7. Algebraic Equations
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Formal Power Series - Wikipedia, the Free Encyclopedia
Formal power series - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Formal_power_series Formal power series From Wikipedia, the free encyclopedia In mathematics, formal power series are a generalization of polynomials as formal objects, where the number of terms is allowed to be infinite; this implies giving up the possibility to substitute arbitrary values for indeterminates. This perspective contrasts with that of power series, whose variables designate numerical values, and which series therefore only have a definite value if convergence can be established. Formal power series are often used merely to represent the whole collection of their coefficients. In combinatorics, they provide representations of numerical sequences and of multisets, and for instance allow giving concise expressions for recursively defined sequences regardless of whether the recursion can be explicitly solved; this is known as the method of generating functions. Contents 1 Introduction 2 The ring of formal power series 2.1 Definition of the formal power series ring 2.1.1 Ring structure 2.1.2 Topological structure 2.1.3 Alternative topologies 2.2 Universal property 3 Operations on formal power series 3.1 Multiplying series 3.2 Power series raised to powers 3.3 Inverting series 3.4 Dividing series 3.5 Extracting coefficients 3.6 Composition of series 3.6.1 Example 3.7 Composition inverse 3.8 Formal differentiation of series 4 Properties 4.1 Algebraic properties of the formal power series ring 4.2 Topological properties of the formal power series -
Operations with Algebraic Expressions: Addition and Subtraction of Monomials
Operations with Algebraic Expressions: Addition and Subtraction of Monomials A monomial is an algebraic expression that consists of one term. Two or more monomials can be added or subtracted only if they are LIKE TERMS. Like terms are terms that have exactly the SAME variables and exponents on those variables. The coefficients on like terms may be different. Example: 7x2y5 and -2x2y5 These are like terms since both terms have the same variables and the same exponents on those variables. 7x2y5 and -2x3y5 These are NOT like terms since the exponents on x are different. Note: the order that the variables are written in does NOT matter. The different variables and the coefficient in a term are multiplied together and the order of multiplication does NOT matter (For example, 2 x 3 gives the same product as 3 x 2). Example: 8a3bc5 is the same term as 8c5a3b. To prove this, evaluate both terms when a = 2, b = 3 and c = 1. 8a3bc5 = 8(2)3(3)(1)5 = 8(8)(3)(1) = 192 8c5a3b = 8(1)5(2)3(3) = 8(1)(8)(3) = 192 As shown, both terms are equal to 192. To add two or more monomials that are like terms, add the coefficients; keep the variables and exponents on the variables the same. To subtract two or more monomials that are like terms, subtract the coefficients; keep the variables and exponents on the variables the same. Addition and Subtraction of Monomials Example 1: Add 9xy2 and −8xy2 9xy2 + (−8xy2) = [9 + (−8)] xy2 Add the coefficients. Keep the variables and exponents = 1xy2 on the variables the same. -
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). -
Algebraic Expressions 25
Section P.3 Algebraic Expressions 25 P.3 Algebraic Expressions What you should learn: Algebraic Expressions •How to identify the terms and coefficients of algebraic A basic characteristic of algebra is the use of letters (or combinations of letters) expressions to represent numbers. The letters used to represent the numbers are variables, and •How to identify the properties of combinations of letters and numbers are algebraic expressions. Here are a few algebra examples. •How to apply the properties of exponents to simplify algebraic x 3x, x ϩ 2, , 2x Ϫ 3y expressions x2 ϩ 1 •How to simplify algebraic expressions by combining like terms and removing symbols Definition of Algebraic Expression of grouping A collection of letters (called variables) and real numbers (called constants) •How to evaluate algebraic expressions combined using the operations of addition, subtraction, multiplication, divi- sion and exponentiation is called an algebraic expression. Why you should learn it: Algebraic expressions can help The terms of an algebraic expression are those parts that are separated by you construct tables of values. addition. For example, the algebraic expression x2 Ϫ 3x ϩ 6 has three terms: x2, For instance, in Example 14 on Ϫ Ϫ page 33, you can determine the 3x, and 6. Note that 3x is a term, rather than 3x, because hourly wages of miners using an x2 Ϫ 3x ϩ 6 ϭ x2 ϩ ͑Ϫ3x͒ ϩ 6. Think of subtraction as a form of addition. expression and a table of values. The terms x2 and Ϫ3x are called the variable terms of the expression, and 6 is called the constant term of the expression. -
Algebraic Expression Meaning and Examples
