# Lecture notes for Math Multivariable Calculus

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1 Lecture notes for Math Multivariable alculus J. Dimock Dept. of Mathematics SUNY at Buffalo Buffalo, NY December 4, 2012 ontents 1 multivariable calculus vectors functions of several variables limits partial derivatives derivatives the chain rule implicit function theorem -I implicit function theorem -II inverse functions inverse function theorem maxima and minima differentiation under the integral sign Leibniz rule calculus of variations vector calculus vectors vector-valued functions other coordinate systems line integrals double integrals triple integrals parametrized surfaces

2 2.8 surface area surface integrals change of variables in R change of variables in R derivatives in R gradient divergence theorem applications more line integrals Stoke s theorem still more line integrals more applications general coordinate systems complex variables complex numbers definitions and properties polar form functions special functions derivatives auchy-riemann equations analyticity complex line integrals properties of line integrals auchy s theorem auchy integral formula higher derivatives auchy inequalities real integrals Fourier and Laplace transforms

3 1 multivariable calculus 1.1 vectors We start with some definitions. A real number x is positive, zero, or negative and is rational or irrational. We denote R = set of all real numbers x (1) The real numbers label the points on a line once we pick an origin and a unit of length. Real numbers are also called scalars Next define R 2 = all pairs of real numbers x = (x 1, x 2 ) (2) The elements of R 2 label points in the plane once we pick an origin and a pair of orthogonal axes. Elements of R 2 are also called (2-dimensional) vectors and can be represented by arrows from the origin to the point represented. Next define R 3 = all triples of real numbers x = (x 1, x 2, x 3 ) (3) The elements of R 3 label points in space once we pick an origin and three orthogonal axes. Elements of R 3 are (3-dimensional) vectors. Especially for R 3 one might emphasize that x is a vector by writing it in bold face x = (x 1, x 2, x 3 ) or with an arrow x = (x 1, x 2, x 3 ) but we refrain from doing this for the time being. Generalizing still further we define R n = all n-tuples of real numbers x = (x 1, x 2,..., x n ) (4) are the points in n-dimensional space and are also called (n- The elements of R n dimensional) vectors Given a vector x = (x 1,..., x n ) in R n and a scalar α R the product is the vector αx = (αx 1,..., αx n ) (5) Another vector y = (y 1,..., y n ) can to added to x to give a vector x + y = (x 1 + y 1,..., x n + y n ) (6) Because elements of R n can be multiplied by a scalar and added it is called a vector space. We can also subtract vectors defining x y = x + ( 1)y and then x y = (x 1 y 1,..., x n y n ) (7) For two or three dimensional vectors these operations have a geometric interpretation. αx is a vector in the same direction as x (opposite direction if α < 0) with length 3

4 Figure 1: vector operations increased by α. The vector x + y can be found by completing a parallelogram with sides x, y and taking the diagonal, or by putting the tail of y on the head of x and drawing the arrow from the tail of x to the head of y. The vector x y is found by drawing x + ( 1)y. Alternatively if the tail of x y put a the head of y then the arrow goes from the head of y to the head of x. See figure 1. A vector x = (x 1,..., x n ) has a length which is x = length of x = x x 2 n (8) Since x y goes from the point y to the point x, the length of this vector is the distance between the points: x y = distance between x and y = (x 1 y 1 ) (x n y n ) 2 (9) by One can also form the dot product of vectors x, y in R n. The result is a scalar given x y = x 1 y 1 + x 2 y x n y n (10) Then we have x x = x 2 (11) 4

5 1.2 functions of several variables We are interested in functions f from R n to R m (or more generally from a subset D R n to R m called the domain of the function). A function f assigns to each x R n a point y R m and we write y = f(x) (12) The set of all such points y is the range of the function. Each component of y = (y 1,..., y m ) is real-valued function of x R n written y i = f i (x) and the function can also be written as the collection of n functions y 1 = f 1 (x),, y m = f m (x) (13) If we also write out the components of x = (x 1,..., x n ), then are function can be written as m functions of n variables each: y 1 =f 1 (x 1,..., x n ) y 2 =f 2 (x 1,..., x n )... y m =f m (x 1,..., x n ) The graph of the function is all pairs (x, y) with y = f(x). It is a subset of R n+m. special cases: 1. n = 1, m = 2 (or m = 3). The function has the form (14) y 1 = f 1 (x) y 2 = f 2 (x) (15) In this case the range of the function is a curve in R n = 2, m = 1. Then function has the form The graph of the function is a surface in R n = 2, m = 3. The function has the form y = f(x 1, x 2 ) (16) y 1 =f 1 (x 1, x 2 ) y 2 =f 2 (x 1, x 2 ) y 3 =f 3 (x 1, x 2 ) The range of the function is a surface in R n = 3, m = 3. The function has the form y 1 =f 1 (x 1, x 2, x 3 ) y 2 =f 2 (x 1, x 2, x 3 ) y 3 =f 3 (x 1, x 2.x 3 ) The function assigns a vector to each point in space and is called a vector field. 5 (17) (18)

