Experiments with MATLAB Cleve Moler October 4, 2011 Ii
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Developing Scientific Computing Software: Current Processes And
DEVELOPING: SGIENffl&Pifli|ii^Mp| CURRENT PROCESSES" WMWiiiiia DEVELOPING SCIENTIFIC COMPUTING SOFTWARE MASTER OF APPLIED SCIENCE(2008) McMaster University COMPUTING AND SOFTWARE Hamilton, Ontario TITLE: Developing Scientific Computing Software: Current Processes and Future Directions AUTHOR: Jin Tang, M.M. (Nanjing University) SUPERVISOR: Dr. Spencer Smith NUMBER OF PAGERS: xxii, 216 n Abstract Considerable emphasis in scientific computing (SC) software development has been placed on the software qualities of performance and correctness. How ever, other software qualities have received less attention, such as the qualities of usability, maintainability, testability and reusability. Presented in this work is a survey titled "Survey on Developing Scien tific Computing Software, which is apparently the first conducted to explore the current approaches to SC software development and to determine which qualities of SC software are in most need of improvement. From the survey. we found that systematic development process is frequently not adopted in the SC software community, since 58% of respondents mentioned that their entire development process potentially consists only of coding and debugging. Moreover, semi-formal and formal specification is rarely used when developing SC software, which is suggested by the fact that 70% of respondents indicate that they only use informal specification. In terms of the problems in SC software development, which are dis covered by analyzing the survey results, a solution is proposed to improve the quality of SC software by using SE methodologies, concretely, using a modified Parnas' Rational Design Process (PRDP) and the Unified Software Development Process (USDP). A comparison of the two candidate processes is provided to help SC software practitioners determine which of the two pro cesses fits their particular situation. -
The Guide to Available Mathematical Software Problem Classification System
The Guide to Available Mathematical Software Problem Classification System Ronald F. Boisvert, Sally E. Howe and David K. Kahaner November 1990 U.S. DEPARTMENT OF COMMERCE National Institute of Standards and Technology Gaithersburg, MD 20899 100 U56 //4475 1990 C.2 NATIONAL, INSrrnJTE OF STANDARDS & TECHNOLOGY / THE GUIDE TO AVAILABLE MATHEMATICAL SOFTWARE PROBLEM CLASSIFICATION SYSTEM Ronald F. Boisvert Sally E. Howe David K. Kahaner U.S. DEPARTMENT OF COMMERCE National InstHute of Standards and Technology Center for Computing and Applied Mathematics Gaithersburg, MO 20899 November 1990 U.S. DEPARTMENT OF COMMERCE Robert A. Mosbacher, Secretary NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY John W. Lyons, Director 2 Boisvert, Howe and Kahaner own manuals or on-line documentation system. In order to determine what software is avail- able to solve a particular problem, users must search through a very large, heterogeneous collection of information. This is a tedious and error-prone process. As a result, there has been much interest in the development of automated advisory systems to help users select software. Keyword search is a popular technique used for this purpose. In such a system keywords or phrases are assigned to each piece of software to succinctly define its purpose, and the set of aU such keywords axe entered into a database. Keyword-based selection systems query users for a set of keywords and then present a fist of software modules which contain them. A major difficulty with such systems is that users often have trouble in providing the appropriate keywords for a given mathematical or statistical problem. There is such a wealth of alternate mathematical and statistical terminology that it would be a rare occurrence for two separate knowledgeable persons to assign the same set of keywords to a given software module. -
Fractals.Pdf
Fractals Self Similarity and Fractal Geometry presented by Pauline Jepp 601.73 Biological Computing Overview History Initial Monsters Details Fractals in Nature Brownian Motion L-systems Fractals defined by linear algebra operators Non-linear fractals History Euclid's 5 postulates: 1. To draw a straight line from any point to any other. 2. To produce a finite straight line continuously in a straight line. 3. To describe a circle with any centre and distance. 4. That all right angles are equal to each other. 5. That, if a straight line falling on two straight lines make the interior angles on the same side less than two right angles, if produced indefinitely, meet on that side on which are the angles less than the two right angles. History Euclid ~ "formless" patterns Mandlebrot's Fractals "Pathological" "gallery of monsters" In 1875: Continuous non-differentiable functions, ie no tangent La Femme Au Miroir 1920 Leger, Fernand Initial Monsters 1878 Cantor's set 1890 Peano's space filling curves Initial Monsters 1906 Koch curve 1960 Sierpinski's triangle Details Fractals : are self similar fractal dimension A square may be broken into N^2 self-similar pieces, each with magnification factor N Details Effective dimension Mandlebrot: " ... a notion that should not be defined precisely. It is an intuitive and potent throwback to the Pythagoreans' archaic Greek geometry" How long is the coast of Britain? Steinhaus 1954, Richardson 1961 Brownian Motion Robert Brown 1827 Jean Perrin 1906 Diffusion-limited aggregation L-Systems and Fractal Growth -