Algebraic Expression Meaning And Examples Unstrung Derrin reinfuses priggishly while Aamir always euhemerising his jugginses rousts unorthodoxly, he derogate so heretofore. Exterminatory and Thessalonian Carmine squilgeed her feudalist inhalants shrouds and quizzings contemptuously. Schizogenous Ransom polarizing hottest and geocentrically, she sad her abysm fightings incommutably. Do we must be restricted or subtracted is set towards the examples and algebraic expression consisting two terms in using the introduction and value Why would you spend time in class working on substitution? The most obvious feature of algebra is the use of special notation. An algebraic expression may consist of one or more terms added or subtracted. It is not written in the order it is read. Now we look at the inner set of brackets and follow the order of operations within this set of brackets. Every algebraic equations which the cube is from an algebraic expression of terms can be closed curve whose vertices are written. Capable an expression two terms at splashlearn is the first term in the other a variable, they contain variables. There is a wide variety of word phrases that translate into sums. Evaluate each of the following. The factor of a number is a number that divides that number exactly. Why do I have to complete a CAPTCHA? Composed of the pedal triangle a polynomial an consisting two terms or set of one or a visit, and distributivity of an expression exists but it! The first thing to note is that in algebra we use letters as well as numbers. Proceeding with the requested move may negatively impact site navigation and SEO. -
Solving Equations; Patterns, Functions, and Algebra; 8.15A
Mathematics Enhanced Scope and Sequence – Grade 8 Solving Equations Reporting Category Patterns, Functions, and Algebra Topic Solving equations in one variable Primary SOL 8.15a The student will solve multistep linear equations in one variable with the variable on one and two sides of the equation. Materials • Sets of algebra tiles • Equation-Solving Balance Mat (attached) • Equation-Solving Ordering Cards (attached) • Be the Teacher: Solving Equations activity sheet (attached) • Student whiteboards and markers Vocabulary equation, variable, coefficient, constant (earlier grades) Student/Teacher Actions (what students and teachers should be doing to facilitate learning) 1. Give each student a set of algebra tiles and a copy of the Equation-Solving Balance Mat. Lead students through the steps for using the tiles to model the solutions to the following equations. As you are working through the solution of each equation with the students, point out that you are undoing each operation and keeping the equation balanced by doing the same thing to both sides. Explain why you do this. When students are comfortable with modeling equation solutions with algebra tiles, transition to writing out the solution steps algebraically while still using the tiles. Eventually, progress to only writing out the steps algebraically without using the tiles. • x + 3 = 6 • x − 2 = 5 • 3x = 9 • 2x + 1 = 9 • −x + 4 = 7 • −2x − 1 = 7 • 3(x + 1) = 9 • 2x = x − 5 Continue to allow students to use the tiles whenever they wish as they work to solve equations. 2. Give each student a whiteboard and marker. Provide students with a problem to solve, and as they write each step, have them hold up their whiteboards so you can ensure that they are completing each step correctly and understanding the process. -
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]. -
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. -
Some Properties of the Discriminant Matrices of a Linear Associative Algebra*
570 R. F. RINEHART [August, SOME PROPERTIES OF THE DISCRIMINANT MATRICES OF A LINEAR ASSOCIATIVE ALGEBRA* BY R. F. RINEHART 1. Introduction. Let A be a linear associative algebra over an algebraic field. Let d, e2, • • • , en be a basis for A and let £»•/*., (hjik = l,2, • • • , n), be the constants of multiplication corre sponding to this basis. The first and second discriminant mat rices of A, relative to this basis, are defined by Ti(A) = \\h(eres[ CrsiCij i, j=l T2(A) = \\h{eres / ,J CrsiC j II i,j=l where ti(eres) and fa{erea) are the first and second traces, respec tively, of eres. The first forms in terms of the constants of multi plication arise from the isomorphism between the first and sec ond matrices of the elements of A and the elements themselves. The second forms result from direct calculation of the traces of R(er)R(es) and S(er)S(es), R{ei) and S(ei) denoting, respectively, the first and second matrices of ei. The last forms of the dis criminant matrices show that each is symmetric. E. Noetherf and C. C. MacDuffeeJ discovered some of the interesting properties of these matrices, and shed new light on the particular case of the discriminant matrix of an algebraic equation. It is the purpose of this paper to develop additional properties of these matrices, and to interpret them in some fa miliar instances. Let A be subjected to a transformation of basis, of matrix M, 7 J rH%j€j 1,2, *0). -
5. Polynomial Rings Let R Be a Commutative Ring. a Polynomial of Degree N in an Indeterminate (Or Variable) X with Coefficients