6 1.3 limits onsider a function y = f(x) from R n to R m (or possibly a subset of R n ). Let x 0 = (x 0 1,... x 0 n) be a point in R n and let y 0 = (y 0 1,..., y 0 m) be a point in R m. We say that y 0 is the limit of f as x goes to x 0, written lim f(x) = y0 (19) x x 0 if for every ɛ > 0 there exists a δ > 0 so that if x x 0 < δ then f(x) y 0 < ɛ. The function is continuous at x 0 if lim f(x) = f(x0 ) (20) x x 0 The function is continuous if it is continuous at every point in its domain. If f, g are continuous at x 0 then so are f ± g. If f, g are scalars (i.e. if m = 1) then the the product fg is defined and continuous at x 0. If f, g are scalars and g(x 0 ) 0 then f/g is defined near x 0 and and continuous at x partial derivatives At first suppose f is a function from R 2 to R written z = f(x, y) (21) We define the partial derivative of f with respect to x at (x 0, y 0 ) to be f x (x 0, y 0 ) = lim h 0 f(x 0 + h, y 0 ) f(x 0, y 0 ) h (22) if the limit exists. It is the same as the ordinary derivative with y fixed at y 0, i.e [ ] d dx f(x, y 0) (23) x=x 0 We also define the partial derivative of f with respect to y at (x 0, y 0 ) to be f y (x 0, y 0 ) = lim h 0 f(x 0, y 0 + h) f(x 0, y 0 ) h (24) if the limit exists. It is the same as the ordinary derivative with x fixed at x 0, i.e [ ] d dy f(x 0, y) (25) y=y 0 6

7 We also use the notation f x = z x f y = z y ( or f ) x or z x ( or f ) (26) y or z y If we let (x 0, y 0 ) vary the partial derivatives are also functions and we can take second partial derivatives like ( ) z (f x ) x f xx also written = 2 z (27) x x x 2 The four second partial derivatives are f xx = 2 z f x 2 xy = 2 z y x f yx = 2 z x y f yy = 2 z y 2 (28) Usually f xy = f yx for we have Theorem 1 If f x, f y, f xy, f yx exist and are continuous near (x 0, y 0 ) (i.e in a little disc centered on (x 0, y 0 ) ) then f xy (x 0, y 0 ) = f yx (x 0, y 0 ) (29) Example: onsider f(x, y) = 3x 2 y + 4xy 3. Then f x = 6xy + 4y 3 f y = 3x xy 2 f xy = 6x + 12y 2 f yx = 6x + 12y 2 (30) We also have partial derivatives for a function f from R n to R written y = f(x 1,... x n ). The partial derivative with respect to x i at (x 0 1,... x 0 n) is f xi (x 0 1,... x 0 n) = lim h 0 f(x 0 1,..., x 0 i + h,..., x 0 n) f(x 0 1,... x 0 n) h (31) It is also written f xi = y x i (32) 7

