Programming in Mathematica, a Problem-Centred Approach
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												  Math/CS 467 (Lesieutre) Homework 2 September 9, 2020 SubmissionMath/CS 467 (Lesieutre) Homework 2 September 9, 2020 Submission instructions: Upload your solutions, minus the code, to Gradescope, like you did last week. Email your code to me as an attachment at [email protected], including the string HW2 in the subject line so I can filter them. Problem 1. Both prime numbers and perfect squares get more and more uncommon among larger and larger numbers. But just how uncommon are they? a) Roughly how many perfect squares are there less than or equal to N? p 2 For a positivep integer k, notice that k is less than N if and only if k ≤ N. So there are about N possibilities for k. b) Are there likely to be more prime numbers or perfect squares less than 10100? Give an estimate of the number of each. p There are about 1010 = 1050 perfect squares. According to the prime number theorem, there are about 10100 10100 π(10100) ≈ = = 1098 · log 10 = 2:3 · 1098: log(10100) 100 log 10 That’s a lot more primes than squares. (Note that the “log” in the prime number theorem is the natural log. If you use log10, you won’t get the right answer.) Problem 2. Compute g = gcd(1661; 231). Find integers a and b so that 1661a + 231b = g. (You can do this by hand or on the computer; either submit the code or show your work.) We do this using the Euclidean algorithm. At each step, we keep track of how to write ri as a combination of a and b.
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												  Further Generalizations of Happy NumbersFurther Generalizations of Happy Numbers E. S. Williams June 15, 2016 Abstract In this paper we generalize the concept of happy numbers in several ways. First we confirm known results of Grundman and Teeple in [2] and establish further results, not given in that work. Then we construct a similar function expanding the definition of happy numbers to negative integers. Using this function, we compute and prove results extending those regarding higher powers and sequences of consecutive happy numbers that El-Sidy and Siksek and Grundman and Teeple proved in [1], [2] and [3] to negative integers. Finally, we consider a variety of special cases, in which the existence of certain fixed points and cycles of infinite families of generalized happy functions can be proven. 1 Introduction Happy numbers have been studied for many years, although the origin of the concept is unclear. Consider the sum of the square of the digits of an arbitrary positive integer. If repeating the process of taking the sums of the squares of the digits of an integer eventually gets us 1, then that integer is happy. This paper first provides a brief overview of traditional happy numbers, then describes existing work on generalizations of the concept to other bases and higher powers. We extend some earlier results for the cubic case to the quartic case. We then extend the happy function S to negative integers, constructing a function Q : Z ! Z which agrees with S over Z+ for this purpose. We then generalize Q to various bases and higher powers. As is traditional in 1 the study of special numbers, we consider consecutive sequences of happy numbers, and generalize this study to Q.
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												  Python – an IntroductionPython { AN IntroduCtion . default parameters . self documenting code Example for extensions: Gnuplot Hans Fangohr, [email protected], CED Seminar 05/02/2004 ² The wc program in Python ² Summary Overview ² Outlook ² Why Python ² Literature: How to get started ² Interactive Python (IPython) ² M. Lutz & D. Ascher: Learning Python Installing extra modules ² ² ISBN: 1565924649 (1999) (new edition 2004, ISBN: Lists ² 0596002815). We point to this book (1999) where For-loops appropriate: Chapter 1 in LP ² ! if-then ² modules and name spaces Alex Martelli: Python in a Nutshell ² ² while ISBN: 0596001886 ² string handling ² ¯le-input, output Deitel & Deitel et al: Python { How to Program ² ² functions ISBN: 0130923613 ² Numerical computation ² some other features Other resources: ² . long numbers www.python.org provides extensive . exceptions ² documentation, tools and download. dictionaries Python { an introduction 1 Python { an introduction 2 Why Python? How to get started: The interpreter and how to run code Chapter 1, p3 in LP Chapter 1, p12 in LP ! Two options: ! All sorts of reasons ;-) interactive session ² ² . Object-oriented scripting language . start Python interpreter (python.exe, python, . power of high-level language double click on icon, . ) . portable, powerful, free . prompt appears (>>>) . mixable (glue together with C/C++, Fortran, . can enter commands (as on MATLAB prompt) . ) . easy to use (save time developing code) execute program ² . easy to learn . Either start interpreter and pass program name . (in-built complex numbers) as argument: python.exe myfirstprogram.py Today: . ² Or make python-program executable . easy to learn (Unix/Linux): . some interesting features of the language ./myfirstprogram.py . use as tool for small sysadmin/data . Note: python-programs tend to end with .py, processing/collecting tasks but this is not necessary.
