A Conic Sections
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Fast and Efficient Algorithms for Evaluating Uniform and Nonuniform
World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:13, No:8, 2019 Fast and Efficient Algorithms for Evaluating Uniform and Nonuniform Lagrange and Newton Curves Taweechai Nuntawisuttiwong, Natasha Dejdumrong Abstract—Newton-Lagrange Interpolations are widely used in quadratic computation time, which is slower than the others. numerical analysis. However, it requires a quadratic computational However, there are some curves in CAGD, Wang-Ball, DP time for their constructions. In computer aided geometric design and Dejdumrong curves with linear complexity. In order to (CAGD), there are some polynomial curves: Wang-Ball, DP and Dejdumrong curves, which have linear time complexity algorithms. reduce their computational time, Bezier´ curve is converted Thus, the computational time for Newton-Lagrange Interpolations into any of Wang-Ball, DP and Dejdumrong curves. Hence, can be reduced by applying the algorithms of Wang-Ball, DP and the computational time for Newton-Lagrange interpolations Dejdumrong curves. In order to use Wang-Ball, DP and Dejdumrong can be reduced by converting them into Wang-Ball, DP and algorithms, first, it is necessary to convert Newton-Lagrange Dejdumrong algorithms. Thus, it is necessary to investigate polynomials into Wang-Ball, DP or Dejdumrong polynomials. In this work, the algorithms for converting from both uniform and the conversion from Newton-Lagrange interpolation into non-uniform Newton-Lagrange polynomials into Wang-Ball, DP and the curves with linear complexity, Wang-Ball, DP and Dejdumrong polynomials are investigated. Thus, the computational Dejdumrong curves. An application of this work is to modify time for representing Newton-Lagrange polynomials can be reduced sketched image in CAD application with the computational into linear complexity. -
Bézout's Theorem
Bézout's theorem Toni Annala Contents 1 Introduction 2 2 Bézout's theorem 4 2.1 Ane plane curves . .4 2.2 Projective plane curves . .6 2.3 An elementary proof for the upper bound . 10 2.4 Intersection numbers . 12 2.5 Proof of Bézout's theorem . 16 3 Intersections in Pn 20 3.1 The Hilbert series and the Hilbert polynomial . 20 3.2 A brief overview of the modern theory . 24 3.3 Intersections of hypersurfaces in Pn ...................... 26 3.4 Intersections of subvarieties in Pn ....................... 32 3.5 Serre's Tor-formula . 36 4 Appendix 42 4.1 Homogeneous Noether normalization . 42 4.2 Primes associated to a graded module . 43 1 Chapter 1 Introduction The topic of this thesis is Bézout's theorem. The classical version considers the number of points in the intersection of two algebraic plane curves. It states that if we have two algebraic plane curves, dened over an algebraically closed eld and cut out by multi- variate polynomials of degrees n and m, then the number of points where these curves intersect is exactly nm if we count multiple intersections and intersections at innity. In Chapter 2 we dene the necessary terminology to state this theorem rigorously, give an elementary proof for the upper bound using resultants, and later prove the full Bézout's theorem. A very modest background should suce for understanding this chapter, for example the course Algebra II given at the University of Helsinki should cover most, if not all, of the prerequisites. In Chapter 3, we generalize Bézout's theorem to higher dimensional projective spaces. -
Chapter 1 Geometry of Plane Curves