8 1.5 derivatives A function z = f(x, y) is said to be differentiable at (x 0, y 0 ) if it can be well-approximated by a linear function near that point. This means there should be constants a, b such that f(x, y) = f(x 0, y 0 ) + a(x x 0 ) + b(y y 0 ) + ɛ(x, y) (33) where the error term ɛ(x, y) is continuous at (x 0, y 0 ) and ɛ(x, y) 0 as (x, y) (x 0, y 0 ) faster than the distance between the points: ɛ(x, y) lim (x,y) (x 0,y 0 ) (x, y) (x 0, y 0 ) = 0 (34) Note that differentiable implies continuous. Suppose it is true and take (x, y) = (x 0 + h, y 0 ). Then f(x 0 + h, y 0 ) = f(x 0, y 0 ) + ah + ɛ(x 0 + h, y 0 ) (35) and so f(x 0 + h, y 0 ) f(x 0, y 0 ) = a + ɛ(x 0 + h, y 0 ) (36) h h Taking the limit h 0 we see that f x (x 0, y 0 ) exists and equals a. Similarly if we take (x, y) = (x 0, y 0 + h) we find that f y (x 0, y 0 ) exists and equals b. Thus if f is differentiable then f(x, y) = f(x 0, y 0 ) + f x (x 0, y 0 )(x x 0 ) + f y (x 0, y 0 )(y y 0 ) + ɛ(x, y) (37) where ɛ satisfies the above condition. The linear approximation is the function z = f(x 0, y 0 ) + f x (x 0, y 0 )(x x 0 ) + f y (x 0, y 0 )(y y 0 ) (38) The graph of the linear approximation is a plane called the tangent plane. See figure 2 It is possible that the partial derivatives f x (x 0, y 0 ) and f y (x 0, y 0 ) exist but still the function is not differentiable as the following example shows example. Define a function by Then f(x, y) = { 1 x = 0 or y = 0 0 otherwise (39) f x (0, 0) = 0 f y (0, 0) = 0 (40) But the function cannot be differentiable at (0, 0) since it is not continuous there. It is not continuous since for example lim f(t, t) = 0 f(0, 0) = 1 (41) t 0 However the following is true: 8

9 Figure 2: tangent plane Theorem 2 If the partial derivatives f x, f y exist and are continuous near (x 0, y 0 ) (i.e in a little disc centered on (x 0, y 0 )) then f is differentiable at (x 0, y 0 ). problem: Show the function f(x, y) = y 3 + 3x 2 y 2 is differentiable at any point and find the linear approximation (tangent plane) at (1, 1). solution: This has partial derivatives f x (x, y) = 6xy 2 f y (x, y) = 3y 2 + 6x 2 y (42) at any point and they are continuous. Thus the function is differentiable. The tangent plane at (1, 1) is z =f(1, 1) + f x (1, 1)(x 1) + f y (1, 1)(y 1) =4 + 6(x 1) + 9(y 1) =6x + 9y 11 (43) Next consider a function from R n to R written y = f(x) = f(x 1,..., x n ). We say f is differentiable at x 0 = (x 0 1,... x 0 n) if there is a vector a = (a 1,..., a n ) such that f(x) = f(x 0 ) + a (x x 0 ) + ɛ(x) (44) 9

10 where as before lim x x 0 If it is true then we find as before that the vector must be ɛ(x) x x 0 = 0 (45) a = (f x1 (x 0 ),..., f xn (x 0 )) ( f)(x 0 ) (46) also called the gradient of f at x 0. Thus we have f(x) = f(x 0 ) + ( f)(x 0 ) (x x 0 ) + ɛ(x) (47) Finally consider a function y = f(x) from R n to R m. We write the points and the function as column vectors: y 1 f 1 (x) x 1 y =. =. x =. (48) y m f m (x) x n The function is differentiable at x 0 is there is an m n matrix (m rows, n columns) A such that f(x) = f(x 0 ) + A(x x 0 ) + ɛ(x) (49) where the error ɛ(x) R m satisfies lim x x 0 ɛ(x) x x 0 = 0 (50) By considering each component separately we find that the i th row of A must be the the gradient of f i at x 0. Thus f 1,x1 (x 0 ) f 1,xn (x 0 ) A = Df(x 0 ).. (51) f m,x1 (x 0 ) f m,xn (x 0 ) This matrix Df(x 0 ) made up of all the partial derivatives of f at x 0 is the derivative of f at x 0. Thus we have The derivative is also written f(x) = f(x 0 ) + Df(x 0 )(x x 0 ) + ɛ(x) (52) Df y 1 / x 1 y 1 / x n.. y m / x 1 y m / x n (53) 10

11 problem: onsider the function from R 2 to R 2 given by ( ) ( ) ( y1 f1 (x) (x1 + x = = 2 + 1) 2 f 2 (x) x 1 (x 2 + 3) Find the linear approximation at (0, 0) y 2 ) (54) solution: The derivative is ( y1 / x Df = 1 y 1 / x 2 y 2 / x 1 y 2 / x 2 ) ( 2(x1 + x = 2 + 1) 2(x 1 + x 2 + 1) x x 1 ) (55) The linear approximation is y = f(0) + Df(0)(x 0) (56) or ( y1 y 2 ) = ( 1 0 ) + ( ) ( x1 x 2 ) ( 2x1 + 2x = x 1 ) (57) 1.6 the chain rule If f : R n R m (i.e. f is a function from R n to R m ) and p : R k R n, then there is a composite function h : R k R m defined by h(x) = f(p(x)) (58) and we also write h = f p. We can represent the situation by the diagram: R k p h R n f R m The chain gives a formula for the derivatives of the composite function h in terms of the derivatives of f and p. We start with some special cases. k=1,n=2,m=1 In this case the functions have the form u =f(x, y) x =p 1 (t) y = p 2 (t) (59) and the composite is u = h(t) = f(p 1 (t), p 2 (t)) (60) 11

