3 History in Mathematics Education

Total Page:16

File Type:pdf, Size:1020Kb

3 History in Mathematics Education 3 History in mathematics education 3.1 Introduction Over the years mathematicians, educators and historians have wondered whether mathematics learning and teaching might profit from integrating elements of history of mathematics. It is clear that mathematics education does not succeed to reach its aims for all students, and that it is therefore worthwhile to investigate whether his- tory can help to improve the situation. The interest in using history and the belief in its value for learning have grown remarkably in recent years – judging from the number of research groups in this field in Italy and France, and the History in Math- ematics Education (HIMED) movement founded after a number of successful con- ferences and if we go back in time we find that acknowledged mathematicians from the eighteenth and nineteenth century held the same point of view. Joseph Louis Lagrange (1736-1813) wrote in one of his lectures for trainee school teachers: Since the calculation of logarithms is now a thing of the past, except in isolated in- stances, it may be thought that the details into which we have entered are devoid of value. We may, however, justly be curious to know the trying and tortuous paths which the great inventors have trodden, the different steps which they have taken to attain their goal, and the extent to which we are indebted to these veritable benefactors of the human race. Such knowledge, moreover, is not a matter of idle curiosity. It can afford us guidance in similar inquiries and sheds an increased light on the subjects with which we are employed (cited in Fauvel & Van Maanen, 2000, p. 35). And in one of his notebooks Niels Henrik Abel (1802-1829) remarked: “It appears to me that if one wants to make progress in mathematics one should study the mas- ters” (cited in Fauvel & Van Maanen, 2000, p. 35). Only recently there has been a stronger call for methodological and theoretical foun- dations for the role of history in mathematics education. The report History in Math- ematics Education: The ICMI Study (Fauvel & Van Maanen, 2000) has made a valu- able contribution in this respect by collecting theories, results, experiences and ideas of implementing history in mathematics education from around the world. The purpose of this chapter is to explain why and how history of mathematics might play a role in the learning and teaching of early algebra. After a brief account of the historical development of algebra we describe how history has instigated some es- sential ideas for the experimental learning strand. 3.2 Arguments for using history We would not plead for the use of history of mathematics in mathematics education if we did not believe that history can make a difference. Incorporating history in mathematics education can be beneficial for students, teachers, curriculum develop- ers and researchers in different ways. We give a number of arguments frequently 35 History in mathematics education mentioned to illustrate this (Radford, 1995, 1996ab, 1997; Fauvel, 1991; Fauvel & Van Maanen, 2000). math as a growing Students can experience the subject as a human activity, discovered, invented, discipline changed and extended under the influence of people over time. Instead of seeing mathematics as a ready-made product, they can see that mathematics is a continu- ously changing and growing body of knowledge to which they can contribute them- selves. Learners will acquire a notion of processes and progress and learn about so- cial and cultural influences. Moreover, history accentuates the links between math- ematical topics and the role of mathematics in other disciplines, which will help to place mathematics in a broader perspective and thus deepen students’ understand- ing. teacher profits History of mathematics provides opportunities for getting a better view of what mathematics is. When a teacher’s own perception and understanding of mathematics changes, it affects the way mathematics is taught and consequently the way students perceive it. Teachers may find that information on the development of a mathemat- ical topic makes it easier to explain or give an example to students. For instance, heu- ristic approaches provided by history can be contrasted with more formal, contem- porary methods. In addition it is believed that historical knowledge gives the teacher more insight in different stages of learning and typical learning difficulties. On a more personal level, history also helps to sustain the teacher’s interest in mathemat- ics. value for the designer Not only the mathematics teacher but also the educational developer or researcher can profit from history in studying subject matter and learning processes. It provides teachers and developers with an abundance of interesting mathematical problems, sources and methods which can be used either implicitly or explicitly. Historical de- velopments can help the researcher to think through a suitable learning trajectory prior to a teaching