems. Sage is also Internet friendly, featuring a web-based interface (see http://www.sagenb.org) that can be used from any computer with a web browser (PC's, Macs, iPhones, Android cell phones, etc.), and has sophisticated interfaces to nearly all other mathematics software, includ- ing the commercial programs Mathematica, Maple, MATLAB and Magma. Sage is not tied to any particular commercial mathematics software platform, is open source, and is completely free. With sufficient new work, Sage has the potential to have a transformative impact on the compu- tational sciences, by helping to set a high standard for reproducible computational research and peer reviewed publication of code. Our vision is that Sage will have a broad impact by supporting cutting edge research in a wide range of areas of computational mathematics, ranging from applied numerical computation to the most abstract realms of number theory. Already, Sage combines several hundred thousand lines of new code with over 5 million lines of code from other projects. Thousands of researchers use Sage in their work to find new conjectures and results (see [13] for a list of over 75 publications that use Sage).