12 Theorem 3 (chain rule) If f and p are differentiable, then so is the composite h = f p and the derivative is h (t) = f x (p 1 (t), p 2 (t))p 1(t) + f y (p 1 (t), p 2 (t))p 1(t) (61) The idea of the proof is as follows. Since f is differentiable h(t + t) h(t) =f((p 1 (t + t), p 2 (t + t)) f(p 1 (t), p 2 (t)) ( ) ( ) =f x (p 1 (t), p 2 (t)) p 1 (t + t) p 2 (t) + f y (p 1 (t), p 2 (t)) p 2 (t + t) p 2 (t) +ɛ(p 1 (t + t), p 2 (t + t)) Now divide by t and let t 0. One has to show that the error term goes to zero and the result follows. (62) This form of the chain rule can also be written in the concise form du dt = u dx x dt + u dy y dt (63) But one must keep in mind that u/ x and u/ y are to be evaluated at x = p 1 (t), y = p 2 (t). Also note that on the left u stands for the function u = h(t) while on the right it stands for the function u = f(x, y). example: Suppose u = x 2 + y 2 and x = cos t, y = sin t. Find du/dt. We have du dt = u dx x dt + u dy y dt =2x( sin t) + 2y cos t =2 cos t( sin t) + 2 sin t cos t =0 (64) (This is to be expected since the composite is u = 1). example: Suppose u = 2 + x 2 + y 2 and x = e t, y = e 2t. Find du/dt at t = 0. At t = 0 we have x = 1, y = 1 and so du dt = u dx x dt + u dy y dt x y = 2 + x2 + y 2 et x2 + y 2 2e2t (65) = =

13 k=1,n,m=1 In this case the functions have the form u = f(x 1,... x n ) x 1 = p 1 (t),..., x n = p n (t) (66) and the composite is The chain rule says u = h(t) = f(p 1 (t),, p n (t)) (67) h (t) = f x1 (p 1 (t),, p n (t))p 1(t) + + f xn (p 1 (t),, p n (t))p n(t) (68) It can also be written du dt = u dx 1 x 1 dt + + u dx n x n dt k=2,n=2,m=2 In this case the functions have the form u = f 1 (x, y) v = f 2 (x, y) x = p 1 (s, t) y = p 2 (s, t) (69) (70) and the composite is u = h 1 (s, t) = f 1 (p 1 (s, t), p 2 (s, t)) v = h 2 (s, t) = f 2 (p 1 (s, t), p 2 (s, t)) (71) Taking partial derivatives with respect to s, t we can use the formula from the case k=1,n=2,m=1 to obtain u s = u x x s + u y y s u t = u x x t + u y y t v s = v x x s + v y y s v t = v x x t + v y y t (72) Here derivatives with respect to x, y are to be evaluated at x = p 1 (s, t), y = p 2 (s, t). This can also be written as a matrix product: ( ) ( ) ( ) u/ s u/ t u/ x u/ y x/ s x/ t = (73) v/ s v/ t v/ x v/ y y/ s y/ t 13

14 These matrices are just the derivatives of the various functions and the last equation can be written (Dh)(s, t) = (Df)(p(s, t))(dp)(s, t) (74) The last form holds in the general case. If p : R k R n and f : R n R m are differentiable then the composite function h = f p : R k R m is differentiable and (Dh)(x) }{{} m k matrix = (Df)(p(x)) }{{} m n matrix (Dp)(x) }{{} n k matrix (75) In other words the derivative of a composite is the matrix product of the derivatives of the two elements. All forms of the chain rule are special cases of this equation. 1.7 implicit function theorem -I Suppose we have an equation of the form f(x, y, z) = 0 (76) an we solve it for z?. More precisely, is it the case that for each x, y in some domain there is a unique z so that f(x, y, z) = 0? If so one can define an implicit function by z = φ(x, y). Geometrically points satisfying f(x, y, z) = 0 are a surface and we are asking whether the surface is the graph of a function. Suppose there is an implicit function. Then f(x, y, φ(x, y)) = 0 (77) Assuming everything is differentiable we can take partial derivatives of this equation. By the chain rule we have 0 = x [f(x, y, φ(x, y))] =f x(x, y, φ(x, y)) + f z (x, y, φ(x, y))φ x (x, y) 0 = y [f(x, y, φ(x, y))] =f y(x, y, φ(x, y)) + f z (x, y, φ(x, y))φ y (x, y) (78) Solve this for φ x (x, y) and φ y (x, y) and get φ x (x, y) = f x(x, y, φ(x, y)) f z (x, y, φ(x, y)) φ y (x, y) = f y(x, y, φ(x, y)) f z (x, y, φ(x, y)) (79) 14