experiment, but it can also bring new perspectives to the analysis of student work. In the context of using history to study learning processes we men- tion the so-called Biogenetic Law popular at the beginning of the 20th century. The Biogenetic Law states that mathematical learning in the individual (philogenesis) follows the same course as the historical development of mathematics itself (onto- genesis). However, it has become more and more clear since then that such a strong statement cannot be sustained. A short study of mathematical history is sufficient to conclude that its development is not as consistent as this law would require. Freudenthal explains what he understands by ‘guided reinvention’: Urging that ideas are taught genetically does not mean that they should be presented in the order in which they arose, not even with all the deadlocks closed and all the de- tours cut out. What the blind invented and discovered, the sighted afterwards can tell how it should have been discovered if there had been teachers who had known what we know now. (...) It is not the historical footprints of the inventor we should follow but an improved and better guided course of history (Freudenthal, 1973, p. 101, 103). 36 History of algebra: a summary learning trajectory In other words, we can find history helpful in designing a hypothetical learning tra- jectory and use parts of it as a guideline. For instance, Harper (1987) argues that al- gebra students pass through different stages of equation solving, using more sophis- ticated strategies as they become older, in a progression similar to the historical evolvement of equation solving. Harper pleads for more awareness of these levels of algebraic formality in algebra teaching. 3.3 History of algebra: a summary It is generally accepted to distinguish three periods in the development of algebra – oversimplifying, of course, the complex history in doing so – according to the dif- ferent forms of notation: rhetorical, syncopated and symbolic, as shown in table 3.1 (see, for example, Boyer & Merzbach, 1989). rhetoric syncopated symbolic written form of the only words words and numbers words and numbers problem written form in the only words words and numbers; words and numbers; solution method abbreviations and abbreviations and mathematical sym- mathematical sym- bols for operations bols for operations and exponents and exponents representation of word symbol or letter letter the unknown representation of specific numbers specific numbers letters given numbers table 3.1: characteristics of the 3 types of algebraic notation rhetorical phase The rhetorical phase lasted from ancient times until around 250 AD, where the prob- lem itself and the solution process were written in only words. In this period early algebra was a more or less sophisticated way of solving word problems. A typical rule used by the Egyptians and Babylonians for solving problems on proportions is the regula tri or Rule of Three: given three numbers, find the fourth proportionate number (see also Kool, 1999). In modern notations this means: given the numbers a, b and c, find d such that a : b = c : d. The Rule of Three also specifies in which order the numbers in the problem must be written down and then manipulated. An example of such a problem is problem 69 in Rhind Papyrus (ca. 1650 BC), which says: “With 3 half-pecks of flour 80 loafs of bread can be made. How much flour is needed for 1 loaf? How many loafs can be made from 1 half-peck of flour?” (note: 1 half-peck ≈ 4.8 liters) (Tropfke, 1980, p. 359). Indian mathematicians (7th and 11th century) extended the Rule of Three to 5, 7, 9 and 11 numbers. Such problems are commonly classified as arithmetical, but in cases where numbers do not represent specific concrete objects and operations are required on unknown quantities, we can speak of algebraic thinking. 37 History in mathematics education unknown Depending on the number concept of each civilization as well as the mathematical problem, the unknown was a magnitude denoted by words like ‘heap’ (Egyptian), ‘length’ or ‘area’ (Babylonian, Greek), ‘thing’ or ‘root’ (Arabic), ‘cosa’, ‘res’ or ‘ding(k)’ (Western European). The solution was given in terms of instructions and calculations, with no explanation or mention of rules. These calculations indicate that unknowns were treated as if they were known and reasoning about an undeter- mined quantity apparently did not form a conceptual barrier. For instance, in the case x of problems that we would nowadays represent by linear equations of typex + ---= a , n the unknown quantity x was conveniently split up into n equal parts. Rule of False Position The Babylonians also used linear scaling to solve for the unknown in the equation ax= b , much like the regula falsi or Rule of False Position first used systematically by Diophantus (Tropfke, 1980). According to this rule one is to assume a certain val- ue for the solution, perform the operations stated in the problem, and depending on the error in the answer, adjust the initial value using proportions.
