Explicit Formulas for the Multivariate Resultant
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10 7 General Vector Spaces Proposition & Definition 7.14
10 7 General vector spaces Proposition & Definition 7.14. Monomial basis of n(R) P Letn N 0. The particular polynomialsm 0,m 1,...,m n n(R) defined by ∈ ∈P n 1 n m0(x) = 1,m 1(x) = x, . ,m n 1(x) =x − ,m n(x) =x for allx R − ∈ are called monomials. The family =(m 0,m 1,...,m n) forms a basis of n(R) B P and is called the monomial basis. Hence dim( n(R)) =n+1. P Corollary 7.15. The method of equating the coefficients Letp andq be two real polynomials with degreen N, which means ∈ n n p(x) =a nx +...+a 1x+a 0 andq(x) =b nx +...+b 1x+b 0 for some coefficientsa n, . , a1, a0, bn, . , b1, b0 R. ∈ If we have the equalityp=q, which means n n anx +...+a 1x+a 0 =b nx +...+b 1x+b 0, (7.3) for allx R, then we can concludea n =b n, . , a1 =b 1 anda 0 =b 0. ∈ VL18 ↓ Remark: Since dim( n(R)) =n+1 and we have the inclusions P 0(R) 1(R) 2(R) (R) (R), P ⊂P ⊂P ⊂···⊂P ⊂F we conclude that dim( (R)) and dim( (R)) cannot befinite natural numbers. Sym- P F bolically, we write dim( (R)) = in such a case. P ∞ 7.4 Coordinates with respect to a basis 11 7.4 Coordinates with respect to a basis 7.4.1 Basis implies coordinates Again, we deal with the caseF=R andF=C simultaneously. Therefore, letV be an F-vector space with the two operations+ and . -
Selecting a Monomial Basis for Sums of Squares Programming Over a Quotient Ring
Selecting a Monomial Basis for Sums of Squares Programming over a Quotient Ring Frank Permenter1 and Pablo A. Parrilo2 Abstract— In this paper we describe a method for choosing for λi(x) and s(x), this feasibility problem is equivalent to a “good” monomial basis for a sums of squares (SOS) program an SDP. This follows because the sum of squares constraint formulated over a quotient ring. It is known that the monomial is equivalent to a semidefinite constraint on the coefficients basis need only include standard monomials with respect to a Groebner basis. We show that in many cases it is possible of s(x), and the equality constraint is equivalent to linear to use a reduced subset of standard monomials by combining equations on the coefficients of s(x) and λi(x). Groebner basis techniques with the well-known Newton poly- Naturally, the complexity of this sums of squares program tope reduction. This reduced subset of standard monomials grows with the complexity of the underlying variety, as yields a smaller semidefinite program for obtaining a certificate does the size of the corresponding SDP. It is therefore of non-negativity of a polynomial on an algebraic variety. natural to explore how algebraic structure can be exploited I. INTRODUCTION to simplify this sums of squares program. Consider the Many practical engineering problems require demonstrat- following reformulation [10], which is feasible if and only ing non-negativity of a multivariate polynomial over an if (2) is feasible: algebraic variety, i.e. over the solution set of polynomial Find s(x) equations. -
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. -
A Review of Some Basic Mathematical Concepts and Differential Calculus
A Review of Some Basic Mathematical Concepts and Differential Calculus Kevin Quinn Assistant Professor Department of Political Science and The Center for Statistics and the Social Sciences Box 354320, Padelford Hall University of Washington Seattle, WA 98195-4320 October 11, 2002 1 Introduction These notes are written to give students in CSSS/SOC/STAT 536 a quick review of some of the basic mathematical concepts they will come across during this course. These notes are not meant to be comprehensive but rather to be a succinct treatment of some of the key ideas. The notes draw heavily from Apostol (1967) and Simon and Blume (1994). Students looking for a more detailed presentation are advised to see either of these sources. 2 Preliminaries 2.1 Notation 1 1 R or equivalently R denotes the real number line. R is sometimes referred to as 1-dimensional 2 Euclidean space. 2-dimensional Euclidean space is represented by R , 3-dimensional space is rep- 3 k resented by R , and more generally, k-dimensional Euclidean space is represented by R . 1 1 Suppose a ∈ R and b ∈ R with a < b. 1 A closed interval [a, b] is the subset of R whose elements are greater than or equal to a and less than or equal to b. 1 An open interval (a, b) is the subset of R whose elements are greater than a and less than b. 1 A half open interval [a, b) is the subset of R whose elements are greater than or equal to a and less than b. -
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). -
1 Expressing Vectors in Coordinates
