Chapter 2 Algorithms for Algebraic Geometry
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Ideals, Varieties and Macaulay 2
Ideals, Varieties and Macaulay 2 Bernd Sturmfels? This chapter introduces Macaulay 2 commands for some elementary compu- tations in algebraic geometry. Familiarity with Gr¨obnerbases is assumed. Many students and researchers alike have their first encounter with Gr¨ob- ner bases through the delightful text books [1] and [2] by David Cox, John Little and Donal O'Shea. This chapter illustrates the use of Macaulay 2 for some computations discussed in these books. It can be used as a supplement for an advanced undergraduate course or first-year graduate course in com- putational algebraic geometry. The mathematically advanced reader will find this chapter a useful summary of some basic Macaulay 2 commands. 1 A Curve in Affine Three-Space Our first example concerns geometric objects in (complex) affine 3-space. We start by setting up the ring of polynomial functions with rational coefficients. i1 : R = QQ[x,y,z] o1 = R o1 : PolynomialRing Various monomial orderings are available in Macaulay 2; since we did not specify one explicitly, the monomials in the ring R will be sorted in graded reverse lexicographic order [1, I.2, Definition 6]. We define an ideal generated by two polynomials in this ringx and assign it to the variable named curve. i2 : curve = ideal( x^4-y^5, x^3-y^7 ) 5 4 7 3 o2 = ideal (- y + x , - y + x ) o2 : Ideal of R We compute the reduced Gr¨obnerbasis of our ideal: i3 : gb curve o3 = | y5-x4 x4y2-x3 x8-x3y3 | o3 : GroebnerBasis By inspecting leading terms (and using [1, 9.3, Theorem 8]), we see that our ideal curve does indeed define a one-dimensionalx affine variety. -
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 . -
Homogeneous Polynomial Solutions to Constant Coefficient Pde’S
HOMOGENEOUS POLYNOMIAL SOLUTIONS TO CONSTANT COEFFICIENT PDE'S Bruce Reznick University of Illinois 1409 W. Green St. Urbana, IL 61801 [email protected] October 21, 1994 1. Introduction Suppose p is a homogeneous polynomial over a field K. Let p(D) be the differen- @ tial operator defined by replacing each occurence of xj in p by , defined formally @xj 2 in case K is not a subset of C. (The classical example is p(x1; · · · ; xn) = j xj , for which p(D) is the Laplacian r2.) In this paper we solve the equation p(DP)q = 0 for homogeneous polynomials q over K, under the restriction that K be an algebraically closed field of characteristic 0. (This restriction is mild, considering that the \natu- ral" field is C.) Our Main Theorem and its proof are the natural extensions of work by Sylvester, Clifford, Rosanes, Gundelfinger, Cartan, Maass and Helgason. The novelties in the presentation are the generalization of the base field and the removal of some restrictions on p. The proofs require only undergraduate mathematics and Hilbert's Nullstellensatz. A fringe benefit of the proof of the Main Theorem is an Expansion Theorem for homogeneous polynomials over R and C. This theorem is 2 2 2 trivial for linear forms, goes back at least to Gauss for p = x1 + x2 + x3, and has also been studied by Maass and Helgason. Main Theorem (See Theorem 4.1). Let K be an algebraically closed field of mj characteristic 0. Suppose p 2 K[x1; · · · ; xn] is homogeneous and p = j pj for distinct non-constant irreducible factors pj 2 K[x1; · · · ; xn]. -
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. -
Solving Cubic Polynomials
Solving Cubic Polynomials 1.1 The general solution to the quadratic equation There are four steps to finding the zeroes of a quadratic polynomial. 1. First divide by the leading term, making the polynomial monic. a 2. Then, given x2 + a x + a , substitute x = y − 1 to obtain an equation without the linear term. 1 0 2 (This is the \depressed" equation.) 3. Solve then for y as a square root. (Remember to use both signs of the square root.) a 4. Once this is done, recover x using the fact that x = y − 1 . 2 For example, let's solve 2x2 + 7x − 15 = 0: First, we divide both sides by 2 to create an equation with leading term equal to one: 7 15 x2 + x − = 0: 2 2 a 7 Then replace x by x = y − 1 = y − to obtain: 2 4 169 y2 = 16 Solve for y: 13 13 y = or − 4 4 Then, solving back for x, we have 3 x = or − 5: 2 This method is equivalent to \completing the square" and is the steps taken in developing the much- memorized quadratic formula. For example, if the original equation is our \high school quadratic" ax2 + bx + c = 0 then the first step creates the equation b c x2 + x + = 0: a a b We then write x = y − and obtain, after simplifying, 2a b2 − 4ac y2 − = 0 4a2 so that p b2 − 4ac y = ± 2a and so p b b2 − 4ac x = − ± : 2a 2a 1 The solutions to this quadratic depend heavily on the value of b2 − 4ac. -
Algebra Polynomial(Lecture-I)
