Multiplication and Division
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Lesson 2: the Multiplication of Polynomials
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 2 M1 ALGEBRA II Lesson 2: The Multiplication of Polynomials Student Outcomes . Students develop the distributive property for application to polynomial multiplication. Students connect multiplication of polynomials with multiplication of multi-digit integers. Lesson Notes This lesson begins to address standards A-SSE.A.2 and A-APR.C.4 directly and provides opportunities for students to practice MP.7 and MP.8. The work is scaffolded to allow students to discern patterns in repeated calculations, leading to some general polynomial identities that are explored further in the remaining lessons of this module. As in the last lesson, if students struggle with this lesson, they may need to review concepts covered in previous grades, such as: The connection between area properties and the distributive property: Grade 7, Module 6, Lesson 21. Introduction to the table method of multiplying polynomials: Algebra I, Module 1, Lesson 9. Multiplying polynomials (in the context of quadratics): Algebra I, Module 4, Lessons 1 and 2. Since division is the inverse operation of multiplication, it is important to make sure that your students understand how to multiply polynomials before moving on to division of polynomials in Lesson 3 of this module. In Lesson 3, division is explored using the reverse tabular method, so it is important for students to work through the table diagrams in this lesson to prepare them for the upcoming work. There continues to be a sharp distinction in this curriculum between justification and proof, such as justifying the identity (푎 + 푏)2 = 푎2 + 2푎푏 + 푏 using area properties and proving the identity using the distributive property. -
Computation of 2700 Billion Decimal Digits of Pi Using a Desktop Computer
Computation of 2700 billion decimal digits of Pi using a Desktop Computer Fabrice Bellard Feb 11, 2010 (4th revision) This article describes some of the methods used to get the world record of the computation of the digits of π digits using an inexpensive desktop computer. 1 Notations We assume that numbers are represented in base B with B = 264. A digit in base B is called a limb. M(n) is the time needed to multiply n limb numbers. We assume that M(Cn) is approximately CM(n), which means M(n) is mostly linear, which is the case when handling very large numbers with the Sch¨onhage-Strassen multiplication [5]. log(n) means the natural logarithm of n. log2(n) is log(n)/ log(2). SI and binary prefixes are used (i.e. 1 TB = 1012 bytes, 1 GiB = 230 bytes). 2 Evaluation of the Chudnovsky series 2.1 Introduction The digits of π were computed using the Chudnovsky series [10] ∞ 1 X (−1)n(6n)!(A + Bn) = 12 π (n!)3(3n)!C3n+3/2 n=0 with A = 13591409 B = 545140134 C = 640320 . It was evaluated with the binary splitting algorithm. The asymptotic running time is O(M(n) log(n)2) for a n limb result. It is worst than the asymptotic running time of the Arithmetic-Geometric Mean algorithms of O(M(n) log(n)) but it has better locality and many improvements can reduce its constant factor. Let S be defined as n2 n X Y pk S(n1, n2) = an . qk n=n1+1 k=n1+1 1 We define the auxiliary integers n Y2 P (n1, n2) = pk k=n1+1 n Y2 Q(n1, n2) = qk k=n1+1 T (n1, n2) = S(n1, n2)Q(n1, n2). -
Course Notes 1 1.1 Algorithms: Arithmetic
CS 125 Course Notes 1 Fall 2016 Welcome to CS 125, a course on algorithms and computational complexity. First, what do these terms means? An algorithm is a recipe or a well-defined procedure for performing a calculation, or in general, for transform- ing some input into a desired output. In this course we will ask a number of basic questions about algorithms: • Does the algorithm halt? • Is it correct? That is, does the algorithm’s output always satisfy the input to output specification that we desire? • Is it efficient? Efficiency could be measured in more than one way. For example, what is the running time of the algorithm? What is its memory consumption? Meanwhile, computational complexity theory focuses on classification of problems according to the com- putational resources they require (time, memory, randomness, parallelism, etc.) in various computational models. Computational complexity theory asks questions such as • Is the class of problems that can be solved time-efficiently with a deterministic algorithm exactly the same as the class that can be solved time-efficiently with a randomized algorithm? • For a given class of problems, is there a “complete” problem for the class such that solving that one problem efficiently implies solving all problems in the class efficiently? • Can every problem with a time-efficient algorithmic solution also be solved with extremely little additional memory (beyond the memory required to store the problem input)? 1.1 Algorithms: arithmetic Some algorithms very familiar to us all are those those for adding and multiplying integers. We all know the grade school algorithm for addition from kindergarten: write the two numbers on top of each other, then add digits right 1-1 1-2 1 7 8 × 2 1 3 5 3 4 1 7 8 +3 5 6 3 7 914 Figure 1.1: Grade school multiplication. -
