New Method for Bounding the Roots of a Univariate Polynomial
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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. -
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. -
Using Computer Programming As an Effective Complement To
Using Computer Programming as an Effective Complement to Mathematics Education: Experimenting with the Standards for Mathematics Practice in a Multidisciplinary Environment for Teaching and Learning with Technology in the 21st Century By Pavel Solin1 and Eugenio Roanes-Lozano2 1University of Nevada, Reno, 1664 N Virginia St, Reno, NV 89557, USA. Founder and Director of NCLab (http://nclab.com). 2Instituto de Matemática Interdisciplinar & Departamento de Didáctica de las Ciencias Experimentales, Sociales y Matemáticas, Facultad de Educación, Universidad Complutense de Madrid, c/ Rector Royo Villanova s/n, 28040 – Madrid, Spain. [email protected], [email protected] Received: 30 September 2018 Revised: 12 February 2019 DOI: 10.1564/tme_v27.3.03 Many mathematics educators are not aware of a strong 2. KAREL THE ROBOT connection that exists between the education of computer programming and mathematics. The reason may be that they Karel the Robot is a widely used educational have not been exposed to computer programming. This programming language which was introduced by Richard E. connection is worth exploring, given the current trends of Pattis in his 1981 textbook Karel the Robot: A Gentle automation and Industry 4.0. Therefore, in this paper we Introduction to the Art of Computer Programming (Pattis, take a closer look at the Common Core's eight Mathematical 1995). Let us note that Karel the Robot constitutes an Practice Standards. We show how each one of them can be environment related to Turtle Geometry (Abbelson and reinforced through computer programming. The following diSessa, 1981), but is not yet another implementation, as will discussion is virtually independent of the choice of a be detailed below. -
New Directions in Symbolic Computer Algebra 3.5 Year Doctoral Research Student Position Leading to a Phd in Maths Supervisor: Dr
Cadabra: new directions in symbolic computer algebra 3:5 year doctoral research student position leading to a PhD in Maths supervisor: Dr. Kasper Peeters, [email protected] Project description The Cadabra software is a new approach to symbolic computer algebra. Originally developed to solve problems in high-energy particle and gravitational physics, it is now attracting increas- ing attention from other disciplines which deal with aspects of tensor field theory. The system was written from the ground up with the intention of exploring new ideas in computer alge- bra, in order to help areas of applied mathematics for which suitable tools have so far been inadequate. The system is open source, built on a C++ graph-manipulating core accessible through a Python layer in a notebook interface, and in addition makes use of other open maths libraries such as SymPy, xPerm, LiE and Maxima. More information and links to papers using the software can be found at http://cadabra.science. With increasing attention and interest from disciplines such as earth sciences, machine learning and condensed matter physics, we are now looking for an enthusiastic student who is interested in working on this highly multi-disciplinary computer algebra project. The aim is to enlarge the capabilities of the system and bring it to an ever widening user base. Your task will be to do research on new algorithms and take responsibility for concrete implemen- tations, based on the user feedback which we have collected over the last few years. There are several possibilities to collaborate with people in some of the fields listed above, either in the Department of Mathematical Sciences or outside of it. -
Sage? History Community Some Useful Features
What is Sage? History Community Some useful features Sage : mathematics software based on Python Sébastien Labbé [email protected] Franco Saliola [email protected] Département de Mathématiques, UQÀM 29 novembre 2010 What is Sage? History Community Some useful features Outline 1 What is Sage? 2 History 3 Community 4 Some useful features What is Sage? History Community Some useful features What is Sage? What is Sage? History Community Some useful features Sage is . a distribution of software What is Sage? History Community Some useful features Sage is a distribution of software When you install Sage, you get: ATLAS Automatically Tuned Linear Algebra Software BLAS Basic Fortan 77 linear algebra routines Bzip2 High-quality data compressor Cddlib Double Description Method of Motzkin Common Lisp Multi-paradigm and general-purpose programming lang. CVXOPT Convex optimization, linear programming, least squares Cython C-Extensions for Python F2c Converts Fortran 77 to C code Flint Fast Library for Number Theory FpLLL Euclidian lattice reduction FreeType A Free, High-Quality, and Portable Font Engine What is Sage? History Community Some useful features Sage is a distribution of software When you install Sage, you get: G95 Open source Fortran 95 compiler GAP Groups, Algorithms, Programming GD Dynamic graphics generation tool Genus2reduction Curve data computation Gfan Gröbner fans and tropical varieties Givaro C++ library for arithmetic and algebra GMP GNU Multiple Precision Arithmetic Library GMP-ECM Elliptic Curve Method for Integer Factorization -
