Unit 3 – (Ch 6) Polynomials and Polynomial Functions NOTES PACKET
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Robust Algorithms for Object Localization
Robust Algorithms for Ob ject Lo calizati on y Dinesh Mano cha Aaron S. Wallack Department of Computer Science Computer Science Division University of North Carolina University of California Chap el Hill, NC 27599-3175 Berkeley, CA 94720 mano [email protected] wallack@rob otics.eecs.b erkeley.edu Abstract Ob ject lo calization using sensed data features and corresp onding mo del features is a fun- damental problem in machine vision. We reformulate ob ject lo calization as a least squares problem: the optimal p ose estimate minimizes the squared error discrepancy b etween the sensed and predicted data. The resulting problem is non-linear and previous attempts to estimate the optimal p ose using lo cal metho ds such as gradient descent su er from lo cal minima and, at times, return incorrect results. In this pap er, we describ e an exact, accurate and ecient algorithm based on resultants, linear algebra, and numerical analysis, for solv- ing the nonlinear least squares problem asso ciated with lo calizing two-dimensional ob jects given two-dimensional data. This work is aimed at tasks where the sensor features and the mo del features are of di erenttyp es and where either the sensor features or mo del features are p oints. It is applicable to lo calizing mo deled ob jects from image data, and estimates the p ose using all of the pixels in the detected edges. The algorithm's running time dep ends mainly on the typ e of non-p oint features, and it also dep ends to a small extent on the number of features. -
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. -
The Fast Quartic Solver Peter Strobach AST-Consulting Inc., Bahnsteig 6, 94133 Röhrnbach, Germany Article Info a B S T R a C T
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Journal of Computational and Applied Mathematics 234 (2010) 3007–3024 Contents lists available at ScienceDirect Journal of Computational and Applied Mathematics journal homepage: www.elsevier.com/locate/cam The fast quartic solver Peter Strobach AST-Consulting Inc., Bahnsteig 6, 94133 Röhrnbach, Germany article info a b s t r a c t Article history: A fast and highly accurate algorithm for solving quartic equations is introduced. This Received 27 February 2010 new algorithm is more than six times as fast and several times more accurate than the Received in revised form 14 April 2010 quasi-standard Companion matrix eigenvalue quartic solver. Moreover, the method is exceptionally robust in cases of extreme root spread. The new algorithm is based on MSC: a factorization of the quartic in two quadratics, which are solved in closed form. The 65E05 performance key at this point is a fixed-point iteration based fitting algorithm for backward Keywords: optimization of the underlying quartic-to-quadratic polynomial decomposition. Detailed Quartic function experimental results confirm our claims. Polynomial factorization ' 2010 Elsevier B.V. All rights reserved. Polynomial rooting Companion matrix Eigenvalues 1. Introduction Consider the practical rooting of quartic polynomials or functions of the type f .x/ D x4 C ax3 C bx2 C cx C d D x2 C αx C β x2 C γ x C δ D .x − x1/.x − x2/.x − x3/.x − x4/ : (1) A quartic solver is an algorithm that relates a set of real or complex conjugate or mixed real/complex conjugate roots fx1; x2; x3; x4g to a given set of real quartic coefficients fa; b; c; dg. -
Fields Besides the Real Numbers Math 130 Linear Algebra
manner, which are both commutative and asso- ciative, both have identity elements (the additive identity denoted 0 and the multiplicative identity denoted 1), addition has inverse elements (the ad- ditive inverse of x denoted −x as usual), multipli- cation has inverses of nonzero elements (the multi- Fields besides the Real Numbers 1 −1 plicative inverse of x denoted x or x ), multipli- Math 130 Linear Algebra cation distributes over addition, and 0 6= 1. D Joyce, Fall 2015 Of course, one example of a field is the field of Most of the time in linear algebra, our vectors real numbers R. What are some others? will have coordinates that are real numbers, that is