5. polynomial rings Let R be a commutative ring. A polynomial of degree n in an indeterminate (or variable) x with coefficients in R is an expression of the form f = f(x)=a + a x + + a xn, 0 1 ··· n where a0, ,an R and an =0.Wesaythata0, ,an are the coefficients of f and n ··· ∈ ̸ ··· that anx is the highest degree term of f. A polynomial is determined by its coeffiecients. m i n i Two polynomials f = i=0 aix and g = i=1 bix are equal if m = n and ai = bi for i =0, 1, ,n. ! ! Degree··· of a polynomial is a non-negative integer. The polynomials of degree zero are just the elements of R, these are called constant polynomials, or simply constants. Let R[x] denote the set of all polynomials in the variable x with coefficients in R. The addition and 2 multiplication of polynomials are defined in the usual manner: If f(x)=a0 + a1x + a2x + a x3 + and g(x)=b + b x + b x2 + b x3 + are two elements of R[x], then their sum is 3 ··· 0 1 2 3 ··· f(x)+g(x)=(a + b )+(a + b )x +(a + b )x2 +(a + b )x3 + 0 0 1 1 2 2 3 3 ··· and their product is defined by f(x) g(x)=a b +(a b + a b )x +(a b + a b + a b )x2 +(a b + a b + a b + a b )x3 + · 0 0 0 1 1 0 0 2 1 1 2 0 0 3 1 2 2 1 3 0 ··· Let 1 denote the constant polynomial 1 and 0 denote the constant polynomial zero. -
Diffman: an Object-Oriented MATLAB Toolbox for Solving Differential Equations on Manifolds
Applied Numerical Mathematics 39 (2001) 323–347 www.elsevier.com/locate/apnum DiffMan: An object-oriented MATLAB toolbox for solving differential equations on manifolds Kenth Engø a,∗,1, Arne Marthinsen b,2, Hans Z. Munthe-Kaas a,3 a Department of Informatics, University of Bergen, N-5020 Bergen, Norway b Department of Mathematical Sciences, NTNU, N-7491 Trondheim, Norway Abstract We describe an object-oriented MATLAB toolbox for solving differential equations on manifolds. The software reflects recent development within the area of geometric integration. Through the use of elements from differential geometry, in particular Lie groups and homogeneous spaces, coordinate free formulations of numerical integrators are developed. The strict mathematical definitions and results are well suited for implementation in an object- oriented language, and, due to its simplicity, the authors have chosen MATLAB as the working environment. The basic ideas of DiffMan are presented, along with particular examples that illustrate the working of and the theory behind the software package. 2001 IMACS. Published by Elsevier Science B.V. All rights reserved. Keywords: Geometric integration; Numerical integration of ordinary differential equations on manifolds; Numerical analysis; Lie groups; Lie algebras; Homogeneous spaces; Object-oriented programming; MATLAB; Free Lie algebras 1. Introduction DiffMan is an object-oriented MATLAB [24] toolbox designed to solve differential equations evolving on manifolds. The current version of the toolbox addresses primarily the solution of ordinary differential equations. The solution techniques implemented fall into the category of geometric integrators— a very active area of research during the last few years. The essence of geometric integration is to construct numerical methods that respect underlying constraints, for instance, the configuration space * Corresponding author. -
NP-Hardness of Deciding Convexity of Quartic Polynomials and Related Problems
NP-hardness of Deciding Convexity of Quartic Polynomials and Related Problems Amir Ali Ahmadi, Alex Olshevsky, Pablo A. Parrilo, and John N. Tsitsiklis ∗y Abstract We show that unless P=NP, there exists no polynomial time (or even pseudo-polynomial time) algorithm that can decide whether a multivariate polynomial of degree four (or higher even degree) is globally convex. This solves a problem that has been open since 1992 when N. Z. Shor asked for the complexity of deciding convexity for quartic polynomials. We also prove that deciding strict convexity, strong convexity, quasiconvexity, and pseudoconvexity of polynomials of even degree four or higher is strongly NP-hard. By contrast, we show that quasiconvexity and pseudoconvexity of odd degree polynomials can be decided in polynomial time. 1 Introduction The role of convexity in modern day mathematical programming has proven to be remarkably fundamental, to the point that tractability of an optimization problem is nowadays assessed, more often than not, by whether or not the problem benefits from some sort of underlying convexity. In the famous words of Rockafellar [39]: \In fact the great watershed in optimization isn't between linearity and nonlinearity, but convexity and nonconvexity." But how easy is it to distinguish between convexity and nonconvexity? Can we decide in an efficient manner if a given optimization problem is convex? A class of optimization problems that allow for a rigorous study of this question from a com- putational complexity viewpoint is the class of polynomial optimization problems. These are op- timization problems where the objective is given by a polynomial function and the feasible set is described by polynomial inequalities.