The Role of the Interval Domain in Modern Exact Real Airthmetic
The Role of the Interval Domain in Modern Exact Real Airthmetic Andrej Bauer Iztok Kavkler Faculty of Mathematics and Physics University of Ljubljana, Slovenia Domains VIII & Computability over Continuous Data Types Novosibirsk, September 2007 Teaching theoreticians a lesson Recently I have been told by an anonymous referee that “Theoreticians do not like to be taught lessons.” and by a friend that “You should stop competing with programmers.” In defiance of this advice, I shall talk about the lessons I learned, as a theoretician, in programming exact real arithmetic. The spectrum of real number computation slow fast Formally verified, Cauchy sequences iRRAM extracted from streams of signed digits RealLib proofs floating point Moebius transformtions continued fractions Mathematica "theoretical" "practical" I Common features: I Reals are represented by successive approximations. I Approximations may be computed to any desired accuracy. I State of the art, as far as speed is concerned: I iRRAM by Norbert Muller,¨ I RealLib by Branimir Lambov. What makes iRRAM and ReaLib fast? I Reals are represented by sequences of dyadic intervals (endpoints are rationals of the form m/2k). I The approximating sequences need not be nested chains of intervals. I No guarantee on speed of converge, but arbitrarily fast convergence is possible. I Previous approximations are not stored and not reused when the next approximation is computed. I Each next approximation roughly doubles the amount of work done. The theory behind iRRAM and RealLib I Theoretical models used to design iRRAM and RealLib: I Type Two Effectivity I a version of Real RAM machines I Type I representations I The authors explicitly reject domain theory as a suitable computational model. -
Division by Fractions 6.1.1 - 6.1.4
DIVISION BY FRACTIONS 6.1.1 - 6.1.4 Division by fractions introduces three methods to help students understand how dividing by fractions works. In general, think of division for a problem like 8..,.. 2 as, "In 8, how many groups of 2 are there?" Similarly, ½ + ¼ means, "In ½ , how many fourths are there?" For more information, see the Math Notes boxes in Lessons 7.2 .2 and 7 .2 .4 of the Core Connections, Course 1 text. For additional examples and practice, see the Core Connections, Course 1 Checkpoint 8B materials. The first two examples show how to divide fractions using a diagram. Example 1 Use the rectangular model to divide: ½ + ¼ . Step 1: Using the rectangle, we first divide it into 2 equal pieces. Each piece represents ½. Shade ½ of it. - Step 2: Then divide the original rectangle into four equal pieces. Each section represents ¼ . In the shaded section, ½ , there are 2 fourths. 2 Step 3: Write the equation. Example 2 In ¾ , how many ½ s are there? In ¾ there is one full ½ 2 2 I shaded and half of another Thatis,¾+½=? one (that is half of one half). ]_ ..,_ .l 1 .l So. 4 . 2 = 2 Start with ¾ . 3 4 (one and one-half halves) Parent Guide with Extra Practice © 2011, 2013 CPM Educational Program. All rights reserved. 49 Problems Use the rectangular model to divide. .l ...:... J_ 1 ...:... .l 1. ..,_ l 1 . 1 3 . 6 2. 3. 4. 1 4 . 2 5. 2 3 . 9 Answers l. 8 2. 2 3. 4 one thirds rm I I halves - ~I sixths fourths fourths ~I 11 ~'.¿;¡~:;¿~ ffk] 8 sixths 2 three fourths 4. -
Chapter 2. Multiplication and Division of Whole Numbers in the Last Chapter You Saw That Addition and Subtraction Were Inverse Mathematical Operations