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												  6Th Online Learning #2 MATH Subject: Mathematics State: Ohio6th Online Learning #2 MATH Subject: Mathematics State: Ohio Student Name: Teacher Name: School Name: 1 Yari was doing the long division problem shown below. When she finishes, her teacher tells her she made a mistake. Find Yari's mistake. Explain it to her using at least 2 complete sentences. Then, re-do the long division problem correctly. 2 Use the computation shown below to find the products. Explain how you found your answers. (a) 189 × 16 (b) 80 × 16 (c) 9 × 16 3 Solve. 34,992 ÷ 81 = ? 4 The total amount of money collected by a store for sweatshirt sales was $10,000. Each sweatshirt sold for $40. What was the total number of sweatshirts sold by the store? (A) 100 (B) 220 (C) 250 (D) 400 5 Justin divided 403 by a number and got a quotient of 26 with a remainder of 13. What was the number Justin divided by? (A) 13 (B) 14 (C) 15 (D) 16 6 What is the quotient of 13,632 ÷ 48? (A) 262 R36 (B) 272 (C) 284 (D) 325 R32 7 What is the result when 75,069 is divided by 45? 8 What is the value of 63,106 ÷ 72? Write your answer below. 9 Divide. 21,900 ÷ 25 Write the exact quotient below. 10 The manager of a bookstore ordered 480 copies of a book. The manager paid a total of $7,440 for the books. The books arrived at the store in 5 cartons. Each carton contained the same number of books. A worker unpacked books at a rate of 48 books every 2 minutes.
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												  Introduction to GNU OctaveIntroduction 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.
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												  13A. Lists of Numbers13A. Lists of Numbers Topics: Lists of numbers List Methods: Void vs Fruitful Methods Setting up Lists A Function that returns a list We Have Seen Lists Before Recall that the rgb encoding of a color involves a triplet of numbers: MyColor = [.3,.4,.5] DrawDisk(0,0,1,FillColor = MyColor) MyColor is a list. A list of numbers is a way of assembling a sequence of numbers. Terminology x = [3.0, 5.0, -1.0, 0.0, 3.14] How we talk about what is in a list: 5.0 is an item in the list x. 5.0 is an entry in the list x. 5.0 is an element in the list x. 5.0 is a value in the list x. Get used to the synonyms. A List Has a Length The following would assign the value of 5 to the variable n: x = [3.0, 5.0, -1.0, 0.0, 3.14] n = len(x) The Entries in a List are Accessed Using Subscripts The following would assign the value of -1.0 to the variable a: x = [3.0, 5.0, -1.0, 0.0, 3.14] a = x[2] A List Can Be Sliced This: x = [10,40,50,30,20] y = x[1:3] z = x[:3] w = x[3:] Is same as: x = [10,40,50,30,20] y = [40,50] z = [10,40,50] w = [30,20] Lists are Similar to Strings s: ‘x’ ‘L’ ‘1’ ‘?’ ‘a’ ‘C’ x: 3 5 2 7 0 4 A string is a sequence of characters.