Chapter 1 Geometry of Plane Curves Definition 1 A (parametrized) curve in Rn is a piece-wise differentiable func- tion α :(a, b) Rn. If I is any other subset of R, α : I Rn is a curve provided that α→extends to a piece-wise differentiable function→ on some open interval (a, b) I. The trace of a curve α is the set of points α ((a, b)) Rn. ⊃ ⊂ AsubsetS Rn is parametrized by α if and only if there exists set I R ⊂ ⊆ such that α : I Rn is a curve for which α (I)=S. A one-to-one parame- trization α assigns→ and orientation toacurveC thought of as the direction of increasing parameter, i.e., if s<t,the direction of increasing parameter runs from α (s) to α (t) . AsubsetS Rn may be parametrized in many ways. ⊂ Example 2 Consider the circle C : x2 + y2 =1and let ω>0. The curve 2π α : 0, R2 given by ω → ¡ ¤ α(t)=(cosωt, sin ωt) . is a parametrization of C for each choice of ω R. Recall that the velocity and acceleration are respectively given by ∈ α0 (t)=( ω sin ωt, ω cos ωt) − and α00 (t)= ω2 cos 2πωt, ω2 sin 2πωt = ω2α(t). − − − The speed is given by ¡ ¢ 2 2 v(t)= α0(t) = ( ω sin ωt) +(ω cos ωt) = ω. (1.1) k k − q 1 2 CHAPTER 1. GEOMETRY OF PLANE CURVES The various parametrizations obtained by varying ω change the velocity, ac- 2π celeration and speed but have the same trace, i.e., α 0, ω = C for each ω. -
Combination of Cubic and Quartic Plane Curve
IOSR Journal of Mathematics (IOSR-JM) e-ISSN: 2278-5728,p-ISSN: 2319-765X, Volume 6, Issue 2 (Mar. - Apr. 2013), PP 43-53 www.iosrjournals.org Combination of Cubic and Quartic Plane Curve C.Dayanithi Research Scholar, Cmj University, Megalaya Abstract The set of complex eigenvalues of unistochastic matrices of order three forms a deltoid. A cross-section of the set of unistochastic matrices of order three forms a deltoid. The set of possible traces of unitary matrices belonging to the group SU(3) forms a deltoid. The intersection of two deltoids parametrizes a family of Complex Hadamard matrices of order six. The set of all Simson lines of given triangle, form an envelope in the shape of a deltoid. This is known as the Steiner deltoid or Steiner's hypocycloid after Jakob Steiner who described the shape and symmetry of the curve in 1856. The envelope of the area bisectors of a triangle is a deltoid (in the broader sense defined above) with vertices at the midpoints of the medians. The sides of the deltoid are arcs of hyperbolas that are asymptotic to the triangle's sides. I. Introduction Various combinations of coefficients in the above equation give rise to various important families of curves as listed below. 1. Bicorn curve 2. Klein quartic 3. Bullet-nose curve 4. Lemniscate of Bernoulli 5. Cartesian oval 6. Lemniscate of Gerono 7. Cassini oval 8. Lüroth quartic 9. Deltoid curve 10. Spiric section 11. Hippopede 12. Toric section 13. Kampyle of Eudoxus 14. Trott curve II. Bicorn curve In geometry, the bicorn, also known as a cocked hat curve due to its resemblance to a bicorne, is a rational quartic curve defined by the equation It has two cusps and is symmetric about the y-axis. -
Mials P
Euclid's Algorithm 171 Euclid's Algorithm Horner’s method is a special case of Euclid's Algorithm which constructs, for given polyno- mials p and h =6 0, (unique) polynomials q and r with deg r<deg h so that p = hq + r: For variety, here is a nonstandard discussion of this algorithm, in terms of elimination. Assume that d h(t)=a0 + a1t + ···+ adt ;ad =06 ; and n p(t)=b0 + b1t + ···+ bnt : Then we seek a polynomial n−d q(t)=c0 + c1t + ···+ cn−dt for which r := p − hq has degree <d. This amounts to the square upper triangular linear system adc0 + ad−1c1 + ···+ a0cd = bd adc1 + ad−1c2 + ···+ a0cd+1 = bd+1 . adcn−d−1 + ad−1cn−d = bn−1 adcn−d = bn for the unknown coefficients c0;:::;cn−d which can be uniquely solved by back substitution since its diagonal entries all equal ad =0.6 19aug02 c 2002 Carl de Boor 172 18. Index Rough index for these notes 1-1:-5,2,8,40 cartesian product: 2 1-norm: 79 Cauchy(-Bunyakovski-Schwarz) 2-norm: 79 Inequality: 69 A-invariance: 125 Cauchy-Binet formula: -9, 166 A-invariant: 113 Cayley-Hamilton Theorem: 133 absolute value: 167 CBS Inequality: 69 absolutely homogeneous: 70, 79 Chaikin algorithm: 139 additive: 20 chain rule: 153 adjugate: 164 change of basis: -6 affine: 151 characteristic function: 7 affine combination: 148, 150 characteristic polynomial: -8, 130, 132, 134 affine hull: 150 circulant: 140 affine map: 149 codimension: 50, 53 affine polynomial: 152 coefficient vector: 21 affine space: 149 cofactor: 163 affinely independent: 151 column map: -6, 23 agrees with y at Λt:59 column space: 29 algebraic dual: 95 column -
