5.1 Polynomial Functions Interval Notation: Sets of Real Numbers That Make up Portions of the Number Line Can Be Represented by Intervals
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A Computational Approach to Solve a System of Transcendental Equations with Multi-Functions and Multi-Variables
mathematics Article A Computational Approach to Solve a System of Transcendental Equations with Multi-Functions and Multi-Variables Chukwuma Ogbonnaya 1,2,* , Chamil Abeykoon 3 , Adel Nasser 1 and Ali Turan 4 1 Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester M13 9PL, UK; [email protected] 2 Faculty of Engineering and Technology, Alex Ekwueme Federal University, Ndufu Alike Ikwo, Abakaliki PMB 1010, Nigeria 3 Aerospace Research Institute and Northwest Composites Centre, School of Materials, The University of Manchester, Manchester M13 9PL, UK; [email protected] 4 Independent Researcher, Manchester M22 4ES, Lancashire, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-(0)74-3850-3799 Abstract: A system of transcendental equations (SoTE) is a set of simultaneous equations containing at least a transcendental function. Solutions involving transcendental equations are often problematic, particularly in the form of a system of equations. This challenge has limited the number of equations, with inter-related multi-functions and multi-variables, often included in the mathematical modelling of physical systems during problem formulation. Here, we presented detailed steps for using a code- based modelling approach for solving SoTEs that may be encountered in science and engineering problems. A SoTE comprising six functions, including Sine-Gordon wave functions, was used to illustrate the steps. Parametric studies were performed to visualize how a change in the variables Citation: Ogbonnaya, C.; Abeykoon, affected the superposition of the waves as the independent variable varies from x1 = 1:0.0005:100 to C.; Nasser, A.; Turan, A. -
Math 1232-04F (Survey of Calculus) Dr. J.S. Zheng Chapter R. Functions
Math 1232-04F (Survey of Calculus) Dr. J.S. Zheng Chapter R. Functions, Graphs, and Models R.4 Slope and Linear Functions R.5* Nonlinear Functions and Models R.6 Exponential and Logarithmic Functions R.7* Mathematical Modeling and Curve Fitting • Linear Functions (11) Graph the following equations. Determine if they are functions. (a) y = 2 (b) x = 2 (c) y = 3x (d) y = −2x + 4 (12) Definition. The variable y is directly proportional to x (or varies directly with x) if there is some positive constant m such that y = mx. We call m the constant of proportionality, or variation constant. (13) The weight M of a person's muscles is directly proportional to the person's body weight W . It is known that a person weighing 200 lb has 80 lb of muscle. (a) Find an equation of variation expressing M as a function of W . (b) What is the muscle weight of a person weighing 120 lb? (14) Definition. A linear function is any function that can be written in the form y = mx + b or f(x) = mx + b, called the slope-intercept equation of a line. The constant m is called the slope. The point (0; b) is called the y-intercept. (15) Find the slope and y-intercept of the graph of 3x + 5y − 2 = 0. (16) Find an equation of the line that has slope 4 and passes through the point (−1; 1). (17) Definition. The equation y − y1 = m(x − x1) is called the point-slope equation of a line. The point is (x1; y1), and the slope is m. -
Algorithmic Factorization of Polynomials Over Number Fields
Rose-Hulman Institute of Technology Rose-Hulman Scholar Mathematical Sciences Technical Reports (MSTR) Mathematics 5-18-2017 Algorithmic Factorization of Polynomials over Number Fields Christian Schulz Rose-Hulman Institute of Technology Follow this and additional works at: https://scholar.rose-hulman.edu/math_mstr Part of the Number Theory Commons, and the Theory and Algorithms Commons Recommended Citation Schulz, Christian, "Algorithmic Factorization of Polynomials over Number Fields" (2017). Mathematical Sciences Technical Reports (MSTR). 