Recommended publications
  • A Family of Regula Falsi Root-Finding Methods
    A family of regula falsi root-finding methods Sérgio Galdino Polytechnic School of Pernambuco University Rua Benfica, 455 - Madalena 50.750-470 - Recife - PE - Brazil Email: [email protected] Abstract—A family of regula falsi methods for finding a root its brackets. Second, the convergence is quadratic. Since each ξ of a nonlinear equation f(x)=0 in the interval [a,b] is studied iteration requires√ two function evaluations, the actual order of in this paper. The Illinois, Pegasus, Anderson & Björk and more the method is 2, not 2; but this is still quite respectably nine new proposed methods have been tested on a series of examples. The new methods, inspired on Pegasus procedure, superlinear: The number of significant digits in the answer are pedagogically important algorithms. The numerical results approximately doubles with each two function evaluations. empirically show that some of new methods are very effective. Third, taking out the function’s “bend” via exponential (that Index Terms—regula ralsi, root finding, nonlinear equations. is, ratio) factors, rather than via a polynomial technique (e.g., fitting a parabola), turns out to give an extraordinarily robust I. INTRODUCTION algorithm. In both reliability and speed, Ridders’ method is Is not so hard the design of heuristic procedures to solve a generally competitive with the more highly developed and problem using numerical algorithms. The main drawback of better established (but more complicated) method of van this attitude is that you can be rediscovering the wheel. The Wijngaarden, Dekker, and Brent [9]. famous Halley’s iteration method [1] 1, have the distinction of being the most frequently rediscovered iterative method [2].
    [Show full text]
  • A Review of Bracketing Methods for Finding Zeros of Nonlinear Functions
    Applied Mathematical Sciences, Vol. 12, 2018, no. 3, 137 - 146 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2018.811 A Review of Bracketing Methods for Finding Zeros of Nonlinear Functions Somkid Intep Department of Mathematics, Faculty of Science Burapha University, Chonburi, Thailand Copyright c 2018 Somkid Intep. This article is distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper presents a review of recent bracketing methods for solving a root of nonlinear equations. The performance of algorithms is verified by the iteration numbers and the function evaluation numbers on a number of test examples. Mathematics Subject Classification: 65B99, 65G40 Keywords: Bracketing method, Nonlinear equation 1 Introduction Many scientific and engineering problems are described as nonlinear equations, f(x) = 0. It is not easy to find the roots of equations evolving nonlinearity using analytic methods. In general, the appropriate methods solving the non- linear equation f(x) = 0 are iterative methods. They are divided into two groups: open methods and bracketing methods. The open methods are relied on formulas requiring a single initial guess point or two initial guess points that do not necessarily bracket a real root. They may sometimes diverge from the root as the iteration progresses. Some of the known open methods are Secant method, Newton-Raphson method, and Muller's method. The bracketing methods require two initial guess points, a and b, that bracket or contain the root. The function also has the different parity at these 138 Somkid Intep two initial guesses i.e.
    [Show full text]
  • MAT 2310. Computational Mathematics
    MAT 2310. Computational Mathematics Wm C Bauldry Fall, 2012 Introduction to Computational Mathematics \1 + 1 = 3 for large enough values of 1." Introduction to Computational Mathematics Table of Contents I. Computer Arithmetic.......................................1 II. Control Structures.........................................27 S I. Special Topics: Computation Cost and Horner's Form....... 59 III. Numerical Differentiation.................................. 64 IV. Root Finding Algorithms................................... 77 S II. Special Topics: Modified Newton's Method................ 100 V. Numerical Integration.................................... 103 VI. Polynomial Interpolation.................................. 125 S III. Case Study: TI Calculator Numerics.......................146 VII. Projects................................................. 158 ICM i I. Computer Arithmetic Sections 1. Scientific Notation.............................................1 2. Converting to Different Bases...................................2 3. Floating Point Numbers........................................7 4. IEEE-754 Floating Point Standard..............................9 5. Maple's Floating Point Representation......................... 16 6. Error......................................................... 18 Exercises..................................................... 25 ICM ii II. Control Structures Sections 1. Control Structures............................................ 27 2. A Common Example.......................................... 33 3. Control