CS321-001 Introduction to Numerical Methods
CS321-001 Introduction to Numerical Methods Lecture 3 Interpolation and Numerical Differentiation Professor Jun Zhang Department of Computer Science University of Kentucky Lexington, KY 40506-0633 Polynomial Interpolation Given a set of discrete values, how can we estimate other values between these data The method that we will use is called polynomial interpolation. We assume the data we had are from the evaluation of a smooth function. We may be able to use a polynomial p(x) to approximate this function, at least locally. A condition: the polynomial p(x) takes the given values at the given points (nodes), i.e., p(xi) = yi with 0 ≤ i ≤ n. The polynomial is said to interpolate the table, since we do not know the function. 2 Polynomial Interpolation Note that all the points are passed through by the curve 3 Polynomial Interpolation We do not know the original function, the interpolation may not be accurate 4 Order of Interpolating Polynomial A polynomial of degree 0, a constant function, interpolates one set of data If we have two sets of data, we can have an interpolating polynomial of degree 1, a linear function x x1 x x0 p(x) y0 y1 x0 x1 x1 x0 y1 y0 y0 (x x0 ) x1 x0 Review carefully if the interpolation condition is satisfied Interpolating polynomials can be written in several forms, the most well known ones are the Lagrange form and Newton form. Each has some advantages 5 Lagrange Form For a set of fixed nodes x0, x1, …, xn, the cardinal functions, l0, l1,…, ln, are defined as 0 if i j li (x j ) ij -
Polynomial Curves and Surfaces
Polynomial Curves and Surfaces Chandrajit Bajaj and Andrew Gillette September 8, 2010 Contents 1 What is an Algebraic Curve or Surface? 2 1.1 Algebraic Curves . .3 1.2 Algebraic Surfaces . .3 2 Singularities and Extreme Points 4 2.1 Singularities and Genus . .4 2.2 Parameterizing with a Pencil of Lines . .6 2.3 Parameterizing with a Pencil of Curves . .7 2.4 Algebraic Space Curves . .8 2.5 Faithful Parameterizations . .9 3 Triangulation and Display 10 4 Polynomial and Power Basis 10 5 Power Series and Puiseux Expansions 11 5.1 Weierstrass Factorization . 11 5.2 Hensel Lifting . 11 6 Derivatives, Tangents, Curvatures 12 6.1 Curvature Computations . 12 6.1.1 Curvature Formulas . 12 6.1.2 Derivation . 13 7 Converting Between Implicit and Parametric Forms 20 7.1 Parameterization of Curves . 21 7.1.1 Parameterizing with lines . 24 7.1.2 Parameterizing with Higher Degree Curves . 26 7.1.3 Parameterization of conic, cubic plane curves . 30 7.2 Parameterization of Algebraic Space Curves . 30 7.3 Automatic Parametrization of Degree 2 Curves and Surfaces . 33 7.3.1 Conics . 34 7.3.2 Rational Fields . 36 7.4 Automatic Parametrization of Degree 3 Curves and Surfaces . 37 7.4.1 Cubics . 38 7.4.2 Cubicoids . 40 7.5 Parameterizations of Real Cubic Surfaces . 42 7.5.1 Real and Rational Points on Cubic Surfaces . 44 7.5.2 Algebraic Reduction . 45 1 7.5.3 Parameterizations without Real Skew Lines . 49 7.5.4 Classification and Straight Lines from Parametric Equations . 52 7.5.5 Parameterization of general algebraic plane curves by A-splines . -
Lagrange & Newton Interpolation
