Multivariable Calculus Math 21A
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Lecture 11 Line Integrals of Vector-Valued Functions (Cont'd)
Lecture 11 Line integrals of vector-valued functions (cont’d) In the previous lecture, we considered the following physical situation: A force, F(x), which is not necessarily constant in space, is acting on a mass m, as the mass moves along a curve C from point P to point Q as shown in the diagram below. z Q P m F(x(t)) x(t) C y x The goal is to compute the total amount of work W done by the force. Clearly the constant force/straight line displacement formula, W = F · d , (1) where F is force and d is the displacement vector, does not apply here. But the fundamental idea, in the “Spirit of Calculus,” is to break up the motion into tiny pieces over which we can use Eq. (1 as an approximation over small pieces of the curve. We then “sum up,” i.e., integrate, over all contributions to obtain W . The total work is the line integral of the vector field F over the curve C: W = F · dx . (2) ZC Here, we summarize the main steps involved in the computation of this line integral: 1. Step 1: Parametrize the curve C We assume that the curve C can be parametrized, i.e., x(t)= g(t) = (x(t),y(t), z(t)), t ∈ [a, b], (3) so that g(a) is point P and g(b) is point Q. From this parametrization we can compute the velocity vector, ′ ′ ′ ′ v(t)= g (t) = (x (t),y (t), z (t)) . (4) 1 2. Step 2: Compute field vector F(g(t)) over curve C F(g(t)) = F(x(t),y(t), z(t)) (5) = (F1(x(t),y(t), z(t), F2(x(t),y(t), z(t), F3(x(t),y(t), z(t))) , t ∈ [a, b] . -
Quadratic Approximation at a Stationary Point Let F(X, Y) Be a Given
Multivariable Calculus Grinshpan Quadratic approximation at a stationary point Let f(x; y) be a given function and let (x0; y0) be a point in its domain. Under proper differentiability conditions one has f(x; y) = f(x0; y0) + fx(x0; y0)(x − x0) + fy(x0; y0)(y − y0) 1 2 1 2 + 2 fxx(x0; y0)(x − x0) + fxy(x0; y0)(x − x0)(y − y0) + 2 fyy(x0; y0)(y − y0) + higher−order terms: Let (x0; y0) be a stationary (critical) point of f: fx(x0; y0) = fy(x0; y0) = 0. Then 2 2 f(x; y) = f(x0; y0) + A(x − x0) + 2B(x − x0)(y − y0) + C(y − y0) + higher−order terms, 1 1 1 1 where A = 2 fxx(x0; y0);B = 2 fxy(x0; y0) = 2 fyx(x0; y0), and C = 2 fyy(x0; y0). Assume for simplicity that (x0; y0) = (0; 0) and f(0; 0) = 0. [ This can always be achieved by translation: f~(x; y) = f(x0 + x; y0 + y) − f(x0; y0). ] Then f(x; y) = Ax2 + 2Bxy + Cy2 + higher−order terms. Thus, provided A, B, C are not all zero, the graph of f near (0; 0) resembles the quadric surface z = Ax2 + 2Bxy + Cy2: Generically, this quadric surface is either an elliptic or a hyperbolic paraboloid. We distinguish three scenarios: * Elliptic paraboloid opening up, (0; 0) is a point of local minimum. * Elliptic paraboloid opening down, (0; 0) is a point of local maximum. * Hyperbolic paraboloid, (0; 0) is a saddle point. It should certainly be possible to tell which case we are dealing with by looking at the coefficients A, B, and C, and this is the idea behind the Second Partials Test. -
Chapter 11. Three Dimensional Analytic Geometry and Vectors
Chapter 11. Three dimensional analytic geometry and vectors. Section 11.5 Quadric surfaces. Curves in R2 : x2 y2 ellipse + =1 a2 b2 x2 y2 hyperbola − =1 a2 b2 parabola y = ax2 or x = by2 A quadric surface is the graph of a second degree equation in three variables. The most general such equation is Ax2 + By2 + Cz2 + Dxy + Exz + F yz + Gx + Hy + Iz + J =0, where A, B, C, ..., J are constants. By translation and rotation the equation can be brought into one of two standard forms Ax2 + By2 + Cz2 + J =0 or Ax2 + By2 + Iz =0 In order to sketch the graph of a quadric surface, it is useful to determine the curves of intersection of the surface with planes parallel to the coordinate planes. These curves are called traces of the surface. Ellipsoids The quadric surface with equation x2 y2 z2 + + =1 a2 b2 c2 is called an ellipsoid because all of its traces are ellipses. 2 1 x y 3 2 1 z ±1 ±2 ±3 ±1 ±2 The six intercepts of the ellipsoid are (±a, 0, 0), (0, ±b, 0), and (0, 0, ±c) and the ellipsoid lies in the box |x| ≤ a, |y| ≤ b, |z| ≤ c Since the ellipsoid involves only even powers of x, y, and z, the ellipsoid is symmetric with respect to each coordinate plane. Example 1. Find the traces of the surface 4x2 +9y2 + 36z2 = 36 1 in the planes x = k, y = k, and z = k. Identify the surface and sketch it. Hyperboloids Hyperboloid of one sheet. The quadric surface with equations x2 y2 z2 1. -