to say, our scalar field is R, the real numbers. Example 2 (The field of rational numbers, Q). Another example is the field of rational numbers. But linear algebra works over other fields, too, A rational number is the quotient of two integers like C, the complex numbers. In fact, when we a=b where the denominator is not 0. The set of discuss eigenvalues and eigenvectors, we'll need to all rational numbers is denoted Q. We're familiar do linear algebra over C. Some of the applications with the fact that the sum, difference, product, and of linear algebra such as solving linear differential quotient (when the denominator is not zero) of ra- equations require C as as well. tional numbers is another rational number, so Q Some applications in computer science use linear has all the operations it needs to be a field, and algebra over a two-element field Z (described be- 2 since it's part of the field of the real numbers R, its low). -
Ring (Mathematics) 1 Ring (Mathematics)
Ring (mathematics) 1 Ring (mathematics) In mathematics, a ring is an algebraic structure consisting of a set together with two binary operations usually called addition and multiplication, where the set is an abelian group under addition (called the additive group of the ring) and a monoid under multiplication such that multiplication distributes over addition.a[›] In other words the ring axioms require that addition is commutative, addition and multiplication are associative, multiplication distributes over addition, each element in the set has an additive inverse, and there exists an additive identity. One of the most common examples of a ring is the set of integers endowed with its natural operations of addition and multiplication. Certain variations of the definition of a ring are sometimes employed, and these are outlined later in the article. Polynomials, represented here by curves, form a ring under addition The branch of mathematics that studies rings is known and multiplication. as ring theory. Ring theorists study properties common to both familiar mathematical structures such as integers and polynomials, and to the many less well-known mathematical structures that also satisfy the axioms of ring theory. The ubiquity of rings makes them a central organizing principle of contemporary mathematics.[1] Ring theory may be used to understand fundamental physical laws, such as those underlying special relativity and symmetry phenomena in molecular chemistry. The concept of a ring first arose from attempts to prove Fermat's last theorem, starting with Richard Dedekind in the 1880s. After contributions from other fields, mainly number theory, the ring notion was generalized and firmly established during the 1920s by Emmy Noether and Wolfgang Krull.[2] Modern ring theory—a very active mathematical discipline—studies rings in their own right. -
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. -
A Brief History of Ring Theory
A Brief History of Ring Theory by Kristen Pollock Abstract Algebra II, Math 442 Loyola College, Spring 2005 A Brief History of Ring Theory Kristen Pollock 2 1. Introduction In order to fully define and examine an abstract ring, this essay will follow a procedure that is unlike a typical algebra textbook. That is, rather than initially offering just definitions, relevant examples will first be supplied so that the origins of a ring and its components can be better understood. Of course, this is the path that history has taken so what better way to proceed? First, it is important to understand that the abstract ring concept emerged from not one, but two theories: commutative ring theory and noncommutative ring the- ory. These two theories originated in different problems, were developed by different people and flourished in different directions. Still, these theories have much in com- mon and together form the foundation of today's ring theory. Specifically, modern commutative ring theory has its roots in problems of algebraic number theory and algebraic geometry. On the other hand, noncommutative ring theory originated from an attempt to expand the complex numbers to a variety of hypercomplex number systems. 2. Noncommutative Rings We will begin with noncommutative ring theory and its main originating ex- ample: the quaternions. According to Israel Kleiner's article \The Genesis of the Abstract Ring Concept," [2]. these numbers, created by Hamilton in 1843, are of the form a + bi + cj + dk (a; b; c; d 2 R) where addition is through its components 2 2 2 and multiplication is subject to the relations i =pj = k = ijk = −1. -