Lagrange & Newton interpolation In this section, we shall study the polynomial interpolation in the form of Lagrange and Newton. Given a se- quence of (n +1) data points and a function f, the aim is to determine an n-th degree polynomial which interpol- ates f at these points. We shall resort to the notion of divided differences. Interpolation Given (n+1) points {(x0, y0), (x1, y1), …, (xn, yn)}, the points defined by (xi)0≤i≤n are called points of interpolation. The points defined by (yi)0≤i≤n are the values of interpolation. To interpolate a function f, the values of interpolation are defined as follows: yi = f(xi), i = 0, …, n. Lagrange interpolation polynomial The purpose here is to determine the unique polynomial of degree n, Pn which verifies Pn(xi) = f(xi), i = 0, …, n. The polynomial which meets this equality is Lagrange interpolation polynomial n Pnx=∑ l j x f x j j=0 where the lj ’s are polynomials of degree n forming a basis of Pn n x−xi x−x0 x−x j−1 x−x j 1 x−xn l j x= ∏ = ⋯ ⋯ i=0,i≠ j x j −xi x j−x0 x j−x j−1 x j−x j1 x j−xn Properties of Lagrange interpolation polynomial and Lagrange basis They are the lj polynomials which verify the following property: l x = = 1 i= j , ∀ i=0,...,n. j i ji {0 i≠ j They form a basis of the vector space Pn of polynomials of degree at most equal to n n ∑ j l j x=0 j=0 By setting: x = xi, we obtain: n n ∑ j l j xi =∑ j ji=0 ⇒ i =0 j=0 j=0 The set (lj)0≤j≤n is linearly independent and consists of n + 1 vectors. -
Construction of Rational Surfaces of Degree 12 in Projective Fourspace
Construction of Rational Surfaces of Degree 12 in Projective Fourspace Hirotachi Abo Abstract The aim of this paper is to present two different constructions of smooth rational surfaces in projective fourspace with degree 12 and sectional genus 13. In particular, we establish the existences of five different families of smooth rational surfaces in projective fourspace with the prescribed invariants. 1 Introduction Hartshone conjectured that only finitely many components of the Hilbert scheme of surfaces in P4 contain smooth rational surfaces. In 1989, this conjecture was positively solved by Ellingsrud and Peskine [7]. The exact bound for the degree is, however, still open, and hence the question concerning the exact bound motivates us to find smooth rational surfaces in P4. The goal of this paper is to construct five different families of smooth rational surfaces in P4 with degree 12 and sectional genus 13. The rational surfaces in P4 were previously known up to degree 11. In this paper, we present two different constructions of smooth rational surfaces in P4 with the given invariants. Both the constructions are based on the technique of “Beilinson monad”. Let V be a finite-dimensional vector space over a field K and let W be its dual space. The basic idea behind a Beilinson monad is to represent a given coherent sheaf on P(W ) as a homology of a finite complex whose objects are direct sums of bundles of differentials. The differentials in the monad are given by homogeneous matrices over an exterior algebra E = V V . To construct a Beilinson monad for a given coherent sheaf, we typically take the following steps: Determine the type of the Beilinson monad, that is, determine each object, and then find differentials in the monad. -
Mathematical Topics
A Mathematical Topics This chapter discusses most of the mathematical background needed for a thor ough understanding of the material presented in the book. It has been mentioned in the Preface, however, that math concepts which are only used once (such as the mediation operator and points vs. vectors) are discussed right where they are introduced. Do not worry too much about your difficulties in mathematics, I can assure you that mine a.re still greater. - Albert Einstein. A.I Fourier Transforms Our curves are functions of an arbitrary parameter t. For functions used in science and engineering, time is often the parameter (or the independent variable). We, therefore, say that a function g( t) is represented in the time domain. Since a typical function oscillates, we can think of it as being similar to a wave and we may try to represent it as a wave (or as a combination of waves). When this is done, we have the function G(f), where f stands for the frequency of the wave, and we say that the function is represented in the frequency domain. This turns out to be a useful concept, since many operations on functions are easy to carry out in the frequency domain. Transforming a function between the time and frequency domains is easy when the function is periodic, but it can also be done for certain non periodic functions. The present discussion is restricted to periodic functions. 1IIIt Definition: A function g( t) is periodic if (and only if) there exists a constant P such that g(t+P) = g(t) for all values of t (Figure A.la). -