Pencils of Quadrics
Europ. J. Combinatorics (1988) 9, 255-270 Intersections in Projective Space II: Pencils of Quadrics A. A. BRUEN AND J. w. P. HIRSCHFELD A complete classification is given of pencils of quadrics in projective space of three dimensions over a finite field, where each pencil contains at least one non-singular quadric and where the base curve is not absolutely irreducible. This leads to interesting configurations in the space such as partitions by elliptic quadrics and by lines. 1. INTRODUCTION A pencil of quadrics in PG(3, q) is non-singular if it contains at least one non-singular quadric. The base of a pencil is a quartic curve and is reducible if over some extension of GF(q) it splits into more than one component: four lines, two lines and a conic, two conics, or a line and a twisted cubic. In order to contain no points or to contain some line over GF(q), the base is necessarily reducible. In the main part of this paper a classification is given of non-singular pencils ofquadrics in L = PG(3, q) with a reducible base. This leads to geometrically interesting configur ations obtained from elliptic and hyperbolic quadrics, such as spreads and partitions of L. The remaining part of the paper deals with the case when the base is not reducible and is therefore a rational or elliptic quartic. The number of points in the elliptic case is then governed by the Hasse formula. However, we are able to derive an interesting combi natorial relationship, valid also in higher dimensions, between the number of points in the base curve and the number in the base curve of a dual pencil. -
Simple Infinite Presentations for the Mapping Class Group of a Compact
SIMPLE INFINITE PRESENTATIONS FOR THE MAPPING CLASS GROUP OF A COMPACT NON-ORIENTABLE SURFACE RYOMA KOBAYASHI Abstract. Omori and the author [6] have given an infinite presentation for the mapping class group of a compact non-orientable surface. In this paper, we give more simple infinite presentations for this group. 1. Introduction For g ≥ 1 and n ≥ 0, we denote by Ng,n the closure of a surface obtained by removing disjoint n disks from a connected sum of g real projective planes, and call this surface a compact non-orientable surface of genus g with n boundary components. We can regard Ng,n as a surface obtained by attaching g M¨obius bands to g boundary components of a sphere with g + n boundary components, as shown in Figure 1. We call these attached M¨obius bands crosscaps. Figure 1. A model of a non-orientable surface Ng,n. The mapping class group M(Ng,n) of Ng,n is defined as the group consisting of isotopy classes of all diffeomorphisms of Ng,n which fix the boundary point- wise. M(N1,0) and M(N1,1) are trivial (see [2]). Finite presentations for M(N2,0), M(N2,1), M(N3,0) and M(N4,0) ware given by [9], [1], [14] and [16] respectively. Paris-Szepietowski [13] gave a finite presentation of M(Ng,n) with Dehn twists and arXiv:2009.02843v1 [math.GT] 7 Sep 2020 crosscap transpositions for g + n > 3 with n ≤ 1. Stukow [15] gave another finite presentation of M(Ng,n) with Dehn twists and one crosscap slide for g + n > 3 with n ≤ 1, applying Tietze transformations for the presentation of M(Ng,n) given in [13]. -
Calculus & Analytic Geometry
TQS 126 Spring 2008 Quinn Calculus & Analytic Geometry III Quadratic Equations in 3-D Match each function to its graph 1. 9x2 + 36y2 +4z2 = 36 2. 4x2 +9y2 4z2 =0 − 3. 36x2 +9y2 4z2 = 36 − 4. 4x2 9y2 4z2 = 36 − − 5. 9x2 +4y2 6z =0 − 6. 9x2 4y2 6z =0 − − 7. 4x2 + y2 +4z2 4y 4z +36=0 − − 8. 4x2 + y2 +4z2 4y 4z 36=0 − − − cone • ellipsoid • elliptic paraboloid • hyperbolic paraboloid • hyperboloid of one sheet • hyperboloid of two sheets 24 TQS 126 Spring 2008 Quinn Calculus & Analytic Geometry III Parametric Equations (§10.1) and Vector Functions (§13.1) Definition. If x and y are given as continuous function x = f(t) y = g(t) over an interval of t-values, then the set of points (x, y)=(f(t),g(t)) defined by these equation is a parametric curve (sometimes called aplane curve). The equations are parametric equations for the curve. Often we think of parametric curves as describing the movement of a particle in a plane over time. Examples. x = 2cos t x = et 0 t π 1 t e y = 3sin t ≤ ≤ y = ln t ≤ ≤ Can we find parameterizations of known curves? the line segment circle x2 + y2 =1 from (1, 3) to (5, 1) Why restrict ourselves to only moving through planes? Why not space? And why not use our nifty vector notation? 25 Definition. If x, y, and z are given as continuous functions x = f(t) y = g(t) z = h(t) over an interval of t-values, then the set of points (x,y,z)= (f(t),g(t), h(t)) defined by these equation is a parametric curve (sometimes called a space curve). -