Subrings, Ideals and Quotient Rings the First Definition Should Not Be
Section 17: Subrings, Ideals and Quotient Rings The first definition should not be unexpected: Def: A nonempty subset S of a ring R is a subring of R if S is closed under addition, negatives (so it's an additive subgroup) and multiplication; in other words, S inherits operations from R that make it a ring in its own right. Naturally, Z is a subring of Q, which is a subring of R, which is a subring of C. Also, R is a subring of R[x], which is a subring of R[x; y], etc. The additive subgroups nZ of Z are subrings of Z | the product of two multiples of n is another multiple of n. For the same reason, the subgroups hdi in the various Zn's are subrings. Most of these latter subrings do not have unities. But we know that, in the ring Z24, 9 is an idempotent, as is 16 = 1 − 9. In the subrings h9i and h16i of Z24, 9 and 16 respectively are unities. The subgroup h1=2i of Q is not a subring of Q because it is not closed under multiplication: (1=2)2 = 1=4 is not an integer multiple of 1=2. Of course the immediate next question is which subrings can be used to form factor rings, as normal subgroups allowed us to form factor groups. Because a ring is commutative as an additive group, normality is not a problem; so we are really asking when does multiplication of additive cosets S + a, done by multiplying their representatives, (S + a)(S + b) = S + (ab), make sense? Again, it's a question of whether this operation is well-defined: If S + a = S + c and S + b = S + d, what must be true about S so that we can be sure S + (ab) = S + (cd)? Using the definition of cosets: If a − c; b − d 2 S, what must be true about S to assure that ab − cd 2 S? We need to have the following element always end up in S: ab − cd = ab − ad + ad − cd = a(b − d) + (a − c)d : Because b − d and a − c can be any elements of S (and either one may be 0), and a; d can be any elements of R, the property required to assure that this element is in S, and hence that this multiplication of cosets is well-defined, is that, for all s in S and r in R, sr and rs are also in S. -
Define the Determinant of A, to Be Det(A) = Ad − Bc. EX
DETERMINANT 1. 2 × 2 Determinants Fix a matrix A 2 R2×2 a b A = c d Define the determinant of A, to be det(A) = ad − bc: 2 −1 EXAMPLE: det = 2(2) − (−1)(−4) = 0. −4 2 det(A) encodes important geometric and algebraic information about A. For instance, the matrix A is invertible if and only if det(A) 6= 0. To see this suppose det(A) 6= 0, in this case the matrix 1 d −b B = det A −c a is well-defined. One computes, 1 ad − bc 0 BA = = I : ad − bc 0 ad − bc 2 and so A is invertible and A−1 = B. In the other direction, if A is not invertible, then the columns are linearly dependent and so either a = kb and c = kd or b = ka and d = kc for some k 2 R. Hence, det(A) = kbd − kbd = 0 or = kac − kac = 0. More geometrically, if x1 S = : 0 ≤ x1; x2 ≤ 1 x2 is the unit square and A(S) = fA~x : ~x 2 Sg is the image under multiplication by A of S, then the area of A(S), which we denote by jA(S)j is jA(S)j = j det(A)j: 2. Determinants of n × n matrices We seek a good notion for det(A) when A 2 Rn×n. It is improtant to remember that while this is of historical and theoretical importance, it is not as significant for computational purposes. Begin by fixing a matrix A 2 Rn×n of the form 2 3 a11 ··· a1n 6 . -
Fundamental Units and Regulators of an Infinite Family of Cyclic Quartic Function Fields