    [Show full text]
  • Solutions of Equations in One Variable Secant & Regula Falsi
    Solutions of Equations in One Variable Secant & Regula Falsi Methods Numerical Analysis (9th Edition) R L Burden & J D Faires Beamer Presentation Slides prepared by John Carroll Dublin City University c 2011 Brooks/Cole, Cengage Learning Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm 2 Comparing the Secant & Newton’s Methods 3 The Method of False Position (Regula Falsi) Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 2 / 25 Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm 2 Comparing the Secant & Newton’s Methods 3 The Method of False Position (Regula Falsi) Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 3 / 25 Secant Derivation Secant Example Regula Falsi Rationale for the Secant Method Problems with Newton’s Method Newton’s method is an extremely powerful technique, but it has a major weakness: the need to know the value of the derivative of f at each approximation. Frequently, f ′(x) is far more difficult and needs more arithmetic operations to calculate than f (x). Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 4 / 25 Secant Derivation Secant Example Regula Falsi Derivation of the Secant Method ′ f (x) − f (pn−1) f p − lim . ( n 1)= → x pn−1 x − pn−1 Circumvent the Derivative Evaluation If pn−2 is close to pn−1, then ′ f (pn−2) − f (pn−1) f (pn−1) − f (pn−2) f (pn−1) ≈ = . pn−2 − pn−1 pn−1 − pn−2 ′ Using this approximation for f (pn−1)
    [Show full text]
  • [0.125In]3.375In0.02In Secant & Regula Falsi Methods
    Solutions of Equations in One Variable Secant & Regula Falsi Methods Numerical Analysis (9th Edition) R L Burden & J D Faires Beamer Presentation Slides prepared by John Carroll Dublin City University c 2011 Brooks/Cole, Cengage Learning 2 Comparing the Secant & Newton’s Methods 3 The Method of False Position (Regula Falsi) Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 2 / 25 3 The Method of False Position (Regula Falsi) Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm 2 Comparing the Secant & Newton’s Methods Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 2 / 25 Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm 2 Comparing the Secant & Newton’s Methods 3 The Method of False Position (Regula Falsi) Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 2 / 25 Secant Derivation Secant Example Regula Falsi Outline 1 Secant Method: Derivation & Algorithm 2 Comparing the Secant & Newton’s Methods 3 The Method of False Position (Regula Falsi) Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 3 / 25 Newton’s method is an extremely powerful technique, but it has a major weakness: the need to know the value of the derivative of f at each approximation. Frequently, f 0(x) is far more difficult and needs more arithmetic operations to calculate than f (x). Secant Derivation Secant Example Regula Falsi Rationale for the Secant Method Problems with Newton’s Method Numerical Analysis (Chapter 2) Secant & Regula Falsi Methods R L Burden & J D Faires 4 / 25 Frequently, f 0(x) is far more difficult and needs more arithmetic operations to calculate than f (x).
    [Show full text]
  • Chapter 6 Nonlinear Equations
    Chapter 6 Nonlinear Equations 6.1 The Problem of Nonlinear Root-finding In this module we consider the problem of using numerical techniques to find the roots of nonlinear x = 0 equations, f . Initially we examine the case where the nonlinear equations are a scalar function of a single independent variable, x. Later, we shall consider the more difficult problem n f x= 0 where we have a system of n nonlinear equations in independent variables, . x = 0 Since the roots of a nonlinear equation, f , cannot in general be expressed in closed form, we must use approximate methods to solve the problem. Usually, iterative techniques are used to start from an initial approximation to a root, and produce a sequence of approximations 0 1 2 x ;x ;x ;::: ; which converge toward a root. Convergence to a root is usually possible provided that the function x f is sufficiently smooth and the initial approximation is close enough to the root. 6.2 Rate of Convergence Let 0 1 2 x ;x ;x ;::: c Department of Engineering Science, University of Auckland, New Zealand. All rights reserved. July 11, 2002 70 NONLINEAR EQUATIONS k k = x p be a sequence which converges to a root ,andlet . If there exists a number and a non-zero constant c such that k +1 lim = c; p (6.1) k k !1 j j p = 1; 2; 3 then p is called the order of convergence of the sequence. For the order is said to be linear, quadratic and cubic respectively. 6.3 Termination Criteria Since numerical computations are performed using floating point arithmetic, there will always be a finite precision to which a function can be evaluated.
