Comparing Infinite Sets
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Math/CS 467 (Lesieutre) Homework 2 September 9, 2020 Submission
Math/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. -
COMPSCI 501: Formal Language Theory Insights on Computability Turing Machines Are a Model of Computation Two (No Longer) Surpris
Insights on Computability Turing machines are a model of computation COMPSCI 501: Formal Language Theory Lecture 11: Turing Machines Two (no longer) surprising facts: Marius Minea Although simple, can describe everything [email protected] a (real) computer can do. University of Massachusetts Amherst Although computers are powerful, not everything is computable! Plus: “play” / program with Turing machines! 13 February 2019 Why should we formally define computation? Must indeed an algorithm exist? Back to 1900: David Hilbert’s 23 open problems Increasingly a realization that sometimes this may not be the case. Tenth problem: “Occasionally it happens that we seek the solution under insufficient Given a Diophantine equation with any number of un- hypotheses or in an incorrect sense, and for this reason do not succeed. known quantities and with rational integral numerical The problem then arises: to show the impossibility of the solution under coefficients: To devise a process according to which the given hypotheses or in the sense contemplated.” it can be determined in a finite number of operations Hilbert, 1900 whether the equation is solvable in rational integers. This asks, in effect, for an algorithm. Hilbert’s Entscheidungsproblem (1928): Is there an algorithm that And “to devise” suggests there should be one. decides whether a statement in first-order logic is valid? Church and Turing A Turing machine, informally Church and Turing both showed in 1936 that a solution to the Entscheidungsproblem is impossible for the theory of arithmetic. control To make and prove such a statement, one needs to define computability. In a recent paper Alonzo Church has introduced an idea of “effective calculability”, read/write head which is equivalent to my “computability”, but is very differently defined. -
Section 4.2 Counting
Section 4.2 Counting CS 130 – Discrete Structures Counting: Multiplication Principle • The idea is to find out how many members are present in a finite set • Multiplication Principle: If there are n possible outcomes for a first event and m possible outcomes for a second event, then there are n*m possible outcomes for the sequence of two events. • From the multiplication principle, it follows that for 2 sets A and B, |A x B| = |A|x|B| – A x B consists of all ordered pairs with first component from A and second component from B CS 130 – Discrete Structures 27 Examples • How many four digit number can there be if repetitions of numbers is allowed? • And if repetition of numbers is not allowed? • If a man has 4 suits, 8 shirts and 5 ties, how many outfits can he put together? CS 130 – Discrete Structures 28 Counting: Addition Principle • Addition Principle: If A and B are disjoint events with n and m outcomes, respectively, then the total number of possible outcomes for event “A or B” is n+m • If A and B are disjoint sets, then |A B| = |A| + |B| using the addition principle • Example: A customer wants to purchase a vehicle from a dealer. The dealer has 23 autos and 14 trucks in stock. How many selections does the customer have? CS 130 – Discrete Structures 29 More On Addition Principle • If A and B are disjoint sets, then |A B| = |A| + |B| • Prove that if A and B are finite sets then |A-B| = |A| - |A B| and |A-B| = |A| - |B| if B A (A-B) (A B) = (A B) (A B) = A (B B) = A U = A Also, A-B and A B are disjoint sets, therefore using the addition principle, |A| = | (A-B) (A B) | = |A-B| + |A B| Hence, |A-B| = |A| - |A B| If B A, then A B = B Hence, |A-B| = |A| - |B| CS 130 – Discrete Structures 30 Using Principles Together • How many four-digit numbers begin with a 4 or a 5 • How many three-digit integers (numbers between 100 and 999 inclusive) are even? • Suppose the last four digit of a telephone number must include at least one repeated digit. -