A Brief Note on the Approach to the Conic Sections of a Right Circular Cone from Dynamic Geometry
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by EPrints Complutense A brief note on the approach to the conic sections of a right circular cone from dynamic geometry Eugenio Roanes–Lozano Abstract. Nowadays there are different powerful 3D dynamic geometry systems (DGS) such as GeoGebra 5, Calques 3D and Cabri Geometry 3D. An obvious application of this software that has been addressed by several authors is obtaining the conic sections of a right circular cone: the dynamic capabilities of 3D DGS allows to slowly vary the angle of the plane w.r.t. the axis of the cone, thus obtaining the different types of conics. In all the approaches we have found, a cone is firstly constructed and it is cut through variable planes. We propose to perform the construction the other way round: the plane is fixed (in fact it is a very convenient plane: z = 0) and the cone is the moving object. This way the conic is expressed as a function of x and y (instead of as a function of x, y and z). Moreover, if the 3D DGS has algebraic capabilities, it is possible to obtain the implicit equation of the conic. Mathematics Subject Classification (2010). Primary 68U99; Secondary 97G40; Tertiary 68U05. Keywords. Conics, Dynamic Geometry, 3D Geometry, Computer Algebra. 1. Introduction Nowadays there are different powerful 3D dynamic geometry systems (DGS) such as GeoGebra 5 [2], Calques 3D [7, 10] and Cabri Geometry 3D [11]. Focusing on the most widespread one (GeoGebra 5) and conics, there is a “conics” icon in the ToolBar. -
Chapter 9: Conics Section 9.1 Ellipses
Chapter 9: Conics Section 9.1 Ellipses ..................................................................................................... 579 Section 9.2 Hyperbolas ............................................................................................... 597 Section 9.3 Parabolas and Non-Linear Systems ......................................................... 617 Section 9.4 Conics in Polar Coordinates..................................................................... 630 In this chapter, we will explore a set of shapes defined by a common characteristic: they can all be formed by slicing a cone with a plane. These families of curves have a broad range of applications in physics and astronomy, from describing the shape of your car headlight reflectors to describing the orbits of planets and comets. Section 9.1 Ellipses The National Statuary Hall1 in Washington, D.C. is an oval-shaped room called a whispering chamber because the shape makes it possible for sound to reflect from the walls in a special way. Two people standing in specific places are able to hear each other whispering even though they are far apart. To determine where they should stand, we will need to better understand ellipses. An ellipse is a type of conic section, a shape resulting from intersecting a plane with a cone and looking at the curve where they intersect. They were discovered by the Greek mathematician Menaechmus over two millennia ago. The figure below2 shows two types of conic sections. When a plane is perpendicular to the axis of the cone, the shape of the intersection is a circle. A slightly titled plane creates an oval-shaped conic section called an ellipse. 1 Photo by Gary Palmer, Flickr, CC-BY, https://www.flickr.com/photos/gregpalmer/2157517950 2 Pbroks13 (https://commons.wikimedia.org/wiki/File:Conic_sections_with_plane.svg), “Conic sections with plane”, cropped to show only ellipse and circle by L Michaels, CC BY 3.0 This chapter is part of Precalculus: An Investigation of Functions © Lippman & Rasmussen 2020.