    [Show full text]
  • Unit 3 Chord Methods for Finding Roots
    UNIT 3 CHORD METHODS FOR FINDING ROOTS Structure 3.1 Introduction Objectives 3.2 Regula-Falsi Method 3.3 Newton-Raphson Method 3.4 Convergence Criterion 3.5 Summary 3.1 INTRODUCTION In the last unit we introduced you to two iteration methods for finding roots of an equation . f(x) = 0. There we have shown that a root of the equation f(x) = 0 can be obtained by writing the equation in the form x = g(x). Using this form we generate a sequence of approximations x, + , = g(xi) for i = 0. 1,2, . We had also mentioned there that the success of the iteration methods depends upon the form of g(x) and the initial approximation xo. In this unit, we shall discuss two iteration methods : regula-falsi and Newton-Raphson methods. These methods produce results faster than bisection method. The first two sections of this unit deal with derivations and the use of these two methods. You will be able to appreciate these iteration methods better if you can compare the efficiency of these methods. With this In view we introduce fhe concept of convergence criterion which helps us to check the efficiency of each method. Sec 3.4 is devoted to the study of rate of convergence of different iterative methods. Objectives After studying the unit you should be able to : apply regula-falsi and secant methods for finding roots apply Newton-Raphson method for finding roots define 'order of convergence' of an iterative scheme 'obtain the order of convergence of the following four methods : i) bisection method ii) fixed point iteration mettiod iii) secant method iv) Newton-Raphson method 3.2 REGULA-FALSI METHOD (OR METHOD OF FALSE POSITION) In this section we shaH discuss the 'regula-falsi metbod'.
    [Show full text]
  • Blended Root Finding Algorithm Outperforms Bisection and Regula Falsi Algorithms
    Missouri University of Science and Technology Scholars' Mine Computer Science Faculty Research & Creative Works Computer Science 01 Nov 2019 Blended Root Finding Algorithm Outperforms Bisection and Regula Falsi Algorithms Chaman Sabharwal Missouri University of Science and Technology, [email protected] Follow this and additional works at: https://scholarsmine.mst.edu/comsci_facwork Part of the Computer Sciences Commons Recommended Citation C. Sabharwal, "Blended Root Finding Algorithm Outperforms Bisection and Regula Falsi Algorithms," Mathematics, vol. 7, no. 11, MDPI AG, Nov 2019. The definitive version is available at https://doi.org/10.3390/math7111118 This work is licensed under a Creative Commons Attribution 4.0 License. This Article - Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Computer Science Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [email protected]. mathematics Article Blended Root Finding Algorithm Outperforms Bisection and Regula Falsi Algorithms Chaman Lal Sabharwal Computer Science Department, Missouri University of Science and Technology, Rolla, MO 65409, USA; [email protected] Received: 5 October 2019; Accepted: 11 November 2019; Published: 16 November 2019 Abstract: Finding the roots of an equation is a fundamental problem in various fields, including numerical computing, social and physical sciences. Numerical techniques are used when an analytic solution is not available. There is not a single algorithm that works best for every function. We designed and implemented a new algorithm that is a dynamic blend of the bisection and regula falsi algorithms.
    [Show full text]
  • Chapter 1 Root Finding Algorithms
    Chapter 1 Root finding algorithms 1.1 Root finding problem In this chapter, we will discuss several general algorithms that find a solution of the following canonical equation f(x) = 0 (1.1) Here f(x) is any function of one scalar variable x1, and an x which satisfies the above equation is called a root of the function f. Often, we are given a problem which looks slightly different: h(x) = g(x) (1.2) But it is easy to transform to the canonical form with the following relabeling f(x) = h(x) − g(x) = 0 (1.3) Example 3x3 + 2 = sin x ! 3x3 + 2 − sin x = 0 (1.4) For some problems of this type, there are methods to find analytical or closed-form solu- tions. Recall, for example, the quadratic equation problem, which we discussed in detail in chapter4. Whenever it is possible, we should use the closed-form solutions. They are 1 Methods to solve a more general equation in the form f~(~x) = 0 are considered in chapter 13, which covers optimization. 1 usually exact, and their implementations are much faster. However, an analytical solution might not exist for a general equation, i.e. our problem is transcendental. Example The following equation is transcendental ex − 10x = 0 (1.5) We will formulate methods which are agnostic to the functional form of eq. (1.1) in the following text2. 1.2 Trial and error method Broadly speaking, all methods presented in this chapter are of the trial and error type. One can attempt to obtain the solution by just guessing it, and eventually he would succeed.
