Symbolic Computing
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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. -
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 -
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. -
New Method for Bounding the Roots of a Univariate Polynomial
New method for bounding the roots of a univariate polynomial Eric Biagioli, Luis Penaranda,˜ Roberto Imbuzeiro Oliveira IMPA – Instituto de Matemtica Pura e Aplicada Rio de Janeiro, Brazil w3.impa.br/∼{eric,luisp,rimfog Abstract—We present a new algorithm for computing upper by any other method. Our method works as follows: at the bounds for the maximum positive real root of a univariate beginning, an upper bound for the positive roots of the input polynomial. The algorithm improves complexity and accuracy polynomial is computed using one existing method (for exam- of current methods. These improvements do impact in the performance of methods for root isolation, which are the first step ple the First Lambda method). During the execution of this (and most expensive, in terms of computational effort) executed (already existent) method, our method also creates the killing by current methods for computing the real roots of a univariate graph associated to the solution produced by that method. It polynomial. We also validated our method experimentally. structure allows, after the method have been run, to analyze Keywords-upper bounds, positive roots, polynomial real root the produced output and improve it. isolation, polynomial real root bounding. Moreover, the complexity of our method is O(t + log2(d)), where t is the number of monomials the input polynomial has I. INTRODUCTION and d is its degree. Since the other methods are either linear Computing the real roots of a univariate polynomial p(x) is or quadratic in t, our method does not introduce a significant a central problem in algebra, with applications in many fields complexity overhead. -
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. -
SMT Solving in a Nutshell
SAT and SMT Solving in a Nutshell Erika Abrah´ am´ RWTH Aachen University, Germany LuFG Theory of Hybrid Systems February 27, 2020 Erika Abrah´ am´ - SAT and SMT solving 1 / 16 What is this talk about? Satisfiability problem The satisfiability problem is the problem of deciding whether a logical formula is satisfiable. We focus on the automated solution of the satisfiability problem for first-order logic over arithmetic theories, especially using SAT and SMT solving. Erika Abrah´ am´ - SAT and SMT solving 2 / 16 CAS SAT SMT (propositional logic) (SAT modulo theories) Enumeration Computer algebra DP (resolution) systems [Davis, Putnam’60] DPLL (propagation) [Davis,Putnam,Logemann,Loveland’62] Decision procedures NP-completeness [Cook’71] for combined theories CAD Conflict-directed [Shostak’79] [Nelson, Oppen’79] backjumping Partial CAD Virtual CDCL [GRASP’97] [zChaff’04] DPLL(T) substitution Watched literals Equalities and uninterpreted Clause learning/forgetting functions Variable ordering heuristics Bit-vectors Restarts Array theory Arithmetic Decision procedures for first-order logic over arithmetic theories in mathematical logic 1940 Computer architecture development 1960 1970 1980 2000 2010 Erika Abrah´ am´ - SAT and SMT solving 3 / 16 SAT SMT (propositional logic) (SAT modulo theories) Enumeration DP (resolution) [Davis, Putnam’60] DPLL (propagation) [Davis,Putnam,Logemann,Loveland’62] Decision procedures NP-completeness [Cook’71] for combined theories Conflict-directed [Shostak’79] [Nelson, Oppen’79] backjumping CDCL [GRASP’97] [zChaff’04] -
Highlighting Wxmaxima in Calculus
mathematics Article Not Another Computer Algebra System: Highlighting wxMaxima in Calculus Natanael Karjanto 1,* and Husty Serviana Husain 2 1 Department of Mathematics, University College, Natural Science Campus, Sungkyunkwan University Suwon 16419, Korea 2 Department of Mathematics Education, Faculty of Mathematics and Natural Science Education, Indonesia University of Education, Bandung 40154, Indonesia; [email protected] * Correspondence: [email protected] Abstract: This article introduces and explains a computer algebra system (CAS) wxMaxima for Calculus teaching and learning at the tertiary level. The didactic reasoning behind this approach is the need to implement an element of technology into classrooms to enhance students’ understanding of Calculus concepts. For many mathematics educators who have been using CAS, this material is of great interest, particularly for secondary teachers and university instructors who plan to introduce an alternative CAS into their classrooms. By highlighting both the strengths and limitations of the software, we hope that it will stimulate further debate not only among mathematics educators and software users but also also among symbolic computation and software developers. Keywords: computer algebra system; wxMaxima; Calculus; symbolic computation Citation: Karjanto, N.; Husain, H.S. 1. Introduction Not Another Computer Algebra A computer algebra system (CAS) is a program that can solve mathematical problems System: Highlighting wxMaxima in by rearranging formulas and finding a formula that solves the problem, as opposed to Calculus. Mathematics 2021, 9, 1317. just outputting the numerical value of the result. Maxima is a full-featured open-source https://doi.org/10.3390/ CAS: the software can serve as a calculator, provide analytical expressions, and perform math9121317 symbolic manipulations. -
Using Python Like Matlab and Mathematica
Using Python like Matlab and Mathematica Adam Watts This notebook is a beginning tutorial of how to use Python in a way very similar to Matlab and Mathematica using some of the Scientific Python libraries. This tutorial is using Python 2.6. The most convenient way to install everything is to use the Anaconda distribution: https://www.continuum.io/downloads ( https://www.continuum.io/downloads ). To run this notebook, click "Cell", then "Run All". This ensures that all cells are run in the correct sequential order, and the first command "%reset -f" is executed, which clears all variables. Not doing this can cause some headaches later on if you have stale variables floating around in memory. This is only necessary in the Python Notebook (i.e. Jupyter) environment. In [1]: # Clear memory and all variables % reset -f # Import libraries, call them something shorthand import numpy as np import matplotlib.pyplot as plt # Tell the compiler to put plots directly in the notebook % matplotlib inline Lists First I'll introduce Python lists, which are the built-in array type in Python. These are useful because you can append values to the end of them without having to specify where that value goes, and you don't even have to know how big the list will end up being. Lists can contain numbers, characters, strings, or even other lists. In [2]: # Make a list of integers list1 = [1,2,3,4,5] print list1 # Append a number to the end of the list list1.append(6) print list1 # Delete the first element of the list del list1[0] list1 [1, 2, 3, 4, 5] [1, 2, 3, 4, 5, 6] Out[2]: [2, 3, 4, 5, 6] Lists are useful, but problematic if you want to do any element-wise math.