CYCLIC RESULTANTS 1. Introduction the M-Th Cyclic Resultant of A
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Partial Fractions Decompositions (Includes the Coverup Method)
Partial Fractions and the Coverup Method 18.031 Haynes Miller and Jeremy Orloff *Much of this note is freely borrowed from an MIT 18.01 note written by Arthur Mattuck. 1 Heaviside Cover-up Method 1.1 Introduction The cover-up method was introduced by Oliver Heaviside as a fast way to do a decomposition into partial fractions. This is an essential step in using the Laplace transform to solve differential equations, and this was more or less Heaviside's original motivation. The cover-up method can be used to make a partial fractions decomposition of a proper p(s) rational function whenever the denominator can be factored into distinct linear factors. q(s) Note: We put this section first, because the coverup method is so useful and many people have not seen it. Some of the later examples rely on the full algebraic method of undeter- mined coefficients presented in the next section. If you have never seen partial fractions you should read that section first. 1.2 Linear Factors We first show how the method works on a simple example, and then show why it works. s − 7 Example 1. Decompose into partial fractions. (s − 1)(s + 2) answer: We know the answer will have the form s − 7 A B = + : (1) (s − 1)(s + 2) s − 1 s + 2 To determine A by the cover-up method, on the left-hand side we mentally remove (or cover up with a finger) the factor s − 1 associated with A, and substitute s = 1 into what's left; this gives A: s − 7 1 − 7 = = −2 = A: (2) (s + 2) s=1 1 + 2 Similarly, B is found by covering up the factor s + 2 on the left, and substituting s = −2 into what's left. -
Discriminant Analysis
Chapter 5 Discriminant Analysis This chapter considers discriminant analysis: given p measurements w, we want to correctly classify w into one of G groups or populations. The max- imum likelihood, Bayesian, and Fisher’s discriminant rules are used to show why methods like linear and quadratic discriminant analysis can work well for a wide variety of group distributions. 5.1 Introduction Definition 5.1. In supervised classification, there are G known groups and m test cases to be classified. Each test case is assigned to exactly one group based on its measurements wi. Suppose there are G populations or groups or classes where G 2. Assume ≥ that for each population there is a probability density function (pdf) fj(z) where z is a p 1 vector and j =1,...,G. Hence if the random vector x comes × from population j, then x has pdf fj (z). Assume that there is a random sam- ple of nj cases x1,j,..., xnj ,j for each group. Let (xj , Sj ) denote the sample mean and covariance matrix for each group. Let wi be a new p 1 (observed) random vector from one of the G groups, but the group is unknown.× Usually there are many wi, and discriminant analysis (DA) or classification attempts to allocate the wi to the correct groups. The w1,..., wm are known as the test data. Let πk = the (prior) probability that a randomly selected case wi belongs to the kth group. If x1,1..., xnG,G are a random sample of cases from G the collection of G populations, thenπ ˆk = nk/n where n = i=1 ni. -
Solving Quadratic Equations by Factoring
5.2 Solving Quadratic Equations by Factoring Goals p Factor quadratic expressions and solve quadratic equations by factoring. p Find zeros of quadratic functions. Your Notes VOCABULARY Binomial An expression with two terms, such as x ϩ 1 Trinomial An expression with three terms, such as x2 ϩ x ϩ 1 Factoring A process used to write a polynomial as a product of other polynomials having equal or lesser degree Monomial An expression with one term, such as 3x Quadratic equation in one variable An equation that can be written in the form ax2 ϩ bx ϩ c ϭ 0 where a 0 Zero of a function A number k is a zero of a function f if f(k) ϭ 0. Example 1 Factoring a Trinomial of the Form x2 ؉ bx ؉ c Factor x2 Ϫ 9x ϩ 14. Solution You want x2 Ϫ 9x ϩ 14 ϭ (x ϩ m)(x ϩ n) where mn ϭ 14 and m ϩ n ϭ Ϫ9 . Factors of 14 1, Ϫ1, Ϫ 2, Ϫ2, Ϫ (m, n) 14 14 7 7 Sum of factors Ϫ Ϫ m ؉ n) 15 15 9 9) The table shows that m ϭ Ϫ2 and n ϭ Ϫ7 . So, x2 Ϫ 9x ϩ 14 ϭ ( x Ϫ 2 )( x Ϫ 7 ). Lesson 5.2 • Algebra 2 Notetaking Guide 97 Your Notes Example 2 Factoring a Trinomial of the Form ax2 ؉ bx ؉ c Factor 2x2 ϩ 13x ϩ 6. Solution You want 2x2 ϩ 13x ϩ 6 ϭ (kx ϩ m)(lx ϩ n) where k and l are factors of 2 and m and n are ( positive ) factors of 6 . -