15 This can also be written in the form z x = f/ x f/ z z y = f/ y f/ z (80) keeping in mind that the right side is evaluated at z = φ(x, y). For this to work we need that f/ z 0. If this holds and if we restrict attention to a small region then there always is an implicit function. This is the content of the following Theorem 4 (implicit function theorem) Let f(x, y, z) have continuous partial derivatives near some point (x 0, y 0, z 0 ). If f(x 0, y 0, z 0 ) =0 f z (x 0, y 0, z 0 ) 0 (81) Then for every (x, y) near (x 0, y 0 ) there is a unique z near z 0 such that f(x, y, z) = 0. The implicit function z = φ(x, y) satisfies z 0 = φ(x 0, y 0 ) and has continuous partial derivatives near (x 0, y 0 ) which satisfy the equations (79). The theorem does not tell you what φ is or how to find it, only that it exists. However if we take the equations (79) at the special point (x 0, y 0 ) we find φ x (x 0, y 0 ) = f x(x 0, y 0, z 0 ) f z (x 0, y 0, z 0 ) φ x (x 0, y 0 ) = f x(x 0, y 0, z 0 ) f z (x 0, y 0, z 0 ) (82) and these quantities can be computed. example: Suppose the equation is f(x, y, z) = x 2 + y 2 + z 2 1 = 0 (83) which describes the surface of a sphere. Is there an implicit function z = φ(x, y) near a particular point (x 0, y 0, z 0 ) on the sphere? We have the derivatives f x (x, y, z) = 2x f y (x, y, z) = 2y f z (x, y, z) = 2z (84) 15

16 Then f z (x 0, y 0, z 0 ) = 2z 0 is not zero if z 0 0. So by the theorem there is an implicit function z = φ(x, y) near a point with z 0 0 and z x = f x = x f z z z y = f y = y f z z This example is special in that we can find the implicit function exactly and so check these results. The implicit function is (85) z = ± 1 x 2 y 2 (86) with the plus sign if z 0 > 0 and the minus sign if z 0 < 0. In either case we have the expected result z x = ± 1 2 (1 x2 y 2 ) 1/2 ( 2x) = x z z y = ± 1 2 (1 x2 y 2 ) 1/2 ( 2y) = y z If z 0 = 0 there is no implicit function near the point as figure 3 illustrates. problem: onsider the equation (87) f(x, y, z) = xe z + yz + 1 = 0 (88) Note that (x, y, z) = (0, 1, 1) is one solution. Is there an implicit function z = φ(x, y) near this point? What are the derivatives z/ x, z/ y at (x, y) = (0, 1)? solution We have the derivatives f x = e z f y = z f z = xe z + y (89) Then f z (0, 1, 1) = 1 is not zero so by the theorem there is an implicit function. The derivatives at (0,1) are z x = f x = ez f z xe z + y = e 1 z y = f y = z (90) f z xe z + y = 1 alternate solution: Once the existence of the implicit function is established we can argue as follows. Take partial derivatives of xe z + yz + 1 = 0 assuming z = φ(x, y) and obtain x : ez + xe z z x + y z x =0 y : z xez y + z + y z (91) y =0 16

17 Figure 3: There is an implicit function near points with z 0 0, but not near points with z 0 = 0. Now put in the point (x, y, z) = (0, 1, 1) and get e 1 + z x =0 Solving for the derivatives gives the same result. Some remarks: 1 + z y =0 (92) 1. Why is the implicit function theorem true? Instead of solving f(x, y, z) = 0 for z near (x 0, y 0, z 0 ) one could make a linear approximation and try to solve that. Taking into account that f(x 0, y 0, z 0 ) = 0 the linear approximation is f x (x 0, y 0, z 0 )(x x 0 ) + f y (x 0, y 0, z 0 )(y y 0 ) + f z (x 0, y 0, z 0 )(z z 0 ) = 0 (93) If f z (x 0, y 0, z 0 ) 0 this can be solved by z = z 0 f x(x 0, y 0, z 0 ) f z (x 0, y 0, z 0 ) (x x 0) f y(x 0, y 0, z 0 ) f z (x 0, y 0, z 0 ) (y y 0) (94) 17