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												  Ground and Explanation in Mathematicsvolume 19, no. 33 ncreased attention has recently been paid to the fact that in math- august 2019 ematical practice, certain mathematical proofs but not others are I recognized as explaining why the theorems they prove obtain (Mancosu 2008; Lange 2010, 2015a, 2016; Pincock 2015). Such “math- ematical explanation” is presumably not a variety of causal explana- tion. In addition, the role of metaphysical grounding as underwriting a variety of explanations has also recently received increased attention Ground and Explanation (Correia and Schnieder 2012; Fine 2001, 2012; Rosen 2010; Schaffer 2016). Accordingly, it is natural to wonder whether mathematical ex- planation is a variety of grounding explanation. This paper will offer several arguments that it is not. in Mathematics One obstacle facing these arguments is that there is currently no widely accepted account of either mathematical explanation or grounding. In the case of mathematical explanation, I will try to avoid this obstacle by appealing to examples of proofs that mathematicians themselves have characterized as explanatory (or as non-explanatory). I will offer many examples to avoid making my argument too dependent on any single one of them. I will also try to motivate these characterizations of various proofs as (non-) explanatory by proposing an account of what makes a proof explanatory. In the case of grounding, I will try to stick with features of grounding that are relatively uncontroversial among grounding theorists. But I will also Marc Lange look briefly at how some of my arguments would fare under alternative views of grounding. I hope at least to reveal something about what University of North Carolina at Chapel Hill grounding would have to look like in order for a theorem’s grounds to mathematically explain why that theorem obtains.
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												  Whole Numbers03_WNCP_Gr4_U02.qxd.qxd 3/19/07 12:04 PM Page 32 U N I T Whole Numbers Learning Goals • recognize and read numbers from 1 to 10 000 • read and write numbers in standard form, expanded form, and written form • compare and order numbers • use diagrams to show relationships • estimate sums and differences • add and subtract 3-digit and 4-digit numbers mentally • use personal strategies to add and subtract • pose and solve problems 32 03_WNCP_Gr4_U02.qxd.qxd 3/19/07 12:04 PM Page 33 Key Words expanded form The elephant is the world’s largest animal. There are two kinds of elephants. standard form The African elephant can be found in most parts of Africa. Venn diagram The Asian elephant can be found in Southeast Asia. Carroll diagram African elephants are larger and heavier than their Asian cousins. The mass of a typical adult African female elephant is about 3600 kg. The mass of a typical male is about 5500 kg. The mass of a typical adult Asian female elephant is about 2720 kg. The mass of a typical male is about 4990 kg. •How could you find how much greater the mass of the African female elephant is than the Asian female elephant? •Kandula,a male Asian elephant,had a mass of about 145 kg at birth. Estimate how much mass he will gain from birth to adulthood. •The largest elephant on record was an African male with an estimated mass of about 10 000 kg. About how much greater was the mass of this elephant than the typical African male elephant? 33 03_WNCP_Gr4_U02.qxd.qxd 3/19/07 12:04 PM Page 34 LESSON Whole Numbers to 10 000 The largest marching band ever assembled had 4526 members.
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												  1. IntroductionJournal of Combinatorics and Number Theory ISSN 1942-5600 Volume 2, Issue 3, pp. 65-77 c 2010 Nova Science Publishers, Inc. CYCLES AND FIXED POINTS OF HAPPY FUNCTIONS Kathryn Hargreaves∗ and Samir Sikseky Mathematics Institute, University of Warwick Coventry, CV4 7AL, United Kingdom Received October 8, 2009; Accepted September 22, 2010 Abstract Let N = f1; 2; 3; · · · g denote the natural numbers. Given integers e ≥ 1 and Pn i b ≥ 2, let x = i=0 aib with 0 ≤ ai ≤ b − 1 (thus ai are the digits of x in base b). We define the happy function Se;b : N −! N by n X e Se;b(x) = ai : i=0 r A positive integer x is then said to be (e; b)-happy if Se;b(x) = 1 for some r ≥ 0, otherwise we say it is (e; b)-unhappy. In this paper we investigate the cycles and fixed points of the happy functions Se;b. We give an upper bound for the size of elements belonging to the cycles of Se;b. We also prove that the number of fixed points of S2;b is ( b2+1 2 τ 2 − 1 if b is odd, τ(b2 + 1) − 1 if b is even, where τ(m) denotes the number of positive divisors of m. We use our results to determine the densities of happy numbers in a small handful of cases. 2000 Mathematics Subject Classification: Primary 11A63; Secondary 11B05. 1. Introduction Guy [G] introduces Problem E34 Happy numbers as follows: Reg. Allenby’s daughter came home from school in Britain with the concept of happy numbers.