163. https://scholar.rose-hulman.edu/math_mstr/163 This Dissertation is brought to you for free and open access by the Mathematics at Rose-Hulman Scholar. It has been accepted for inclusion in Mathematical Sciences Technical Reports (MSTR) by an authorized administrator of Rose-Hulman Scholar. For more information, please contact [email protected]. Algorithmic Factorization of Polynomials over Number Fields Christian Schulz May 18, 2017 Abstract The problem of exact polynomial factorization, in other words expressing a poly- nomial as a product of irreducible polynomials over some field, has applications in algebraic number theory. Although some algorithms for factorization over algebraic number fields are known, few are taught such general algorithms, as their use is mainly as part of the code of various computer algebra systems. This thesis provides a summary of one such algorithm, which the author has also fully implemented at https://github.com/Whirligig231/number-field-factorization, along with an analysis of the runtime of this algorithm. Let k be the product of the degrees of the adjoined elements used to form the algebraic number field in question, let s be the sum of the squares of these degrees, and let d be the degree of the polynomial to be factored; then the runtime of this algorithm is found to be O(d4sk2 + 2dd3). -
January 10, 2010 CHAPTER SIX IRREDUCIBILITY and FACTORIZATION §1. BASIC DIVISIBILITY THEORY the Set of Polynomials Over a Field
January 10, 2010 CHAPTER SIX IRREDUCIBILITY AND FACTORIZATION §1. BASIC DIVISIBILITY THEORY The set of polynomials over a field F is a ring, whose structure shares with the ring of integers many characteristics. A polynomials is irreducible iff it cannot be factored as a product of polynomials of strictly lower degree. Otherwise, the polynomial is reducible. Every linear polynomial is irreducible, and, when F = C, these are the only ones. When F = R, then the only other irreducibles are quadratics with negative discriminants. However, when F = Q, there are irreducible polynomials of arbitrary degree. As for the integers, we have a division algorithm, which in this case takes the form that, if f(x) and g(x) are two polynomials, then there is a quotient q(x) and a remainder r(x) whose degree is less than that of g(x) for which f(x) = q(x)g(x) + r(x) . The greatest common divisor of two polynomials f(x) and g(x) is a polynomial of maximum degree that divides both f(x) and g(x). It is determined up to multiplication by a constant, and every common divisor divides the greatest common divisor. These correspond to similar results for the integers and can be established in the same way. One can determine a greatest common divisor by the Euclidean algorithm, and by going through the equations in the algorithm backward arrive at the result that there are polynomials u(x) and v(x) for which gcd (f(x), g(x)) = u(x)f(x) + v(x)g(x) . -
Domain and Range of a Function
4.1 Domain and Range of a Function How can you fi nd the domain and range of STATES a function? STANDARDS MA.8.A.1.1 MA.8.A.1.5 1 ACTIVITY: The Domain and Range of a Function Work with a partner. The table shows the number of adult and child tickets sold for a school concert. Input Number of Adult Tickets, x 01234 Output Number of Child Tickets, y 86420 The variables x and y are related by the linear equation 4x + 2y = 16. a. Write the equation in function form by solving for y. b. The domain of a function is the set of all input values. Find the domain of the function. Domain = Why is x = 5 not in the domain of the function? 1 Why is x = — not in the domain of the function? 2 c. The range of a function is the set of all output values. Find the range of the function. Range = d. Functions can be described in many ways. ● by an equation ● by an input-output table y 9 ● in words 8 ● by a graph 7 6 ● as a set of ordered pairs 5 4 Use the graph to write the function 3 as a set of ordered pairs. 2 1 , , ( , ) ( , ) 0 09321 45 876 x ( , ) , ( , ) , ( , ) 148 Chapter 4 Functions 2 ACTIVITY: Finding Domains and Ranges Work with a partner. ● Copy and complete each input-output table. ● Find the domain and range of the function represented by the table. 1 a. y = −3x + 4 b. y = — x − 6 2 x −2 −10 1 2 x 01234 y y c. -
2.4 Algebra of Polynomials ([1], P.136-142) in This Section We Will Give a Brief Introduction to the Algebraic Properties of the Polynomial Algebra C[T]
2.4 Algebra of polynomials ([1], p.136-142) In this section we will give a brief introduction to the algebraic properties of the polynomial algebra C[t]. In particular, we will see that C[t] admits many similarities to the algebraic properties of the set of integers Z. Remark 2.4.1. Let us first recall some of the algebraic properties of the set of integers Z. - division algorithm: given two integers w, z 2 Z, with jwj ≤ jzj, there exist a, r 2 Z, with 0 ≤ r < jwj such that z = aw + r. Moreover, the `long division' process allows us to determine a, r. Here r is the `remainder'. - prime factorisation: for any z 2 Z we can write a1 a2 as z = ±p1 p2 ··· ps , where pi are prime numbers. Moreover, this expression is essentially unique - it is unique up to ordering of the primes appearing. - Euclidean algorithm: given integers w, z 2 Z there exists