Chapter 2. Multiplication and Division of Whole Numbers In the last chapter you saw that addition and subtraction were inverse mathematical operations. For example, a pay raise of 50 cents an hour is the opposite of a 50 cents an hour pay cut. When you have completed this chapter, you’ll understand that multiplication and division are also inverse math- ematical operations. 2.1 Multiplication with Whole Numbers The Multiplication Table Learning the multiplication table shown below is a basic skill that must be mastered. Do you have to memorize this table? Yes! Can’t you just use a calculator? No! You must know this table by heart to be able to multiply numbers, to do division, and to do algebra. To be blunt, until you memorize this entire table, you won’t be able to progress further than this page. MULTIPLICATION TABLE ϫ 012 345 67 89101112 0 000 000LEARNING 00 000 00 1 012 345 67 89101112 2 024 681012Copy14 16 18 20 22 24 3 036 9121518212427303336 4 0481216 20 24 28 32 36 40 44 48 5051015202530354045505560 6061218243036424854606672Distribute 7071421283542495663707784 8081624324048566472808896 90918273HAWKESReview645546372819099108 10 0 10 20 30 40 50 60 70 80 90 100 110 120 ©11 0 11 22 33 44NOT 55 66 77 88 99 110 121 132 12 0 12 24 36 48 60 72 84 96 108 120 132 144 Do Let’s get a couple of things out of the way. First, any number times 0 is 0. When we multiply two numbers, we call our answer the product of those two numbers. -
Grade 7/8 Math Circles the Scale of Numbers Introduction
Faculty of Mathematics Centre for Education in Waterloo, Ontario N2L 3G1 Mathematics and Computing Grade 7/8 Math Circles November 21/22/23, 2017 The Scale of Numbers Introduction Last week we quickly took a look at scientific notation, which is one way we can write down really big numbers. We can also use scientific notation to write very small numbers. 1 × 103 = 1; 000 1 × 102 = 100 1 × 101 = 10 1 × 100 = 1 1 × 10−1 = 0:1 1 × 10−2 = 0:01 1 × 10−3 = 0:001 As you can see above, every time the value of the exponent decreases, the number gets smaller by a factor of 10. This pattern continues even into negative exponent values! Another way of picturing negative exponents is as a division by a positive exponent. 1 10−6 = = 0:000001 106 In this lesson we will be looking at some famous, interesting, or important small numbers, and begin slowly working our way up to the biggest numbers ever used in mathematics! Obviously we can come up with any arbitrary number that is either extremely small or extremely large, but the purpose of this lesson is to only look at numbers with some kind of mathematical or scientific significance. 1 Extremely Small Numbers 1. Zero • Zero or `0' is the number that represents nothingness. It is the number with the smallest magnitude. • Zero only began being used as a number around the year 500. Before this, ancient mathematicians struggled with the concept of `nothing' being `something'. 2. Planck's Constant This is the smallest number that we will be looking at today other than zero. -
A Scalable System-On-A-Chip Architecture for Prime Number Validation
A SCALABLE SYSTEM-ON-A-CHIP ARCHITECTURE FOR PRIME NUMBER VALIDATION Ray C.C. Cheung and Ashley Brown Department of Computing, Imperial College London, United Kingdom Abstract This paper presents a scalable SoC architecture for prime number validation which targets reconfigurable hardware This paper presents a scalable SoC architecture for prime such as FPGAs. In particular, users are allowed to se- number validation which targets reconfigurable hardware. lect predefined scalable or non-scalable modular opera- The primality test is crucial for security systems, especially tors for their designs [4]. Our main contributions in- for most public-key schemes. The Rabin-Miller Strong clude: (1) Parallel designs for Montgomery modular arith- Pseudoprime Test has been mapped into hardware, which metic operations (Section 3). (2) A scalable design method makes use of a circuit for computing Montgomery modu- for mapping the Rabin-Miller Strong Pseudoprime Test lar exponentiation to further speed up the validation and to into hardware (Section 4). (3) An architecture of RAM- reduce the hardware cost. A design generator has been de- based Radix-2 Scalable Montgomery multiplier (Section veloped to generate a variety of scalable and non-scalable 4). (4) A design generator for producing hardware prime Montgomery multipliers based on user-defined parameters. number validators based on user-specified parameters (Sec- The performance and resource usage of our designs, im- tion 5). (5) Implementation of the proposed hardware ar- plemented in Xilinx reconfigurable devices, have been ex- chitectures in FPGAs, with an evaluation of its effective- plored using the embedded PowerPC processor and the soft ness compared with different size and speed tradeoffs (Sec- MicroBlaze processor. -
Arithmetic Algorisms in Different Bases