Alicia Dickenstein
A WORLD OF BINOMIALS ——————————– ALICIA DICKENSTEIN Universidad de Buenos Aires FOCM 2008, Hong Kong - June 21, 2008 A. Dickenstein - FoCM 2008, Hong Kong – p.1/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) BINOMIALS AND RECURRENCE OF HYPERGEOMETRIC COEFFICIENTS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 ORIGINAL PLAN OF THE TALK BASICS ON BINOMIALS COUNTING SOLUTIONS TO BINOMIAL SYSTEMS DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) BINOMIALS AND RECURRENCE OF HYPERGEOMETRIC COEFFICIENTS BINOMIALS AND MASS ACTION KINETICS DYNAMICS A. Dickenstein - FoCM 2008, Hong Kong – p.2/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials A. Dickenstein - FoCM 2008, Hong Kong – p.3/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials COUNTING SOLUTIONS TO BINOMIAL SYSTEMS - with a touch of complexity A. Dickenstein - FoCM 2008, Hong Kong – p.3/39 RESCHEDULED PLAN OF THE TALK BASICS ON BINOMIALS - How binomials “sit” in the polynomial world and the main “secrets” about binomials COUNTING SOLUTIONS TO BINOMIAL SYSTEMS - with a touch of complexity (Mixed) DISCRIMINANTS (DUALS OF BINOMIAL VARIETIES) and an application to real roots A. -
Z-Transform Part 2 February 23, 2017 1 / 38 the Z-Transform and Its Application to the Analysis of LTI Systems
ELC 4351: Digital Signal Processing Liang Dong Electrical and Computer Engineering Baylor University liang [email protected] February 23, 2017 Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 1 / 38 The z-Transform and Its Application to the Analysis of LTI Systems 1 Rational z-Transform 2 Inversion of the z-Transform 3 Analysis of LTI Systems in the z-Domain 4 Causality and Stability Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 2 / 38 Rational z-Transforms X (z) is a rational function, that is, a ratio of two polynomials in z−1 (or z). B(z) X (z) = A(z) −1 −M b0 + b1z + ··· + bM z = −1 −N a0 + a1z + ··· aN z PM b z−k = k=0 k PN −k k=0 ak z Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 3 / 38 Rational z-Transforms X (z) is a rational function, that is, a ratio of two polynomials B(z) and A(z). The polynomials can be expressed in factored forms. B(z) X (z) = A(z) b (z − z )(z − z ) ··· (z − z ) = 0 z−M+N 1 2 M a0 (z − p1)(z − p2) ··· (z − pN ) b QM (z − z ) = 0 zN−M k=1 k a QN 0 k=1(z − pk ) Liang Dong (Baylor University) z-Transform Part 2 February 23, 2017 4 / 38 Poles and Zeros The zeros of a z-transform X (z) are the values of z for which X (z) = 0. The poles of a z-transform X (z) are the values of z for which X (z) = 1. -
Lesson 21 –Resultants
Lesson 21 –Resultants Elimination theory may be considered the origin of algebraic geometry; its history can be traced back to Newton (for special equations) and to Euler and Bezout. The classical method for computing varieties has been through the use of resultants, the focus of today’s discussion. The alternative of using Groebner bases is more recent, becoming fashionable with the onset of computers. Calculations with Groebner bases may reach their limitations quite quickly, however, so resultants remain useful in elimination theory. The proof of the Extension Theorem provided in the textbook (see pages 164-165) also makes good use of resultants. I. The Resultant Let be a UFD and let be the polynomials . The definition and motivation for the resultant of lies in the following very simple exercise. Exercise 1 Show that two polynomials and in have a nonconstant common divisor in if and only if there exist nonzero polynomials u, v such that where and . The equation can be turned into a numerical criterion for and to have a common factor. Actually, we use the equation instead, because the criterion turns out to be a little cleaner (as there are fewer negative signs). Observe that iff so has a solution iff has a solution . Exercise 2 (to be done outside of class) Given polynomials of positive degree, say , define where the l + m coefficients , ,…, , , ,…, are treated as unknowns. Substitute the formulas for f, g, u and v into the equation and compare coefficients of powers of x to produce the following system of linear equations with unknowns ci, di and coefficients ai, bj, in R. -