Addition and Subtraction
A Addition and Subtraction SUMMARY (475– 221 BCE), when arithmetic operations were per- formed by manipulating rods on a flat surface that was Addition and subtraction can be thought of as a pro- partitioned by vertical and horizontal lines. The num- cess of accumulation. For example, if a flock of 3 sheep bers were represented by a positional base- 10 system. is joined with a flock of 4 sheep, the combined flock Some scholars believe that this system— after moving will have 7 sheep. Thus, 7 is the sum that results from westward through India and the Islamic Empire— the addition of the numbers 3 and 4. This can be writ- became the modern system of representing numbers. ten as 3 + 4 = 7 where the sign “+” is read “plus” and The Greeks in the fifth century BCE, in addition the sign “=” is read “equals.” Both 3 and 4 are called to using a complex ciphered system for representing addends. Addition is commutative; that is, the order numbers, used a system that is very similar to Roman of the addends is irrelevant to how the sum is formed. numerals. It is possible that the Greeks performed Subtraction finds the remainder after a quantity is arithmetic operations by manipulating small stones diminished by a certain amount. If from a flock con- on a large, flat surface partitioned by lines. A simi- taining 5 sheep, 3 sheep are removed, then 2 sheep lar stone tablet was found on the island of Salamis remain. In this example, 5 is the minuend, 3 is the in the 1800s and is believed to date from the fourth subtrahend, and 2 is the remainder or difference. -
A Simplified Introduction to Virus Propagation Using Maple's Turtle Graphics Package
E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package A simplified introduction to virus propagation using Maple's Turtle Graphics package Eugenio Roanes-Lozano Instituto de Matemática Interdisciplinar & Departamento de Didáctica de las Ciencias Experimentales, Sociales y Matemáticas Facultad de Educación, Universidad Complutense de Madrid, Spain Carmen Solano-Macías Departamento de Información y Comunicación Facultad de CC. de la Documentación y Comunicación, Universidad de Extremadura, Spain Eugenio Roanes-Macías Departamento de Álgebra, Universidad Complutense de Madrid, Spain [email protected] ; [email protected] ; [email protected] Partially funded by the research project PGC2018-096509-B-100 (Government of Spain) 1 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package 1. INTRODUCTION: TURTLE GEOMETRY AND LOGO • Logo language: developed at the end of the ‘60s • Characterized by the use of Turtle Geometry (a.k.a. as Turtle Graphics). • Oriented to introduce kids to programming (Papert, 1980). • Basic movements of the turtle (graphic cursor): FD, BK RT, LT. • It is not based on a Cartesian Coordinate system. 2 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package • Initially robots were used to plot the trail of the turtle. http://cyberneticzoo.com/cyberneticanimals/1969-the-logo-turtle-seymour-papert-marvin-minsky-et-al-american/ 3 E. Roanes-Lozano, C. Solano-Macías & E. Roanes-Macías.: A simplified introduction to virus propagation using Maple's Turtle Graphics package • IBM Logo / LCSI Logo (’80) 4 E. -
Using Xcas in Calculus Curricula: a Plan of Lectures and Laboratory Projects
Computational and Applied Mathematics Journal 2015; 1(3): 131-138 Published online April 30, 2015 (http://www.aascit.org/journal/camj) Using Xcas in Calculus Curricula: a Plan of Lectures and Laboratory Projects George E. Halkos, Kyriaki D. Tsilika Laboratory of Operations Research, Department of Economics, University of Thessaly, Volos, Greece Email address [email protected] (K. D. Tsilika) Citation George E. Halkos, Kyriaki D. Tsilika. Using Xcas in Calculus Curricula: a Plan of Lectures and Keywords Laboratory Projects. Computational and Applied Mathematics Journal. Symbolic Computations, Vol. 1, No. 3, 2015, pp. 131-138. Computer-Based Education, Abstract Xcas Computer Software We introduce a topic in the intersection of symbolic mathematics and computation, concerning