The Conway Space-Filling
Symmetry: Art and Science Buenos Aires Congress, 2007 THE CONWAY SPACE-FILLING MICHAEL LONGUET-HIGGINS Name: Michael S. Longuet-Higgins, Mathematician and Oceanographer, (b. Lenham, England, 1925). Address: Institute for Nonlinear Science, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92037-0402, U.S.A. Email: [email protected] Fields of interest: Fluid dynamics, ocean waves and currents, geophysics, underwater sound, projective geometry, polyhedra, mathematical toys. Awards: Rayleigh Prize for Mathematics, Cambridge University, 1950; Hon.D. Tech., Technical University of Denmark, 1979; Hon. LL.D., University of Glasgow, Scotland, 1979; Fellow of the American Geophysical Union, 1981; Sverdrup Gold Medal of the American Meteorological Society, 1983; International Coastal Engineering Award of the American Society of Civil Engineers, 1984; Oceanography Award of the Society for Underwater Technology, 1990; Honorary Fellow of the Acoustical Society of America, 2002. Publications: “Uniform polyhedra” (with H.S.M. Coxeter and J.C.P. Miller (1954). Phil. Trans. R. Soc. Lond. A 246, 401-450. “Some Mathematical Toys” (film), (1963). British Association Meeting, Aberdeen, Scotland; “Clifford’s chain and its analogues, in relation to the higher polytopes,” (1972). Proc. R. Soc. Lond. A 330, 443-466. “Inversive properties of the plane n-line, and a symmetric figure of 2x5 points on a quadric,” (1976). J. Lond. Math. Soc. 12, 206-212; Part II (with C.F. Parry) (1979) J. Lond. Math. Soc. 19, 541-560; “Nested triacontahedral shells, or How to grow a quasi-crystal,” (2003) Math. Intelligencer 25, 25-43. Abstract: A remarkable new space-filling, with an unusual symmetry, was recently discovered by John H. -
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. -
Gene H. Golub 1932–2007
Gene H. Golub 1932–2007 A Biographical Memoir by Dianne P. O’Leary ©2018 National Academy of Sciences. Any opinions expressed in this memoir are those of the author and do not necessarily reflect the views of the National Academy of Sciences. GENE HOWARD GOLUB February 29, 1932–November 16, 2007 Elected to the NAS, 1993 Gene Howard Golub was the most influential person of his generation in the field of numerical analysis, in partic- ular, in the area of matrix computation. This was a conse- quence not just of his extraordinary technical contribu- tions but was also due to his clear writing, his influential treatise on matrix computation, his mentorship of a host of students and collaborators, his service as founder and editor of two journals, his leadership in the Society for Industrial and Applied Mathematics (SIAM), his efforts to bring colleagues together in meetings and electronic forums, and his role as a research catalyst. By Dianne P. O’Leary Biography Gene was born in Chicago, Illinois, on Leap Day, February 29, 1932, to Bernice Gelman Golub (an immigrant to the U.S. from Latvia) and Nathan Golub (from Ukraine). Gene’s father worked as a “bread man” and his mother was a seamstress, and Gene was proud of his Jewish heritage. From the age of 12 Gene worked, first at his cousin’s pharmacy and later at other jobs. After junior college and a year at the University of Chicago, he transferred to the University of Illinois in Urbana-Champaign, where he received his BS in Mathematics (1953), MA in Mathematical Statistics (1954), and PhD in Mathematics (1959). -
Object Oriented Programming
No. 52 March-A pril'1990 $3.95 T H E M TEe H CAL J 0 URN A L COPIA Object Oriented Programming First it was BASIC, then it was structures, now it's objects. C++ afi<;ionados feel, of course, that objects are so powerful, so encompassing that anything could be so defined. I hope they're not placing bets, because if they are, money's no object. C++ 2.0 page 8 An objective view of the newest C++. Training A Neural Network Now that you have a neural network what do you do with it? Part two of a fascinating series. Debugging C page 21 Pointers Using MEM Keep C fro111 (C)rashing your system. An AT Keyboard Interface Use an AT keyboard with your latest project. And More ... Understanding Logic Families EPROM Programming Speeding Up Your AT Keyboard ((CHAOS MADE TO ORDER~ Explore the Magnificent and Infinite World of Fractals with FRAC LS™ AN ELECTRONIC KALEIDOSCOPE OF NATURES GEOMETRYTM With FracTools, you can modify and play with any of the included images, or easily create new ones by marking a region in an existing image or entering the coordinates directly. Filter out areas of the display, change colors in any area, and animate the fractal to create gorgeous and mesmerizing images. Special effects include Strobe, Kaleidoscope, Stained Glass, Horizontal, Vertical and Diagonal Panning, and Mouse Movies. The most spectacular application is the creation of self-running Slide Shows. Include any PCX file from any of the popular "paint" programs. FracTools also includes a Slide Show Programming Language, to bring a higher degree of control to your shows. -
Using the GNU Compiler Collection (GCC)