6Th Online Learning #2 MATH Subject: Mathematics State: Ohio
6th 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. -
180: Counting Techniques
180: Counting Techniques In the following exercise we demonstrate the use of a few fundamental counting principles, namely the addition, multiplication, complementary, and inclusion-exclusion principles. While none of the principles are particular complicated in their own right, it does take some practice to become familiar with them, and recognise when they are applicable. I have attempted to indicate where alternate approaches are possible (and reasonable). Problem: Assume n ≥ 2 and m ≥ 1. Count the number of functions f :[n] ! [m] (i) in total. (ii) such that f(1) = 1 or f(2) = 1. (iii) with minx2[n] f(x) ≤ 5. (iv) such that f(1) ≥ f(2). (v) that are strictly increasing; that is, whenever x < y, f(x) < f(y). (vi) that are one-to-one (injective). (vii) that are onto (surjective). (viii) that are bijections. (ix) such that f(x) + f(y) is even for every x; y 2 [n]. (x) with maxx2[n] f(x) = minx2[n] f(x) + 1. Solution: (i) A function f :[n] ! [m] assigns for every integer 1 ≤ x ≤ n an integer 1 ≤ f(x) ≤ m. For each integer x, we have m options. As we make n such choices (independently), the total number of functions is m × m × : : : m = mn. (ii) (We assume n ≥ 2.) Let A1 be the set of functions with f(1) = 1, and A2 the set of functions with f(2) = 1. Then A1 [ A2 represents those functions with f(1) = 1 or f(2) = 1, which is precisely what we need to count. We have jA1 [ A2j = jA1j + jA2j − jA1 \ A2j. -
Infinite Sets
“mcs-ftl” — 2010/9/8 — 0:40 — page 379 — #385 13 Infinite Sets So you might be wondering how much is there to say about an infinite set other than, well, it has an infinite number of elements. Of course, an infinite set does have an infinite number of elements, but it turns out that not all infinite sets have the same size—some are bigger than others! And, understanding infinity is not as easy as you might think. Some of the toughest questions in mathematics involve infinite sets. Why should you care? Indeed, isn’t computer science only about finite sets? Not exactly. For example, we deal with the set of natural numbers N all the time and it is an infinite set. In fact, that is why we have induction: to reason about predicates over N. Infinite sets are also important in Part IV of the text when we talk about random variables over potentially infinite sample spaces. So sit back and open your mind for a few moments while we take a very brief look at infinity. 13.1 Injections, Surjections, and Bijections We know from Theorem 7.2.1 that if there is an injection or surjection between two finite sets, then we can say something about the relative sizes of the two sets. The same is true for infinite sets. In fact, relations are the primary tool for determining the relative size of infinite sets. Definition 13.1.1. Given any two sets A and B, we say that A surj B iff there is a surjection from A to B, A inj B iff there is an injection from A to B, A bij B iff there is a bijection between A and B, and A strict B iff there is a surjection from A to B but there is no bijection from B to A. -
UNIT 1: Counting and Cardinality
Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days EVERGREEN SCHOOL K DISTRICT GRADE Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Counting Operations Geometry Measurement Numbers & Algebraic Thinking and and Data Operations in Cardinality Base 10 8 weeks 4 weeks 8 weeks 3 weeks 4 weeks 5 weeks UNIT 1: Counting and Cardinality Dear Colleagues, Enclosed is a unit that addresses all of the Common Core Counting and Cardinality standards for Kindergarten. We took the time to analyze, group and organize them into a logical learning sequence. Thank you for entrusting us with the task of designing a rich learning experience for all students, and we hope to improve the unit as you pilot it and make it your own. Sincerely, Grade K Math Unit Design Team CRITICAL THINKING COLLABORATION COMMUNICATION CREATIVITY Evergreen School District 1 MATH Curriculum Map aligned to the California Common Core State Standards 7/29/14 Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days UNIT 1 TABLE OF CONTENTS Overview of the grade K Mathematics Program . 