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												  WGLT Program Guide, March-April, 2012Illinois State University ISU ReD: Research and eData WGLT Program Guides Arts and Sciences Spring 3-1-2012 WGLT Program Guide, March-April, 2012 Illinois State University Follow this and additional works at: https://ir.library.illinoisstate.edu/wgltpg Recommended Citation Illinois State University, "WGLT Program Guide, March-April, 2012" (2012). WGLT Program Guides. 241. https://ir.library.illinoisstate.edu/wgltpg/241 This Book is brought to you for free and open access by the Arts and Sciences at ISU ReD: Research and eData. It has been accepted for inclusion in WGLT Program Guides by an authorized administrator of ISU ReD: Research and eData. For more information, please contact [email protected]. You'll be able to count on GLT's own award-winning news team because we will Seven is a not only a prime number but also a double Mersenne prime, have the resources they need to continue a Newman-Shanks-Williams prime, a Woodall prime, a factorial prime, providing you with top quality local a lucky prime, a safe prime (the only Mersenne safe prime) and a "happy and regional news. prime," or happy number. Whether you love our top shelf jazz According to Sir Isaac Newton, a rainbow has seven colors. service or great weekend blues - or both - There are seven deadly sins, seven dwarves, seven continents, seven you'll get what you want when you tune in ancient wonders of the world, seven chakras and seven days in the week. because we'll have the tools and the talent to bring you the music you love.
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												  Gretl User's GuideGretl 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 ..........................................
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												  Automated Likelihood Based Inference for Stochastic Volatility Models HView metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Institutional Knowledge at Singapore Management University Singapore Management University Institutional Knowledge at Singapore Management University Research Collection School Of Economics School of Economics 11-2009 Automated Likelihood Based Inference for Stochastic Volatility Models H. Skaug Jun YU Singapore Management University, [email protected] Follow this and additional works at: https://ink.library.smu.edu.sg/soe_research Part of the Econometrics Commons Citation Skaug, H. and YU, Jun. Automated Likelihood Based Inference for Stochastic Volatility Models. (2009). 1-28. Research Collection School Of Economics. Available at: https://ink.library.smu.edu.sg/soe_research/1151 This Working Paper is brought to you for free and open access by the School of Economics at Institutional Knowledge at Singapore Management University. It has been accepted for inclusion in Research Collection School Of Economics by an authorized administrator of Institutional Knowledge at Singapore Management University. For more information, please email [email protected]. Automated Likelihood Based Inference for Stochastic Volatility Models Hans J. SKAUG , Jun YU November 2009 Paper No. 15-2009 ANY OPINIONS EXPRESSED ARE THOSE OF THE AUTHOR(S) AND NOT NECESSARILY THOSE OF THE SCHOOL OF ECONOMICS, SMU Automated Likelihood Based Inference for Stochastic Volatility Models¤ Hans J. Skaug,y Jun Yuz October 7, 2008 Abstract: In this paper the Laplace approximation is used to perform classical and Bayesian analyses of univariate and multivariate stochastic volatility (SV) models. We show that imple- mentation of the Laplace approximation is greatly simpli¯ed by the use of a numerical technique known as automatic di®erentiation (AD).