18 and this has the expected derivatives. To prove the theorem one has to argue that since the actual function is near the linear approximation there is an implicit function in that case as well. 2. Here are some variations of the implicit function theorem (a) If f(x, y) = 0 one can solve for y = φ(x) near any point where f y 0 and dy/dx = f x /f y. (b) If f(x, y, z) = 0 one can solve for x = φ(y, z) near any point where f x 0 and x/ y = f y /f x and x/ z = f z /f x (c) If f(x, y, z, w) = 0 one can solve for w = φ(x, y, z) near any point where f w 0 and w/ x = f x /f w, etc. 3. One can also find higher derivatives of the implicit function. If f(x, y, z) = 0 defines z = φ(x, y) and φ x (x, y) = f x(x, y, φ(x, y)) f z (x, y, φ(x, y)) (95) then one can find φ xx, φ xy by further differentiation. example: f(x, y, z) = x 2 + y 2 + z 2 1 = 0 defines z = φ(x, y) which satisfies z x = x z Keeping in mind that z is a function of x, y further differentiation yields ( ) ( ) 2 z z x z/ x z x( x/z) x = = = z2 x 2 (97) 2 z 2 z 2 z 3 Since x 2 + y 2 + z 2 = 1 this can also be written as (y 2 1)/z 3. Similarly 1.8 implicit function theorem -II (96) 2 z x y = xy (98) z 3 We give another version of the implicit function theorem. Suppose we have a pair of equations of the form F (x, y, u, v) =0 G(x, y, u, v) =0 (99) 18

19 an we solve them for u, v as functions of x, y? More precisely is it the case that for each x, y in some domain there is a unique u, v so that the equations are satisfied. If it is so we can define implicit functions u =f(x, y) v =g(x, y) (100) If the implicit functions exist we have F (x, y, f(x, y), g(x, y)) =0 G(x, y, f(x, y), g(x, y)) =0 (101) Take partial derivatives with respect to x and y and get F x + F u f x + F v g x =0 G x + G u f x + G v g x =0 F y + F u f y + F v g y =0 G y + G u f y + G v g y =0 These equations can be written as the matrix equations ( ) ( ) ( Fu F v fx Fx = G u G v g x G x ( ) ( ) ( Fu F v fy Fy = G u G v g y G y ) (102) ) (103) Note that the matrix on the left is the derivative of the function (u, v) (F (x, y, u, v), G(x, y, u, v)) (104) for fixed (x, y). If the determinant of this matrix is not zero we can solve for the partial derivatives f x, g x, f y, g y. One finds for example ( ) Fx F det v G x G v f x = ( ) (105) Fu F det v The determinant of the matrix is called the Jacobian determinant and is given a special symbol ( ) (F, G) (u, v) det Fu F v F G u G u G v F v G u (106) v G u G v 19

20 With this notation the equations for the four partial derivatives can be written u x v x u y v y (F, G) G) = / (F, (x, v) (u, v) (F, G) G) = / (F, (u, x) (u, v) (F, G) G) = / (F, (y, v) (u, v) (F, G) G) = / (F, (u, y) (u, v) (107) where u = f(x, y), v = g(x, y) on the right. This holds provided (F, G)/ (u, v) 0 and this is the key condition in the following theorem which guarantees the existence of the implicit functions. Theorem 5 (implicit function theorem) Let F, G have continuous partial derivatives near some point (x 0, y 0, u 0, v 0 ). If F (x 0, y 0, u 0, v 0 ) =0 G(x 0, y 0, u 0, v 0 ) =0 (108) and [ ] (F, G) (x 0, y 0, u 0, v 0 ) 0 (109) (u, v) Then for every (x, y) near (x 0, y 0 ) there is a unique (u, v) near (u 0, v 0 ) such that F (x, y, u, v) = 0 and G(x, y, u, v) = 0. The implicit functions u = f(x, y), v = g(x, y) satisfy u 0 = f(x 0, y 0 ), v 0 = g(x 0, y 0 ) and have continuous partial derivative which satisfy the equations (107). problem: onsider the equations F (x, y, u, v) = x 2 y 2 + 2uv 2 =0 G(x, y, u, v) = 3x + 2xy + u 2 v 2 =0 (110) Note that (x, y, u, v) = (0, 0, 1, 1) is a solution. Are there implicit functions u = f(x, y), v = g(x, y) near (0, 0)? What are the derivatives u/ x, v/ x at (x, y) = (0, 0)? solution: First compute ( (F, G) (u, v) = det Fu F v G u G v ) ( 2v 2u = det 2u 2v ) = 4(u 2 + v 2 ) = 8 (111) 20