3 Essential Standards . 4 Emphasized Mathematical Practices . 4 Enduring Understandings & Essential Questions . 5 Chapter Overviews . 6 Chapter 1 . 8 Chapter 2 . 9 Chapter 3 . 10 Chapter 4 . 11 End-of-Unit Performance Task . 12 Appendices . 13 Evergreen School District 2 MATH Curriculum Map aligned to the California Common Core State Standards 7/29/14 Grade: K Unit Number: 1 Unit Name: Counting and Cardinality Instructional Days: 40 days Overview of the Grade K Mathematics Program UNIT NAME APPROX. -
Axiomatic Set Teory P.D.Welch
Axiomatic Set Teory P.D.Welch. August 16, 2020 Contents Page 1 Axioms and Formal Systems 1 1.1 Introduction 1 1.2 Preliminaries: axioms and formal systems. 3 1.2.1 The formal language of ZF set theory; terms 4 1.2.2 The Zermelo-Fraenkel Axioms 7 1.3 Transfinite Recursion 9 1.4 Relativisation of terms and formulae 11 2 Initial segments of the Universe 17 2.1 Singular ordinals: cofinality 17 2.1.1 Cofinality 17 2.1.2 Normal Functions and closed and unbounded classes 19 2.1.3 Stationary Sets 22 2.2 Some further cardinal arithmetic 24 2.3 Transitive Models 25 2.4 The H sets 27 2.4.1 H - the hereditarily finite sets 28 2.4.2 H - the hereditarily countable sets 29 2.5 The Montague-Levy Reflection theorem 30 2.5.1 Absoluteness 30 2.5.2 Reflection Theorems 32 2.6 Inaccessible Cardinals 34 2.6.1 Inaccessible cardinals 35 2.6.2 A menagerie of other large cardinals 36 3 Formalising semantics within ZF 39 3.1 Definite terms and formulae 39 3.1.1 The non-finite axiomatisability of ZF 44 3.2 Formalising syntax 45 3.3 Formalising the satisfaction relation 46 3.4 Formalising definability: the function Def. 47 3.5 More on correctness and consistency 48 ii iii 3.5.1 Incompleteness and Consistency Arguments 50 4 The Constructible Hierarchy 53 4.1 The L -hierarchy 53 4.2 The Axiom of Choice in L 56 4.3 The Axiom of Constructibility 57 4.4 The Generalised Continuum Hypothesis in L. -
Algorithms, Turing Machines and Algorithmic Undecidability
U.U.D.M. Project Report 2021:7 Algorithms, Turing machines and algorithmic undecidability Agnes Davidsdottir Examensarbete i matematik, 15 hp Handledare: Vera Koponen Examinator: Martin Herschend April 2021 Department of Mathematics Uppsala University Contents 1 Introduction 1 1.1 Algorithms . .1 1.2 Formalisation of the concept of algorithms . .1 2 Turing machines 3 2.1 Coding of machines . .4 2.2 Unbounded and bounded machines . .6 2.3 Binary sequences representing real numbers . .6 2.4 Examples of Turing machines . .7 3 Undecidability 9 i 1 Introduction This paper is about Alan Turing's paper On Computable Numbers, with an Application to the Entscheidungsproblem, which was published in 1936. In his paper, he introduced what later has been called Turing machines as well as a few examples of undecidable problems. A few of these will be brought up here along with Turing's arguments in the proofs but using a more modern terminology. To begin with, there will be some background on the history of why this breakthrough happened at that given time. 1.1 Algorithms The concept of an algorithm has always existed within the world of mathematics. It refers to a process meant to solve a problem in a certain number of steps. It is often repetitive, with only a few rules to follow. In more recent years, the term also has been used to refer to the rules a computer follows to operate in a certain way. Thereby, an algorithm can be used in a plethora of circumstances. The word might describe anything from the process of solving a Rubik's cube to how search engines like Google work [4]. -
13A. Lists of Numbers
13A. 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. -
Ground and Explanation in Mathematics
volume 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. -
Whole Numbers
03_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.