Arithmetic Algorisms in Different Bases The Addition Algorithm Addition Algorithm - continued To add in any base – Step 1 14 Step 2 1 Arithmetic Algorithms in – Add the digits in the “ones ”column to • Add the digits in the “base” 14 + 32 5 find the number of “1s”. column. + 32 2 + 4 = 6 5 Different Bases • If the number is less than the base place – If the sum is less than the base 1 the number under the right hand column. 6 > 5 enter under the “base” column. • If the number is greater than or equal to – If the sum is greater than or equal 1+1+3 = 5 the base, express the number as a base 6 = 11 to the base express the sum as a Addition, Subtraction, 5 5 = 10 5 numeral. The first digit indicates the base numeral. Carry the first digit Multiplication and Division number of the base in the sum so carry Place 1 under to the “base squared” column and Carry the 1 five to the that digit to the “base” column and write 4 + 2 and carry place the second digit under the 52 column. the second digit under the right hand the 1 five to the “base” column. Place 0 under the column. “fives” column Step 3 “five” column • Repeat this process to the end. The sum is 101 5 Text Chapter 4 – Section 4 No in-class assignment problem In-class Assignment 20 - 1 Example of Addition The Subtraction Algorithm A Subtraction Problem 4 9 352 Find: 1 • 3 + 2 = 5 < 6 →no Subtraction in any base, b 35 2 • 2−4 can not do. -
Three Methods of Finding Remainder on the Operation of Modular
Zin Mar Win, Khin Mar Cho; International Journal of Advance Research and Development (Volume 5, Issue 5) Available online at: www.ijarnd.com Three methods of finding remainder on the operation of modular exponentiation by using calculator 1Zin Mar Win, 2Khin Mar Cho 1University of Computer Studies, Myitkyina, Myanmar 2University of Computer Studies, Pyay, Myanmar ABSTRACT The primary purpose of this paper is that the contents of the paper were to become a learning aid for the learners. Learning aids enhance one's learning abilities and help to increase one's learning potential. They may include books, diagram, computer, recordings, notes, strategies, or any other appropriate items. In this paper we would review the types of modulo operations and represent the knowledge which we have been known with the easiest ways. The modulo operation is finding of the remainder when dividing. The modulus operator abbreviated “mod” or “%” in many programming languages is useful in a variety of circumstances. It is commonly used to take a randomly generated number and reduce that number to a random number on a smaller range, and it can also quickly tell us if one number is a factor of another. If we wanted to know if a number was odd or even, we could use modulus to quickly tell us by asking for the remainder of the number when divided by 2. Modular exponentiation is a type of exponentiation performed over a modulus. The operation of modular exponentiation calculates the remainder c when an integer b rose to the eth power, bᵉ, is divided by a positive integer m such as c = be mod m [1]. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
1.3 Division of Polynomials; Remainder and Factor Theorems
1-32 CHAPTER 1. POLYNOMIAL AND RATIONAL FUNCTIONS 1.3 Division of Polynomials; Remainder and Factor Theorems Objectives • Perform long division of polynomials • Perform synthetic division of polynomials • Apply remainder and factor theorems In previous courses, you may have learned how to factor polynomials using various techniques. Many of these techniques apply only to special kinds of polynomial expressions. For example, the the previous two sections of this chapter dealt only with polynomials that could easily be factored to find the zeros and x-intercepts. In order to be able to find zeros and x-intercepts of polynomials which cannot readily be factored, we first introduce long division of polynomials. We will then use the long division algorithm to make a general statement about factors of a polynomial. Long division of polynomials Long division of polynomials is similar to long division of numbers. When divid- ing polynomials, we obtain a quotient and a remainder. Just as with numbers, if a remainder is 0, then the divisor is a factor of the dividend. Example 1 Determining Factors by Division Divide to determine whether x − 1 is a factor of x2 − 3x +2. Solution Here x2 − 3x +2is the dividend and x − 1 is the divisor. Step 1 Set up the division as follows: divisor → x − 1 x2−3x+2 ← dividend Step 2 Divide the leading term of the dividend (x2) by the leading term of the divisor (x). The result (x)is the first term of the quotient, as illustrated below. x ← first term of quotient x − 1 x2 −3x+2 1.3.