Math 416 - Abstract Linear Algebra Fall 2011, section E1 Working in coordinates In these notes, we explain the idea of working \in coordinates" or coordinate-free, and how the two are related. 1 Expressing vectors in coordinates Let V be an n-dimensional vector space. Recall that a choice of basis fv1; : : : ; vng of V is n the same data as an isomorphism ': V ' R , which sends the basis fv1; : : : ; vng of V to the n standard basis fe1; : : : ; eng of R . In other words, we have ' n ': V −! R vi 7! ei 2 3 c1 6 . 7 v = c1v1 + ::: + cnvn 7! 4 . 5 : cn This allows us to manipulate abstract vectors v = c v + ::: + c v simply as lists of numbers, 2 3 1 1 n n c1 6 . 7 n the coordinate vectors 4 . 5 2 R with respect to the basis fv1; : : : ; vng. Note that the cn coordinates of v 2 V depend on the choice of basis. 2 3 c1 Notation: Write [v] := 6 . 7 2 n for the coordinates of v 2 V with respect to the basis fvig 4 . 5 R cn fv1; : : : ; vng. For shorthand notation, let us name the basis A := fv1; : : : ; vng and then write [v]A for the coordinates of v with respect to the basis A. 2 2 Example: Using the monomial basis f1; x; x g of P2 = fa0 + a1x + a2x j ai 2 Rg, we obtain an isomorphism ' 3 ': P2 −! R 2 3 a0 2 a0 + a1x + a2x 7! 4a15 : a2 2 3 a0 2 In the notation above, we have [a0 + a1x + a2x ]fxig = 4a15. -
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. -
Tight Monomials and the Monomial Basis Property
PDF Manuscript TIGHT MONOMIALS AND MONOMIAL BASIS PROPERTY BANGMING DENG AND JIE DU Abstract. We generalize a criterion for tight monomials of quantum enveloping algebras associated with symmetric generalized Cartan matrices and a monomial basis property of those associated with symmetric (classical) Cartan matrices to their respective sym- metrizable case. We then link the two by establishing that a tight monomial is necessarily a monomial defined by a weakly distinguished word. As an application, we develop an algorithm to compute all tight monomials in the rank 2 Dynkin case. The existence of Hall polynomials for Dynkin or cyclic quivers not only gives rise to a simple realization of the ±-part of the corresponding quantum enveloping algebras, but also results in interesting applications. For example, by specializing q to 0, degenerate quantum enveloping algebras have been investigated in the context of generic extensions ([20], [8]), while through a certain non-triviality property of Hall polynomials, the authors [4, 5] have established a monomial basis property for quantum enveloping algebras associated with Dynkin and cyclic quivers. The monomial basis property describes a systematic construction of many monomial/integral monomial bases some of which have already been studied in the context of elementary algebraic constructions of canonical bases; see, e.g., [15, 27, 21, 5] in the simply-laced Dynkin case and [3, 18], [9, Ch. 11] in general. This property in the cyclic quiver case has also been used in [10] to obtain an elementary construction of PBW-type bases, and hence, of canonical bases for quantum affine sln. In this paper, we will complete this program by proving this property for all finite types. -
Hk Hk38 21 56 + + + Y Xyx X 8 40
New York City College of Technology [MODULE 5: FACTORING. SOLVE QUADRATIC EQUATIONS BY FACTORING] MAT 1175 PLTL Workshops Name: ___________________________________ Points: ______ 1. The Greatest Common Factor The greatest common factor (GCF) for a polynomial is the largest monomial that divides each term of the polynomial. GCF contains the GCF of the numerical coefficients and each variable raised to the smallest exponent that appear. a. Factor 8x4 4x3 16x2 4x 24 b. Factor 9 ba 43 3 ba 32 6ab 2 2. Factoring by Grouping a. Factor 56 21k 8h 3hk b. Factor 5x2 40x xy 8y 3. Factoring Trinomials with Lead Coefficients of 1 Steps to factoring trinomials with lead coefficient of 1 1. Write the trinomial in descending powers. 2. List the factorizations of the third term of the trinomial. 3. Pick the factorization where the sum of the factors is the coefficient of the middle term. 4. Check by multiplying the binomials. a. Factor x2 8x 12 b. Factor y 2 9y 36 1 Prepared by Profs. Ghezzi, Han, and Liou-Mark Supported by BMI, NSF STEP #0622493, and Perkins New York City College of Technology [MODULE 5: FACTORING. SOLVE QUADRATIC EQUATIONS BY FACTORING] MAT 1175 PLTL Workshops c. Factor a2 7ab 8b2 d. Factor x2 9xy 18y 2 e. Factor x3214 x 45 x f. Factor 2xx2 24 90 4. Factoring Trinomials with Lead Coefficients other than 1 Method: Trial-and-error 1. Multiply the resultant binomials. 2. Check the sum of the inner and the outer terms is the original middle term bx . a. Factor 2h2 5h 12 b. -
Polynomial Interpolation CPSC 303: Numerical Approximation and Discretization