a, b 2 Z such that aw + bz = gcd(w, z), where gcd(w, z) is the `greatest common divisor' of w and z. In particular, if w, z share no common prime factors then we can write aw + bz = 1. The Euclidean algorithm is a process by which we can determine a, b. We will now introduce the polynomial algebra in one variable. This is simply the set of all polynomials with complex coefficients and where we make explicit the C-vector space structure and the multiplicative structure that this set naturally exhibits. Definition 2.4.2. - The C-algebra of polynomials in one variable, is the quadruple (C[t], α, σ, µ)43 where (C[t], α, σ) is the C-vector space of polynomials in t with C-coefficients defined in Example 1.2.6, and µ : C[t] × C[t] ! C[t];(f , g) 7! µ(f , g), is the `multiplication' function. -
Write the Function in Standard Form
Write The Function In Standard Form Bealle often suppurates featly when active Davidson lopper fleetly and ray her paedogenesis. Tressed Jesse still outmaneuvers: clinometric and georgic Augie diphthongises quite dirtily but mistitling her indumentum sustainedly. If undefended or gobioid Allen usually pulsate his Orientalism miming jauntily or blow-up stolidly and headfirst, how Alhambresque is Gustavo? Now the vertex always sits exactly smack dab between the roots, when you do have roots. For the two sides to be equal, the corresponding coefficients must be equal. So, changing the value of p vertically stretches or shrinks the parabola. To save problems you must sign in. This short tutorial helps you learn how to find vertex, focus, and directrix of a parabola equation with an example using the formulas. The draft was successfully published. To determine the domain and range of any function on a graph, the general idea is to assume that they are both real numbers, then look for places where no values exist. For our purposes, this is close enough. English has also become the most widely used second language. Simplify the radical, but notice that the number under the radical symbol is negative! On this lesson, you fill learn how to graph a quadratic function, find the axis of symmetry, vertex, and the x intercepts and y intercepts of a parabolawi. Be sure to write the terms with the exponent on the variable in descending order. Wendler Polynomial Webquest Introduction: By the end of this webquest, you will have a deeper understanding of polynomials. Anyone can ask a math question, and most questions get answers! Follow along with the highlighted text while you listen! And if I have an upward opening parabola, the vertex is going to be the minimum point. -
Module 1 Lecture Notes
Module 1 Lecture Notes Contents 1.1 Identifying Functions.............................1 1.2 Algebraically Determining the Domain of a Function..........4 1.3 Evaluating Functions.............................6 1.4 Function Operations..............................7 1.5 The Difference Quotient...........................9 1.6 Applications of Function Operations.................... 10 1.7 Determining the Domain and Range of a Function Graphically.... 12 1.8 Reading the Graph of a Function...................... 14 1.1 Identifying Functions In previous classes, you should have studied a variety basic functions. For example, 1 p f(x) = 3x − 5; g(x) = 2x2 − 1; h(x) = ; j(x) = 5x + 2 x − 5 We will begin this course by studying functions and their properties. As the course progresses, we will study inverse, composite, exponential, logarithmic, polynomial and rational functions. Math 111 Module 1 Lecture Notes Definition 1: A relation is a correspondence between two variables. A relation can be ex- pressed through a set of ordered pairs, a graph, a table, or an equation. A set containing ordered pairs (x; y) defines y as a function of x if and only if no two ordered pairs in the set have the same x-coordinate. In other words, every input maps to exactly one output. We write y = f(x) and say \y is a function of x." For the function defined by y = f(x), • x is the independent variable (also known as the input) • y is the dependent variable (also known as the output) • f is the function name Example 1: Determine whether or not each of the following represents a function. Table 1.1 Chicken Name Egg Color Emma Turquoise Hazel Light Brown George(ia) Chocolate Brown Isabella White Yvonne Light Brown (a) The set of ordered pairs of the form (chicken name, egg color) shown in Table 1.1. -
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 -
Finding Equations of Polynomial Functions with Given Zeros