The Diamond Method of Factoring a Quadratic Equation
The Diamond Method of Factoring a Quadratic Equation Important: Remember that the first step in any factoring is to look at each term and factor out the greatest common factor. For example: 3x2 + 6x + 12 = 3(x2 + 2x + 4) AND 5x2 + 10x = 5x(x + 2) If the leading coefficient is negative, always factor out the negative. For example: -2x2 - x + 1 = -1(2x2 + x - 1) = -(2x2 + x - 1) Using the Diamond Method: Example 1 2 Factor 2x + 11x + 15 using the Diamond Method. +30 Step 1: Multiply the coefficient of the x2 term (+2) and the constant (+15) and place this product (+30) in the top quarter of a large “X.” Step 2: Place the coefficient of the middle term in the bottom quarter of the +11 “X.” (+11) Step 3: List all factors of the number in the top quarter of the “X.” +30 (+1)(+30) (-1)(-30) (+2)(+15) (-2)(-15) (+3)(+10) (-3)(-10) (+5)(+6) (-5)(-6) +30 Step 4: Identify the two factors whose sum gives the number in the bottom quarter of the “x.” (5 ∙ 6 = 30 and 5 + 6 = 11) and place these factors in +5 +6 the left and right quarters of the “X” (order is not important). +11 Step 5: Break the middle term of the original trinomial into the sum of two terms formed using the right and left quarters of the “X.” That is, write the first term of the original equation, 2x2 , then write 11x as + 5x + 6x (the num bers from the “X”), and finally write the last term of the original equation, +15 , to get the following 4-term polynomial: 2x2 + 11x + 15 = 2x2 + 5x + 6x + 15 Step 6: Factor by Grouping: Group the first two terms together and the last two terms together. -
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). -
SOME ALGEBRAIC DEFINITIONS and CONSTRUCTIONS Definition
SOME ALGEBRAIC DEFINITIONS AND CONSTRUCTIONS Definition 1. A monoid is a set M with an element e and an associative multipli- cation M M M for which e is a two-sided identity element: em = m = me for all m M×. A−→group is a monoid in which each element m has an inverse element m−1, so∈ that mm−1 = e = m−1m. A homomorphism f : M N of monoids is a function f such that f(mn) = −→ f(m)f(n) and f(eM )= eN . A “homomorphism” of any kind of algebraic structure is a function that preserves all of the structure that goes into the definition. When M is commutative, mn = nm for all m,n M, we often write the product as +, the identity element as 0, and the inverse of∈m as m. As a convention, it is convenient to say that a commutative monoid is “Abelian”− when we choose to think of its product as “addition”, but to use the word “commutative” when we choose to think of its product as “multiplication”; in the latter case, we write the identity element as 1. Definition 2. The Grothendieck construction on an Abelian monoid is an Abelian group G(M) together with a homomorphism of Abelian monoids i : M G(M) such that, for any Abelian group A and homomorphism of Abelian monoids−→ f : M A, there exists a unique homomorphism of Abelian groups f˜ : G(M) A −→ −→ such that f˜ i = f. ◦ We construct G(M) explicitly by taking equivalence classes of ordered pairs (m,n) of elements of M, thought of as “m n”, under the equivalence relation generated by (m,n) (m′,n′) if m + n′ = −n + m′. -
Factoring Polynomials