21 Since this is not zero the implicit functions exist by the theorem. We also compute ( ) ( ) (F, G) (x, v) = det Fx F v 2x 2u = det = 4xv 2u(3 + 2y) = 6 G x G v 3 + 2y 2v (112) and ( (F, G) (u, x) = det Fu F x G u G x ) ( 2v 2x = det 2u 3 + 2y ) = 2v(3 + 2y) 4ux = 6 (113) Then u x = 6 8 = 3 4 v x = 6 8 = 3 4 (114) alternate solution: Assuming the implicit functions exist differentiate the equations F = 0, G = 0 with respect to x assuming u = f(x, y), v = g(x, y). This gives Now put in the point (0, 0, 1, 1) and get 2x + 2v u x + 2u v x = y + 2u u (115) v 2v x x =0 2 u x + 2 v x =0 This again has the solution u/ x = 3/4, v/ x = 3/ inverse functions u x 2 v x =0 (116) Suppose we have a function f from U R 2 to V R 2 which we write in the form x =f 1 (u, v) y =f 2 (u, v) (117) Suppose further for every (x, y) in V there is a unique (u, v) in U such that x = f 1 (u, v), y = f 2 (u, v). (0ne says the function is one-to-one and onto.) Then there is a an inverse function g from V to U defined by u =g 1 (x, y) v =g 2 (x.y) (118) 21

22 Figure 4: inverse function So g sends every point back where it came from. See figure 4. We have (g f)(u, v) =(u, v) (f g)(x, y) =(x, y) (119) We write g = f 1. example: Suppose the function is x =au + bv y =cu + dv defined on all of R 2. This can also be written in matrix form ( ) ( ) ( ) x a b u = y c d v (120) (121) This is invertible if we can solve the equation for (u, v) which is possible if and only if ( ) a b det = ad bc 0 (122) c d 22

23 The inverse function is given by the inverse matrix ( u v where ( a b c d ) ( a b = c d ) 1 = 1 ad bc ) 1 ( x y ) ( d b c a ) (123) (124) example: onsider the function x =r cos θ y =r sin θ (125) from to U = {(r, θ) : r > 0, 0 θ < 2π} (126) V = {(x, y) : (x, y) 0} (127) This function sends (r, θ) to the point with polar coordinates (r, θ). See figure 5. The function is invertible since every point (x, y) in V has unique polar coordinates (r, θ) in U. (It would not be invertible if we took U = R 2 since (r, θ) and (r, θ + 2π) are sent to the same point ). For (x, y) in the first quadrant the inverse is r = x 2 + y 2 ( y ) (128) θ = tan 1 x 1.10 inverse function theorem ontinuing the discussion of the previous section suppose that f has an inverse function g and that both are differentiable. Then differentiating (f g)(x, y) = (x, y) we find by the chain rule ( ) 1 0 (Df)(g(x, y)) (Dg)(x, y) = I I = (129) 0 1 and so the derivative of the inverse function is the matrix inverse (Dg)(x, y) = [(Df)(g(x, y))] 1 (130) It is not always easy to tell whether an inverse function exists. theorem can be helpful. The following 23

24 Figure 5: inverse function for polar coordinates Theorem 6 (inverse function theorem) Let x = f 1 (u, v), y = f 2 (u, v) have continuous partial derivatives and suppose x 0 =f 1 (u 0, v 0 ) y 0 =f 2 (u 0, v 0 ) (131) and ( ) (x, y) (u 0, v 0 ) 0 (132) (u, v) Then there is an inverse function u = g 1 (x, y), v = g 2 (x, y) defined near (x 0, y 0 ) which satisfies u 0 =g 1 (x 0, y 0 ) v 0 =g 2 (x 0, y 0 ) (133) and has a continuous derivative which satisfies (130). In particular Dg(x 0, y 0 ) = [Df(u 0, v 0 )] 1 or ( u/ x ) u/ y v/ x v/ y (x 0,y 0 ) ( x/ u x/ v = y/ u y/ v 24 ) 1 (u 0,v 0 ) (134)