1 Curl and Divergence
Sections 15.5-15.8: Divergence, Curl, Surface Integrals, Stokes' and Divergence Theorems Reeve Garrett 1 Curl and Divergence Definition 1.1 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the divergence of F on D is @ @ @ div F := r · F = ; ; · hf; g; hi = f + g + h ; @x @y @z x y z @ @ @ where r = h @x ; @y ; @z i is the del operator. If div F = 0, we say that F is source free. Note that these definitions (divergence and source free) completely agrees with their 2D analogues in 15.4. Theorem 1.2 Suppose that F is a radial vector field, i.e. if r = hx; y; zi, then for some real number p, r hx;y;zi 3−p F = jrjp = (x2+y2+z2)p=2 , then div F = jrjp . Theorem 1.3 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the curl of F on D is curl F := r × F = hhy − gz; fz − hx; gx − fyi: If curl F = 0, then we say F is irrotational. Note that gx − fy is the 2D curl as defined in section 15.4. Therefore, if we fix a point (a; b; c), [gx − fy](a; b; c), measures the rotation of F at the point (a; b; c) in the plane z = c. Similarly, [hy − gz](a; b; c) measures the rotation of F in the plane x = a at (a; b; c), and [fz − hx](a; b; c) measures the rotation of F in the plane y = b at the point (a; b; c). -
A Brief Tour of Vector Calculus
A BRIEF TOUR OF VECTOR CALCULUS A. HAVENS Contents 0 Prelude ii 1 Directional Derivatives, the Gradient and the Del Operator 1 1.1 Conceptual Review: Directional Derivatives and the Gradient........... 1 1.2 The Gradient as a Vector Field............................ 5 1.3 The Gradient Flow and Critical Points ....................... 10 1.4 The Del Operator and the Gradient in Other Coordinates*............ 17 1.5 Problems........................................ 21 2 Vector Fields in Low Dimensions 26 2 3 2.1 General Vector Fields in Domains of R and R . 26 2.2 Flows and Integral Curves .............................. 31 2.3 Conservative Vector Fields and Potentials...................... 32 2.4 Vector Fields from Frames*.............................. 37 2.5 Divergence, Curl, Jacobians, and the Laplacian................... 41 2.6 Parametrized Surfaces and Coordinate Vector Fields*............... 48 2.7 Tangent Vectors, Normal Vectors, and Orientations*................ 52 2.8 Problems........................................ 58 3 Line Integrals 66 3.1 Defining Scalar Line Integrals............................. 66 3.2 Line Integrals in Vector Fields ............................ 75 3.3 Work in a Force Field................................. 78 3.4 The Fundamental Theorem of Line Integrals .................... 79 3.5 Motion in Conservative Force Fields Conserves Energy .............. 81 3.6 Path Independence and Corollaries of the Fundamental Theorem......... 82 3.7 Green's Theorem.................................... 84 3.8 Problems........................................ 89 4 Surface Integrals, Flux, and Fundamental Theorems 93 4.1 Surface Integrals of Scalar Fields........................... 93 4.2 Flux........................................... 96 4.3 The Gradient, Divergence, and Curl Operators Via Limits* . 103 4.4 The Stokes-Kelvin Theorem..............................108 4.5 The Divergence Theorem ...............................112 4.6 Problems........................................114 List of Figures 117 i 11/14/19 Multivariate Calculus: Vector Calculus Havens 0. -
Appendix a Short Course in Taylor Series