Lecture Notes 2: Polynomial Interpolation CPSC 303: Numerical Approximation and Discretization Ian M. Mitchell [email protected] http://www.cs.ubc.ca/~mitchell University of British Columbia Department of Computer Science Winter Term Two 2012{2013 Copyright 2012{2013 by Ian M. Mitchell This work is made available under the terms of the Creative Commons Attribution 2.5 Canada license http://creativecommons.org/licenses/by/2.5/ca/ Outline • Background • Problem statement and motivation • Formulation: The linear system and its conditioning • Polynomial bases • Monomial • Lagrange • Newton • Uniqueness of polynomial interpolant • Divided differences • Divided difference tables and the Newton basis interpolant • Divided difference connection to derivatives • Osculating interpolation: interpolating derivatives • Error analysis for polynomial interpolation • Reducing the error using the Chebyshev points as abscissae CPSC 303 Notes 2 Ian M. Mitchell | UBC Computer Science 2/ 53 Interpolation Motivation n We are given a collection of data samples f(xi; yi)gi=0 n • The fxigi=0 are called the abscissae (singular: abscissa), n the fyigi=0 are called the data values • Want to find a function p(x) which can be used to estimate y(x) for x 6= xi • Why? We often get discrete data from sensors or computation, but we want information as if the function were not discretely sampled • If possible, p(x) should be inexpensive to evaluate for a given x CPSC 303 Notes 2 Ian M. Mitchell | UBC Computer Science 3/ 53 Interpolation Formulation There are lots of ways to define a function p(x) to approximate n f(xi; yi)gi=0 • Interpolation means p(xi) = yi (and we will only evaluate p(x) for mini xi ≤ x ≤ maxi xi) • Most interpolants (and even general data fitting) is done with a linear combination of (usually nonlinear) basis functions fφj(x)g n X p(x) = pn(x) = cjφj(x) j=0 where cj are the interpolation coefficients or interpolation weights CPSC 303 Notes 2 Ian M. -
Effective Noether Irreducibility Forms and Applications*
Appears in Journal of Computer and System Sciences, 50/2 pp. 274{295 (1995). Effective Noether Irreducibility Forms and Applications* Erich Kaltofen Department of Computer Science, Rensselaer Polytechnic Institute Troy, New York 12180-3590; Inter-Net: [email protected] Abstract. Using recent absolute irreducibility testing algorithms, we derive new irreducibility forms. These are integer polynomials in variables which are the generic coefficients of a multivariate polynomial of a given degree. A (multivariate) polynomial over a specific field is said to be absolutely irreducible if it is irreducible over the algebraic closure of its coefficient field. A specific polynomial of a certain degree is absolutely irreducible, if and only if all the corresponding irreducibility forms vanish when evaluated at the coefficients of the specific polynomial. Our forms have much smaller degrees and coefficients than the forms derived originally by Emmy Noether. We can also apply our estimates to derive more effective versions of irreducibility theorems by Ostrowski and Deuring, and of the Hilbert irreducibility theorem. We also give an effective estimate on the diameter of the neighborhood of an absolutely irreducible polynomial with respect to the coefficient space in which absolute irreducibility is preserved. Furthermore, we can apply the effective estimates to derive several factorization results in parallel computational complexity theory: we show how to compute arbitrary high precision approximations of the complex factors of a multivariate integral polynomial, and how to count the number of absolutely irreducible factors of a multivariate polynomial with coefficients in a rational function field, both in the complexity class . The factorization results also extend to the case where the coefficient field is a function field. -
Arxiv:1805.04488V5 [Math.NA]
GENERALIZED STANDARD TRIPLES FOR ALGEBRAIC LINEARIZATIONS OF MATRIX POLYNOMIALS∗ EUNICE Y. S. CHAN†, ROBERT M. CORLESS‡, AND LEILI RAFIEE SEVYERI§ Abstract. We define generalized standard triples X, Y , and L(z) = zC1 − C0, where L(z) is a linearization of a regular n×n −1 −1 matrix polynomial P (z) ∈ C [z], in order to use the representation X(zC1 − C0) Y = P (z) which holds except when z is an eigenvalue of P . This representation can be used in constructing so-called algebraic linearizations for matrix polynomials of the form H(z) = zA(z)B(z)+ C ∈ Cn×n[z] from generalized standard triples of A(z) and B(z). This can be done even if A(z) and B(z) are expressed in differing polynomial bases. Our main theorem is that X can be expressed using ℓ the coefficients of the expression 1 = Pk=0 ekφk(z) in terms of the relevant polynomial basis. For convenience, we tabulate generalized standard triples for orthogonal polynomial bases, the monomial basis, and Newton interpolational bases; for the Bernstein basis; for Lagrange interpolational bases; and for Hermite interpolational bases. We account for the possibility of common similarity transformations. Key words. Standard triple, regular matrix polynomial, polynomial bases, companion matrix, colleague matrix, comrade matrix, algebraic linearization, linearization of matrix polynomials. AMS subject classifications. 65F15, 15A22, 65D05 1. Introduction. A matrix polynomial P (z) ∈ Fm×n[z] is a polynomial in the variable z with coef- ficients that are m by n matrices with entries from the field F. We will use F = C, the field of complex k numbers, in this paper.