Finding Equations of Polynomial Functions with Given Zeros 푛 푛−1 2 Polynomials are functions of general form 푃(푥) = 푎푛푥 + 푎푛−1 푥 + ⋯ + 푎2푥 + 푎1푥 + 푎0 ′ (푛 ∈ 푤ℎ표푙푒 # 푠) Polynomials can also be written in factored form 푃(푥) = 푎(푥 − 푧1)(푥 − 푧2) … (푥 − 푧푖) (푎 ∈ ℝ) Given a list of “zeros”, it is possible to find a polynomial function that has these specific zeros. In fact, there are multiple polynomials that will work. In order to determine an exact polynomial, the “zeros” and a point on the polynomial must be provided. Examples: Practice finding polynomial equations in general form with the given zeros. Find an* equation of a polynomial with the Find the equation of a polynomial with the following two zeros: 푥 = −2, 푥 = 4 following zeroes: 푥 = 0, −√2, √2 that goes through the point (−2, 1). Denote the given zeros as 푧1 푎푛푑 푧2 Denote the given zeros as 푧1, 푧2푎푛푑 푧3 Step 1: Start with the factored form of a polynomial. Step 1: Start with the factored form of a polynomial. 푃(푥) = 푎(푥 − 푧1)(푥 − 푧2) 푃(푥) = 푎(푥 − 푧1)(푥 − 푧2)(푥 − 푧3) Step 2: Insert the given zeros and simplify. Step 2: Insert the given zeros and simplify. 푃(푥) = 푎(푥 − (−2))(푥 − 4) 푃(푥) = 푎(푥 − 0)(푥 − (−√2))(푥 − √2) 푃(푥) = 푎(푥 + 2)(푥 − 4) 푃(푥) = 푎푥(푥 + √2)(푥 − √2) Step 3: Multiply the factored terms together. Step 3: Multiply the factored terms together 푃(푥) = 푎(푥2 − 2푥 − 8) 푃(푥) = 푎(푥3 − 2푥) Step 4: The answer can be left with the generic “푎”, or a value for “푎”can be chosen, Step 4: Insert the given point (−2, 1) to inserted, and distributed. -
The Church-Turing Thesis in Quantum and Classical Computing∗
The Church-Turing thesis in quantum and classical computing∗ Petrus Potgietery 31 October 2002 Abstract The Church-Turing thesis is examined in historical context and a survey is made of current claims to have surpassed its restrictions using quantum computing | thereby calling into question the basis of the accepted solutions to the Entscheidungsproblem and Hilbert's tenth problem. 1 Formal computability The need for a formal definition of algorithm or computation became clear to the scientific community of the early twentieth century due mainly to two problems of Hilbert: the Entscheidungsproblem|can an algorithm be found that, given a statement • in first-order logic (for example Peano arithmetic), determines whether it is true in all models of a theory; and Hilbert's tenth problem|does there exist an algorithm which, given a Diophan- • tine equation, determines whether is has any integer solutions? The Entscheidungsproblem, or decision problem, can be traced back to Leibniz and was successfully and independently solved in the mid-1930s by Alonzo Church [Church, 1936] and Alan Turing [Turing, 1935]. Church defined an algorithm to be identical to that of a function computable by his Lambda Calculus. Turing, in turn, first identified an algorithm to be identical with a recursive function and later with a function computed by what is now known as a Turing machine1. It was later shown that the class of the recursive functions, Turing machines, and the Lambda Calculus define the same class of functions. The remarkable equivalence of these three definitions of disparate origin quite strongly supported the idea of this as an adequate definition of computability and the Entscheidungsproblem is generally considered to be solved. -
Lesson 1: Multiplying and Factoring Polynomial Expressions
NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 1 M4 ALGEBRA I Lesson 1: Multiplying and Factoring Polynomial Expressions Classwork Opening Exercise Write expressions for the areas of the two rectangles in the figures given below. 8 2 2 Now write an expression for the area of this rectangle: 8 2 Example 1 The total area of this rectangle is represented by 3a + 3a. Find expressions for the dimensions of the total rectangle. 2 3 + 3 square units 2 푎 푎 Lesson 1: Multiplying and Factoring Polynomial Expressions Date: 2/2/14 S.1 This work is licensed under a © 2014 Common Core, Inc. Some rights reserved. commoncore.org Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. NYS COMMON CORE MATHEMATICS CURRICULUM Lesson 1 M4 ALGEBRA I Exercises 1–3 Factor each by factoring out the Greatest Common Factor: 1. 10 + 5 푎푏 푎 2. 3 9 + 3 2 푔 ℎ − 푔 ℎ 12ℎ 3. 6 + 9 + 18 2 3 4 5 푦 푦 푦 Discussion: Language of Polynomials A prime number is a positive integer greater than 1 whose only positive integer factors are 1 and itself. A composite number is a positive integer greater than 1 that is not a prime number. A composite number can be written as the product of positive integers with at least one factor that is not 1 or itself. For example, the prime number 7 has only 1 and 7 as its factors. The composite number 6 has factors of 1, 2, 3, and 6; it could be written as the product 2 3.