Appendix A Short Course in Taylor Series The Taylor series is mainly used for approximating functions when one can identify a small parameter. Expansion techniques are useful for many applications in physics, sometimes in unexpected ways. A.1 Taylor Series Expansions and Approximations In mathematics, the Taylor series is a representation of a function as an infinite sum of terms calculated from the values of its derivatives at a single point. It is named after the English mathematician Brook Taylor. If the series is centered at zero, the series is also called a Maclaurin series, named after the Scottish mathematician Colin Maclaurin. It is common practice to use a finite number of terms of the series to approximate a function. The Taylor series may be regarded as the limit of the Taylor polynomials. A.2 Definition A Taylor series is a series expansion of a function about a point. A one-dimensional Taylor series is an expansion of a real function f(x) about a point x ¼ a is given by; f 00ðÞa f 3ðÞa fxðÞ¼faðÞþf 0ðÞa ðÞþx À a ðÞx À a 2 þ ðÞx À a 3 þÁÁÁ 2! 3! f ðÞn ðÞa þ ðÞx À a n þÁÁÁ ðA:1Þ n! © Springer International Publishing Switzerland 2016 415 B. Zohuri, Directed Energy Weapons, DOI 10.1007/978-3-319-31289-7 416 Appendix A: Short Course in Taylor Series If a ¼ 0, the expansion is known as a Maclaurin Series. Equation A.1 can be written in the more compact sigma notation as follows: X1 f ðÞn ðÞa ðÞx À a n ðA:2Þ n! n¼0 where n ! is mathematical notation for factorial n and f(n)(a) denotes the n th derivation of function f evaluated at the point a. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
How to Compute Line Integrals Scalar Line Integral Vector Line Integral
How to Compute Line Integrals Northwestern, Spring 2013 Computing line integrals can be a tricky business. First, there's two types of line integrals: scalar line integrals and vector line integrals. Although these are related, we compute them in different ways. Also, there's two theorems flying around, Green's Theorem and the Fundamental Theorem of Line Integrals (as I've called it), which give various other ways of computing line integrals. Knowing what to use when and where and why can be confusing, so hopefully this will help to keep things straight. I won't say much about what line integrals mean geometrically or otherwise. Hopefully this is something you have some idea about from class or the book, but I'm always happy to talk about this more in office hours. Here the focus is on computation. Key Point: When you are trying to apply one of these techniques, make sure the technique you are using is actually applicable! (This was a major source of confusion on Quiz 5.) Scalar Line Integral • Arises when integrating a function f over a curve C. • Notationally, is always denoted by something like Z f ds; C where the s in ds is just a normal s, as opposed to ds or d~s. • To compute, find parametric equations x(t); a ≤ t ≤ b for C and use Z Z b f ds = f(x(t)) kx0(t)k dt: C a • In the special case where f is the constant function 1, Z ds = arclength of C: C • For scalar line integrals, the orientation (i.e. -
Recognizing Surfaces
RECOGNIZING SURFACES Ivo Nikolov and Alexandru I. Suciu Mathematics Department College of Arts and Sciences Northeastern University Abstract The subject of this poster is the interplay between the topology and the combinatorics of surfaces. The main problem of Topology is to classify spaces up to continuous deformations, known as homeomorphisms. Under certain conditions, topological invariants that capture qualitative and quantitative properties of spaces lead to the enumeration of homeomorphism types. Surfaces are some of the simplest, yet most interesting topological objects. The poster focuses on the main topological invariants of two-dimensional manifolds—orientability, number of boundary components, genus, and Euler characteristic—and how these invariants solve the classification problem for compact surfaces. The poster introduces a Java applet that was written in Fall, 1998 as a class project for a Topology I course. It implements an algorithm that determines the homeomorphism type of a closed surface from a combinatorial description as a polygon with edges identified in pairs. The input for the applet is a string of integers, encoding the edge identifications. The output of the applet consists of three topological invariants that completely classify the resulting surface. Topology of Surfaces Topology is the abstraction of certain geometrical ideas, such as continuity and closeness. Roughly speaking, topol- ogy is the exploration of manifolds, and of the properties that remain invariant under continuous, invertible transforma- tions, known as homeomorphisms. The basic problem is to classify manifolds according to homeomorphism type. In higher dimensions, this is an impossible task, but, in low di- mensions, it can be done. Surfaces are some of the simplest, yet most interesting topological objects.