J. Korean Math. Soc. 54 (2017), No. 2, pp. 417–426 https://doi.org/10.4134/JKMS.j160002 pISSN: 0304-9914 / eISSN: 2234-3008 FUNDAMENTAL UNITS AND REGULATORS OF AN INFINITE FAMILY OF CYCLIC QUARTIC FUNCTION FIELDS Jungyun Lee and Yoonjin Lee Abstract. We explicitly determine fundamental units and regulators of an infinite family of cyclic quartic function fields Lh of unit rank 3 with a parameter h in a polynomial ring Fq[t], where Fq is the finite field of order q with characteristic not equal to 2. This result resolves the second part of Lehmer’s project for the function field case. 1. Introduction Lecacheux [9, 10] and Darmon [3] obtain a family of cyclic quintic fields over Q, and Washington [22] obtains a family of cyclic quartic fields over Q by using coverings of modular curves. Lehmer’s project [13, 14] consists of two parts; one is finding families of cyclic extension fields, and the other is computing a system of fundamental units of the families. Washington [17, 22] computes a system of fundamental units and the regulators of cyclic quartic fields and cyclic quintic fields, which is the second part of Lehmer’s project. We are interested in working on the second part of Lehmer’s project for the families of function fields which are analogous to the type of the number field families produced by using modular curves given in [22]: that is, finding a system of fundamental units and regulators of families of cyclic extension fields over the rational function field Fq(t). In [11], we obtain the results for the quintic extension case. -
Exponents and Polynomials Exponent Is Defined to Be One Divided by the Nonzero Number to N 2 a Means the Product of N Factors, Each of Which Is a (I.E
how Mr. Breitsprecher’s Edition March 9, 2005 Web: www.clubtnt.org/my_algebra Examples: Quotient of a Monomial by a Easy does it! Let's look at how • (35x³ )/(7x) = 5x² to divide polynomials by starting Monomial • (16x² y² )/(8xy² ) = 2x with the simplest case, dividing a To divide a monomial by a monomial by a monomial. This is monomial, divide numerical Remember: Any nonzero number really just an application of the coefficient by numerical coefficient. divided by itself is one (y²/y² = 1), Quotient Rule that was covered Divide powers of same variable and any nonzero number to the zero when we reviewed exponents. using the Quotient Rule of power is defined to be one (Zero Next, we'll look at dividing a Exponent Rule). Exponents – when dividing polynomial by a monomial. Lastly, exponentials with the same base we we will see how the same concepts • 42x/(7x³ ) = 6/x² subtract the exponent on the are used to divide a polynomial by a denominator from the exponent on Remember: The fraction x/x³ polynomial. Are you ready? Let’s the numerator to obtain the exponent simplifies to 1/x². The Negative begin! on the answer. Exponent Rule says that any nonzero number to a negative Review: Exponents and Polynomials exponent is defined to be one divided by the nonzero number to n 2 a means the product of n factors, each of which is a (i.e. 3 = 3*3, or 9) the positive exponent obtained. We -2 Exponent Rules could write that answer as 6x . • Product Role: am*an=am+n Quotient of a Polynomial by a • Power Rule: (am)n=amn Monomial n n n • Power of a Product Rule: (ab) = a b To divide a polynomial by a n n n • Power of a Quotient Rule: (a/b) =a /b monomial, divide each of the terms • Quotient Rule: am/an=am-n of the polynomial by a monomial. -
Factoring and Roots
Factoring and roots Definition: A polynomial is a function of the form: n n−1 f(x) = anx + an−1x + ::: + a1x + a0 where an; an−1; : : : ; a1; a0 are real numbers and n is a nonnegative integer. The domain of a polynomial is the set of all real numbers. The degree of the polynomial is the largest power of x that appears. Division Algorithm for Polynomials: If p(x) and d(x) denote polynomial functions and if d(x) is a polynomial whose degree is greater than zero, then there are unique polynomial functions q(x) and r(x) such that p(x) r(x) d(x) = q(x) + d(x) or p(x) = q(x)d(x) + r(x). where r(x) is either the zero polynomial or a polynomial of degree less than that of d(x) In the equation above, p(x) is the dividend, d(x) is the divisor, q(x) is the quotient and r(x) is the remainder. We say d(x) divides p(x) () the remainder is 0 () p(x) = d(x)q(x) () d(x) is a factor of p(x). p(x) If d(x) is a factor of p(x), the other factor of p(x) is q(x), the quotient of d(x) . p(x) • Given d(x) , divide (using Long Division or Synthetic Division (if applicable)) to get the quotient q(x) and remainder r(x). Write the answer in division algorithm form: p(x) = d(x)q(x) + r(x). x3+1 • Write x−1 in division law form.