    [Show full text]
  • Learning to Teach Algebra
    THE USE OF THE HISTORY OF MATHEMATICS IN THE LEARNING AND TEACHING OF ALGEBRA The solution of algebraic equations: a historical approach Ercole CASTAGNOLA N.R.D. Dipartimento di Matematica Università “Federico II” NAPOLI (Italy) ABSTRACT The Ministry of Education (MPI) and the Italian Mathematical Union (UMI) have produced a teaching equipment (CD + videotapes) for the teaching of algebra. The present work reports the historical part of that teaching equipment. From a general point of view it is realised the presence of a “fil rouge” that follow all the history of algebra: the method of analysis and synthesis. Moreover many historical forms have been arranged to illustrate the main points of algebra development. These forms should help the secondary school student to get over the great difficulty in learning how to construct and solve equations and also the cognitive gap in the transition from arithmetic to algebra. All this work is in accordance with the recent research on the advantages and possibilities of using and implementing history of mathematics in the classroom that has led to a growing interest in the role of history of mathematics in the learning and teaching of mathematics. 1. Introduction We want to turn the attention to the subject of solution of algebraic equations through a historical approach: an example of the way the introduction of a historical view can change the practice of mathematical education. Such a subject is worked out through the explanation of meaningful problems, in the firm belief that there would never be a construction of mathematical knowledge, if there had been no problems to solve.
    [Show full text]
  • Unified Treatment of Regula Falsi, Newton–Raphson, Secant, And
    Journal of Online Mathematics and Its Applications Uni¯ed Treatment of Regula Falsi, Newton{Raphson, Secant, and Ste®ensen Methods for Nonlinear Equations Avram Sidi Computer Science Department Technion - Israel Institute of Technology Haifa 32000, Israel e-mail: [email protected] http://www.cs.technion.ac.il/ asidi May 2006 Abstract Regula falsi, Newton{Raphson, secant, and Ste®ensen methods are four very e®ec- tive numerical procedures used for solving nonlinear equations of the form f(x) = 0. They are derived via linear interpolation procedures. Their analyses can be carried out by making use of interpolation theory through divided di®erences and Newton's interpolation formula. In this note, we unify these analyses. The analysis of the Stef- fensen method given here seems to be new and is especially simpler than the standard treatments. The contents of this note should also be a useful exercise/example in the application of polynomial interpolation and divided di®erences in introductory courses in numerical analysis. 1 Introduction Let ® be the solution to the equation f(x) = 0; (1) and assume that f(x) is twice continuously di®erentiable in a closed interval I containing ® in its interior. Some iterative methods used for solving (1) and that make direct use of f(x) are the regula falsi method (or false position method), the secant method, the Newton{ Raphson method, and the Ste®ensen method. These methods are discussed in many books on numerical analysis. See, for example, Atkinson [1], Henrici [2], Ralston and Rabinowitz [3], and Stoer and Bulirsch [5]. 8 Y y = f(x) 6 Regula Falsi Method 4 2 x x x x α 2 x 0 0 3 4 1 X −2 −4 −6 −8 −1 −0.5 0 0.5 1 1.5 2 2.5 Figure 1: The regula falsi method.
    [Show full text]
  • UNIT - I Solution of Algebraic and Transcendental Equations
    UNIT - I Solution of Algebraic and Transcendental Equations Solution of Algebraic and Transcendental Equations Bisection Method Method of False Position The Iteration Method Newton Raphson Method Summary Solved University Questions (JNTU) Objective Type Questions 2 Engineering Mathematics - III 1.1 Solution of Algebraic and Transcendental Equations 1.1.1 Introduction A polynomial equation of the form n–1 n–1 n–2 f (x) = pn (x) = a0 x + a1 x + a2 x + … + an–1 x + an = 0 …..(1) is called an Algebraic equation. For example, x4 – 4x2 + 5 = 0, 4x2 – 5x + 7 = 0; 2x3 – 5x2 + 7x + 5 = 0 are algebraic equations. An equation which contains polynomials, trigonometric functions, logarithmic functions, exponential functions etc., is called a Transcendental equation. For example, tan x – ex = 0; sin x – xe2x = 0; x ex = cos x are transcendental equations. Finding the roots or zeros of an equation of the form f(x) = 0 is an important problem in science and engineering. We assume that f (x) is continuous in the required interval. A root of an equation f (x) = 0 is the value of x, say x = for which f () = 0. Geometrically, a root of an equation f (x) = 0 is the value of x at which the graph of the equation y = f (x) intersects the x – axis (see Fig. 1) Fig. 1 Geometrical Interpretation of a root of f (x) = 0 A number is a simple root of f (x) = 0; if f () = 0 and f ' ( α ) 0 . Then, we can write f (x) as, f (x) = (x – ) g(x), g() 0 …..(2) A number is a multiple root of multiplicity m of f (x) = 0, if f () = f 1() = ...
    [Show full text]