Algebra Polynomial(Lecture-I) Dr. Amal Kumar Adak Department of Mathematics, G.D.College, Begusarai March 27, 2020 Algebra Dr. Amal Kumar Adak Definition Function: Any mathematical expression involving a quantity (say x) which can take different values, is called a function of that quantity. The quantity (x) is called the variable and the function of it is denoted by f (x) ; F (x) etc. Example:f (x) = x2 + 5x + 6 + x6 + x3 + 4x5. Algebra Dr. Amal Kumar Adak Polynomial Definition Polynomial: A polynomial in x over a real or complex field is an expression of the form n n−1 n−3 p (x) = a0x + a1x + a2x + ::: + an,where a0; a1;a2; :::; an are constants (free from x) may be real or complex and n is a positive integer. Example: (i)5x4 + 4x3 + 6x + 1,is a polynomial where a0 = 5; a1 = 4; a2 = 0; a3 = 6; a4 = 1a0 = 4; a1 = 4; 2 1 3 3 2 1 (ii)x + x + x + 2 is not a polynomial, since 3 ; 3 are not integers. (iii)x4 + x3 cos (x) + x2 + x + 1 is not a polynomial for the presence of the term cos (x) Algebra Dr. Amal Kumar Adak Definition Zero Polynomial: A polynomial in which all the coefficients a0; a1; a2; :::; an are zero is called a zero polynomial. Definition Complete and Incomplete polynomials: A polynomial with non-zero coefficients is called a complete polynomial. Other-wise it is incomplete. Example of a complete polynomial:x5 + 2x4 + 3x3 + 7x2 + 9x + 1. Example of an incomplete polynomial: x5 + 3x3 + 7x2 + 9x + 1. -
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. -
MA/CSSE 473 Day 15 Return Exam
MA/CSSE 473 Day 15 Return Exam Student questions Towers of Hanoi Subsets Ordered Permutations MA/CSSE 473 Day 13 • Student Questions on exam or anything else •Towers of Hanoi • Subset generation –Gray code • Permutations and order 1 Towers of Hanoi •Move all disks from peg A to peg B •One at a time • Never place larger disk on top of a smaller disk •Demo •Code • Recurrence and solution Towers of Hanoi code Recurrence for number of moves, and its solution? 2 Permutations and order number permutation number permutation •Given a permutation 0 0123 12 2013 of 0, 1, …, n‐1, can 1 0132 13 2031 2 0213 14 2103 we directly find the 3 0231 15 2130 next permutation in 4 0312 16 2301 the lexicographic 5 0321 17 2310 sequence? 6 1023 18 3012 7 1032 19 3021 •Given a permutation 8 1203 20 3102 of 0..n‐1, can we 9 1230 21 3120 determine its 10 1302 22 3201 11 1320 23 3210 permutation sequence number? •Given n and i, can we directly generate the ith permutation of 0, …, n‐1? Subset generation • Goal: generate all subsets of {0, 1, 2, …, N‐1} • Bottom‐up (decrease‐by‐one) approach •First generate Sn‐1, the collection of all subsets of {0, …, N‐2} •Then Sn = Sn‐1 { Sn‐1 {n‐1} : sSn‐1} 3 Subset generation • Numeric approach: Each subset of {0, …, N‐1} corresponds to an bit string of length N where the ith bit is 1 iff i is in the subset. •So each subset can be represented by N bits. -
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). -
Homological Algebra of Monomial Ideals
Homological Algebra of Monomial Ideals Caitlyn Booms A senior thesis completed under the guidance of Professor Claudiu Raicu as part of the SUMR program and towards the completion of a Bachelors of Science in Mathematics with an Honors Concentration. Department of Mathematics May 8, 2018 Contents 1 Introduction 2 2 Homological Algebra 3 2.1 Exact Sequences and Projective and Injective Modules . .3 2.2 Ext and Tor . .5 2.3 Free Resolutions Over the Polynomial Ring . 11 3 Monomial Ideals 13 3.1 Introduction to Monomial Ideals . 13 3.2 Simplicial Complexes . 15 3.3 Graphs and Edge Ideals . 16 4 Useful Applications 17 4.1 Fröberg's Theorem . 22 5 Computing Exti for Monomial Ideals 24 S(S=I; S) 5.1 In the Polynomial Ring with Two Variables . 26 5.2 In the Multivariate Polynomial Ring . 32 1 1 Introduction An important direction in commutative algebra is the study of homological invariants asso- ciated to ideals in a polynomial ring. These invariants tend to be quite mysterious for even some of the simplest ideals, such as those generated by monomials, which we address here. Let S = k[x1; : : : ; xn] be the polynomial ring in n variables over a eld k.A monomial in is an element of the form a1 an where each (possibly zero), and an ideal S x1 ··· xn ai 2 N I ⊆ S is a monomial ideal if it is generated by monomials. Although they arise most naturally in commutative algebra and algebraic geometry, monomial ideals can be further understood using techniques from combinatorics and topology. -
Using Elimination to Describe Maxwell Curves Lucas P
Louisiana State University LSU Digital Commons LSU Master's Theses Graduate School 2006 Using elimination to describe Maxwell curves Lucas P. Beverlin Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_theses Part of the Applied Mathematics Commons Recommended Citation Beverlin, Lucas P., "Using elimination to describe Maxwell curves" (2006). LSU Master's Theses. 2879. https://digitalcommons.lsu.edu/gradschool_theses/2879 This Thesis is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Master's Theses by an authorized graduate school editor of LSU Digital Commons. For more information, please contact [email protected]. USING ELIMINATION TO DESCRIBE MAXWELL CURVES A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial ful¯llment of the requirements for the degree of Master of Science in The Department of Mathematics by Lucas Paul Beverlin B.S., Rose-Hulman Institute of Technology, 2002 August 2006 Acknowledgments This dissertation would not be possible without several contributions. I would like to thank my thesis advisor Dr. James Madden for his many suggestions and his patience. I would also like to thank my other committee members Dr. Robert Perlis and Dr. Stephen Shipman for their help. I would like to thank my committee in the Experimental Statistics department for their understanding while I completed this project. And ¯nally I would like to thank my family for giving me the chance to achieve a higher education.