Using the GNU Compiler Collection (GCC) Using the GNU Compiler Collection by Richard M. Stallman and the GCC Developer Community Last updated 23 May 2004 for GCC 3.4.6 For GCC Version 3.4.6 Published by: GNU Press Website: www.gnupress.org a division of the General: [email protected] Free Software Foundation Orders: [email protected] 59 Temple Place Suite 330 Tel 617-542-5942 Boston, MA 02111-1307 USA Fax 617-542-2652 Last printed October 2003 for GCC 3.3.1. Printed copies are available for $45 each. Copyright c 1988, 1989, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004 Free Software Foundation, Inc. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with the Invariant Sections being \GNU General Public License" and \Funding Free Software", the Front-Cover texts being (a) (see below), and with the Back-Cover Texts being (b) (see below). A copy of the license is included in the section entitled \GNU Free Documentation License". (a) The FSF's Front-Cover Text is: A GNU Manual (b) The FSF's Back-Cover Text is: You have freedom to copy and modify this GNU Manual, like GNU software. Copies published by the Free Software Foundation raise funds for GNU development. i Short Contents Introduction ...................................... 1 1 Programming Languages Supported by GCC ............ 3 2 Language Standards Supported by GCC ............... 5 3 GCC Command Options ......................... -
6.5 the Recursion Theorem
6.5. THE RECURSION THEOREM 417 6.5 The Recursion Theorem The recursion Theorem, due to Kleene, is a fundamental result in recursion theory. Theorem 6.5.1 (Recursion Theorem, Version 1 )Letϕ0,ϕ1,... be any ac- ceptable indexing of the partial recursive functions. For every total recursive function f, there is some n such that ϕn = ϕf(n). The recursion Theorem can be strengthened as follows. Theorem 6.5.2 (Recursion Theorem, Version 2 )Letϕ0,ϕ1,... be any ac- ceptable indexing of the partial recursive functions. There is a total recursive function h such that for all x ∈ N,ifϕx is total, then ϕϕx(h(x)) = ϕh(x). 418 CHAPTER 6. ELEMENTARY RECURSIVE FUNCTION THEORY A third version of the recursion Theorem is given below. Theorem 6.5.3 (Recursion Theorem, Version 3 ) For all n ≥ 1, there is a total recursive function h of n +1 arguments, such that for all x ∈ N,ifϕx is a total recursive function of n +1arguments, then ϕϕx(h(x,x1,...,xn),x1,...,xn) = ϕh(x,x1,...,xn), for all x1,...,xn ∈ N. As a first application of the recursion theorem, we can show that there is an index n such that ϕn is the constant function with output n. Loosely speaking, ϕn prints its own name. Let f be the recursive function such that f(x, y)=x for all x, y ∈ N. 6.5. THE RECURSION THEOREM 419 By the s-m-n Theorem, there is a recursive function g such that ϕg(x)(y)=f(x, y)=x for all x, y ∈ N. -
The Walled Gardens of Ebook Surveillance: a Brief Set of Arguments Against DRM in Libraries
City University of New York (CUNY) CUNY Academic Works Publications and Research CUNY Graduate Center 2015 The Walled Gardens of Ebook Surveillance: A Brief Set of Arguments Against DRM in Libraries Alycia Sellie Graduate Center, CUNY How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_pubs/162 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] The Walled Gardens of Ebook Surveillance: A Brief Set of Arguments Against DRM in Libraries There are three claims I will make in this article about including electronic books with DRM restrictions in library collections. These arguments center upon what the presence of restricted ebooks signifies to patrons about libraries. The first argument outlines how providing books with DRM encourages library users to adopt low expectations for how their personal information will be shared and collected. Second, when users encounter DRM within library collections, not only are they frustrated by the ways that these systems restrict their use of a text, but they become more broadly disappointed in their library. Finally, I will show how the current technological landscape that allows third party surveillance via DRM threatens the professional standing of librarians as protectors of patron information. What is DRM? To me, DRM stands for Digital Restrictions Management. But choosing to call it such is a political choice.1 Others refer to DRM as Digital Rights Management. In a nutshell, DRM controls access to digital content and restricts the functionality a devices, such as an ebook reader or computer. -
April 22 7.1 Recursion Theorem
CSE 431 Theory of Computation Spring 2014 Lecture 7: April 22 Lecturer: James R. Lee Scribe: Eric Lei Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. An interesting question about Turing machines is whether they can reproduce themselves. A Turing machine cannot be be defined in terms of itself, but can it still somehow print its own source code? The answer to this question is yes, as we will see in the recursion theorem. Afterward we will see some applications of this result. 7.1 Recursion Theorem Our end goal is to have a Turing machine that prints its own source code and operates on it. Last lecture we proved the existence of a Turing machine called SELF that ignores its input and prints its source code. We construct a similar proof for the recursion theorem. We will also need the following lemma proved last lecture. ∗ ∗ Lemma 7.1 There exists a computable function q :Σ ! Σ such that q(w) = hPwi, where Pw is a Turing machine that prints w and hats. Theorem 7.2 (Recursion theorem) Let T be a Turing machine that computes a function t :Σ∗ × Σ∗ ! Σ∗. There exists a Turing machine R that computes a function r :Σ∗ ! Σ∗, where for every w, r(w) = t(hRi; w): The theorem says that for an arbitrary computable function t, there is a Turing machine R that computes t on hRi and some input. Proof: We construct a Turing Machine R in three parts, A, B, and T , where T is given by the statement of the theorem. -
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.