topics in multivariable Optimization and Dynamic Analysis. Our computational approach gives emphasis to mathematical methodology and aims at both symbolic and numerical results as implemented by a powerful digital mathematical tool, CAS software Received: March 31, 2015 Xcas. This work could be used as guidance to develop course contents in advanced Revised: April 20, 2015 calculus curricula, to conduct individual or collaborative projects for programming related Accepted: April 21, 2015 objectives, as Xcas is freely available to users and institutions. Furthermore, it could assist educators to reproduce calculus methodologies by generating automatically, in one entry, abstract calculus formulations. 1. Introduction Educational institutions are equipped with computer labs while modern teaching methods give emphasis in computer based learning, with curricula that include courses supported by the appropriate computer software. It has also been established that, software tools integrate successfully into Mathematics education and are considered essential in teaching Geometry, Statistics or Calculus (see indicatively [1], [2]). -
2.10. Sympy : Symbolic Mathematics in Python
2.10. Sympy : Symbolic Mathematics in Python author: Fabian Pedregosa Objectives 1. Evaluate expressions with arbitrary precision. 2. Perform algebraic manipulations on symbolic expressions. 3. Perform basic calculus tasks (limits, differentiation and integration) with symbolic expressions. 4. Solve polynomial and transcendental equations. 5. Solve some differential equations. What is SymPy? SymPy is a Python library for symbolic mathematics. It aims become a full featured computer algebra system that can compete directly with commercial alternatives (Mathematica, Maple) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries. Sympy documentation and packages for installation can be found on http://sympy.org/ Chapters contents First Steps with SymPy Using SymPy as a calculator Exercises Symbols Algebraic manipulations Expand Simplify Exercises Calculus Limits Differentiation Series expansion Exercises Integration Exercises Equation solving Exercises Linear Algebra Matrices Differential Equations Exercises 2.10.1. First Steps with SymPy 2.10.1.1. Using SymPy as a calculator SymPy defines three numerical types: Real, Rational and Integer. The Rational class represents a rational number as a pair of two Integers: the numerator and the denomi‐ nator, so Rational(1,2) represents 1/2, Rational(5,2) 5/2 and so on: >>> from sympy import * >>> >>> a = Rational(1,2) >>> a 1/2 >>> a*2 1 SymPy uses mpmath in the background, which makes it possible to perform computations using arbitrary- precision arithmetic. That way, some special constants, like e, pi, oo (Infinity), are treated as symbols and can be evaluated with arbitrary precision: >>> pi**2 >>> pi**2 >>> pi.evalf() 3.14159265358979 >>> (pi+exp(1)).evalf() 5.85987448204884 as you see, evalf evaluates the expression to a floating-point number. -
Symbolic Statistics with Sympy
PROC. OF THE 9th PYTHON IN SCIENCE CONF. (SCIPY 2010) 1 Symbolic Statistics with SymPy Matthew Rocklin F Abstract—We add a random variable type to a mathematical modeling lan- guage. We demonstrate through examples how this is a highly separable way v to introduce uncertainty and produce and query stochastic models. We motivate g the use of symbolics and thin compilers in scientific computing. Index Terms—Symbolics, mathematical modeling, uncertainty, SymPy Introduction Mathematical modeling is important. Symbolic computer algebra systems are a nice way to simplify the modeling process. They allow us to clearly describe a problem at a high level without simultaneously specifying the method of solution. This allows us to develop general solutions and solve specific problems independently. Fig. 1: The trajectory of a cannon shot This enables a community to act with far greater efficiency. Uncertainty is important. Mathematical models are often flawed. The model itself may be overly simplified or the inputs >>>t= Symbol(’t’) # SymPy variable for time may not be completely known. It is important to understand >>>x= x0+v * cos(theta) * t the extent to which the results of a model can be believed. >>>y= y0+v * sin(theta) * t+g *t**2 To address these concerns it is important that we characterize >>> impact_time= solve(y- yf, t) >>> xf= x0+v * cos(theta) * impact_time the uncertainty in our inputs and understand how this causes >>> xf.evalf() # evaluate xf numerically uncertainty in our results. 65.5842 In this paper we present one solution to this problem. We # Plot x vs. y for t in (0, impact_time) can add uncertainty to symbolic systems by adding a random >>> plot(x, y, (t,0, impact_time)) variable type.