2/13/2013 Chapter 13 § 13.1 Factoring The Greatest Common Polynomials Factor Chapter Sections Factors 13.1 – The Greatest Common Factor Factors (either numbers or polynomials) 13.2 – Factoring Trinomials of the Form x2 + bx + c When an integer is written as a product of 13.3 – Factoring Trinomials of the Form ax 2 + bx + c integers, each of the integers in the product is a factor of the original number. 13.4 – Factoring Trinomials of the Form x2 + bx + c When a polynomial is written as a product of by Grouping polynomials, each of the polynomials in the 13.5 – Factoring Perfect Square Trinomials and product is a factor of the original polynomial. Difference of Two Squares Factoring – writing a polynomial as a product of 13.6 – Solving Quadratic Equations by Factoring polynomials. 13.7 – Quadratic Equations and Problem Solving Martin-Gay, Developmental Mathematics 2 Martin-Gay, Developmental Mathematics 4 1 2/13/2013 Greatest Common Factor Greatest Common Factor Greatest common factor – largest quantity that is a Example factor of all the integers or polynomials involved. Find the GCF of each list of numbers. 1) 6, 8 and 46 6 = 2 · 3 Finding the GCF of a List of Integers or Terms 8 = 2 · 2 · 2 1) Prime factor the numbers. 46 = 2 · 23 2) Identify common prime factors. So the GCF is 2. 3) Take the product of all common prime factors. 2) 144, 256 and 300 144 = 2 · 2 · 2 · 3 · 3 • If there are no common prime factors, GCF is 1. 256 = 2 · 2 · 2 · 2 · 2 · 2 · 2 · 2 300 = 2 · 2 · 3 · 5 · 5 So the GCF is 2 · 2 = 4. -
Algebraic Number Theory
Algebraic Number Theory William B. Hart Warwick Mathematics Institute Abstract. We give a short introduction to algebraic number theory. Algebraic number theory is the study of extension fields Q(α1; α2; : : : ; αn) of the rational numbers, known as algebraic number fields (sometimes number fields for short), in which each of the adjoined complex numbers αi is algebraic, i.e. the root of a polynomial with rational coefficients. Throughout this set of notes we use the notation Z[α1; α2; : : : ; αn] to denote the ring generated by the values αi. It is the smallest ring containing the integers Z and each of the αi. It can be described as the ring of all polynomial expressions in the αi with integer coefficients, i.e. the ring of all expressions built up from elements of Z and the complex numbers αi by finitely many applications of the arithmetic operations of addition and multiplication. The notation Q(α1; α2; : : : ; αn) denotes the field of all quotients of elements of Z[α1; α2; : : : ; αn] with nonzero denominator, i.e. the field of rational functions in the αi, with rational coefficients. It is the smallest field containing the rational numbers Q and all of the αi. It can be thought of as the field of all expressions built up from elements of Z and the numbers αi by finitely many applications of the arithmetic operations of addition, multiplication and division (excepting of course, divide by zero). 1 Algebraic numbers and integers A number α 2 C is called algebraic if it is the root of a monic polynomial n n−1 n−2 f(x) = x + an−1x + an−2x + ::: + a1x + a0 = 0 with rational coefficients ai. -
Arxiv:2004.03341V1
RESULTANTS OVER PRINCIPAL ARTINIAN RINGS CLAUS FIEKER, TOMMY HOFMANN, AND CARLO SIRCANA Abstract. The resultant of two univariate polynomials is an invariant of great impor- tance in commutative algebra and vastly used in computer algebra systems. Here we present an algorithm to compute it over Artinian principal rings with a modified version of the Euclidean algorithm. Using the same strategy, we show how the reduced resultant and a pair of B´ezout coefficient can be computed. Particular attention is devoted to the special case of Z/nZ, where we perform a detailed analysis of the asymptotic cost of the algorithm. Finally, we illustrate how the algorithms can be exploited to improve ideal arithmetic in number fields and polynomial arithmetic over p-adic fields. 1. Introduction The computation of the resultant of two univariate polynomials is an important task in computer algebra and it is used for various purposes in algebraic number theory and commutative algebra. It is well-known that, over an effective field F, the resultant of two polynomials of degree at most d can be computed in O(M(d) log d) ([vzGG03, Section 11.2]), where M(d) is the number of operations required for the multiplication of poly- nomials of degree at most d. Whenever the coefficient ring is not a field (or an integral domain), the method to compute the resultant is given directly by the definition, via the determinant of the Sylvester matrix of the polynomials; thus the problem of determining the resultant reduces to a problem of linear algebra, which has a worse complexity.