25 Proof. The inverse exists if for each (x, y) near (x 0, y 0 ) there is a unique (u, v) near (u 0, v 0 ) such that F (x, y, u, v) f 1 (u, v) x =0 G(x, y, u, v) f 2 (u, v) y =0 (135) This follows by the implicit functions theorem since (x 0, y 0, u 0, v 0 ) is one solution and at this point ( ) ( ) (F, G) (u, v) = det Fu F v f1,u f = det 1,v (x, y) = G u G v f 2,u f 2,v (u, v) 0 The differentiability of the inverse also follows from the implicit function theorem. problem: onsider the function x =u + v 2 y =u 2 + v (137) which sends (u, v) = (1, 2) to (x, y) = (5, 3). Show that the function is invertible near (1, 2) and find the partial derivatives of the inverse function at (5, 3). solution: We have ( x/ u x/ v y/ u y/ v ) = ( 1 2v 2u 1 ) = ( ) at (1, 2) (138) Therefore ( (x, y) 1 4 (u, v) = det 2 1 ) = 7 at (1, 2) (139) This is not zero so the inverse exists by the theorem and sends (5, 3) to (1, 2). We have for the derivatives ( ) ( ) 1 u/ x u/ y x/ u x/ v = v/ x v/ y y/ u y/ v (5,3) (1,2) ( ) 1 ( ) (140) 1 4 1/7 4/7 = = 2 1 2/7 1/7 alternate solution: Differentiate x = u + v 2, y = u 2 + v assuming u, v are functions of x, y, then put in the point. 25

26 1.11 maxima and minima Let f(x) = f(x 1,..., x n ) be a function from R n to R and let x 0 = (x 0 1,... x 0 n) be a point in R n. We say that f has a local maximum at x 0 if f(x) f(x 0 ) for all x near x 0. We say that f has a local minimum at x 0 if f(x) f(x 0 ) for all x near x 0. Theorem 7 If f is differentiable at x 0 and f has a local maximum or minimum at x 0 then all partial derivatives vanish at the point, i.e. f x1 (x 0 ) = = f xn (x 0 ) = 0 (141) Proof. For any h = (h 1,..., h n ) R n consider the function F (t) = f(x 0 + th) of the single variable t. If f has a local maximum or minimum at x 0 then F has a local maximum or minimum at t = 0. By the chain rule F (t) is differentiable and and it is a result of elementary calculus that F (0) = 0. But the chain rule says F (t) = n i=1 f xi (x 0 + th) d(x0 i + th i ) dt = n f xi (x 0 + th)h i (142) i=1 Thus 0 = F (0) = Since this is true for any h it must be that f xi (x 0 ) = 0. n f xi (x 0 )h i (143) A point x 0 with f xi (x 0 ) = 0 is called a critical point for f. We have seen that if f is has a local maximum or minimum at x 0 then it is a critical point. We are interested whether the converse is true. If x 0 is a critical point is it a local maximum or minimum for f? Which is it? To answer this question consider again F (t) = f(x 0 + th) and suppose f is many times differentiable. By Taylor s theorem for one variable we have i=1 F (1) = F (0) + F (0) F (0) F (s) (144) for some s between 0 and 1. But F (t) is computed above, and similarly we have F (t) = F (t) = n i=1 n i=1 n f xi x j (x 0 + th)h i h j j=1 n j=1 k=1 (145) n f xi x j x k (x 0 + th)h i h j h k 26

27 Then the Taylor s expansion becomes f(x 0 + h) = f(x 0 ) + n f xi (x 0 )h i i=1 n n f xi x j (x 0 )h i h j + R(h) (146) i=1 j=1 This is an example of a multivariable Taylor s theorem with remainder. The remainder R(h) = F (s)/6 is small if h is small and one can show that there is a constant such that for h small R(h) h 3. Now suppose x 0 is a critical point so the first derivatives vanish. Also define a matrix of second derivatives A = f x1,x 1 (x 0 ) f x1,x n (x 0 ).. f xn,x1 (x 0 ) f xn,xn (x 0 ) called the Hessian of f at x 0. Then we can write our expansion as (147) f(x 0 + h) = f(x 0 ) + 1 h Ah + R(h) (148) 2 We are interested in whether f(x 0 + h) is greater than or less than f(x 0 ) for h small. Then idea is that since R(h) is much smaller than 1 h Ah it is the latter term which 2 determines the answer. A n n matrix A is called positive definite if there is a constant M so h Ah > M h 2 for all h 0. It is called negative definite if h Ah < M h 2 for all h 0. Theorem 8 Let x 0 be a critical point for f and suppose the Hessian A has det A If A is positive definite then f has a local minimum at x If A is negative definite then f has a local maximum at x Otherwise f has a saddle point at x 0, i.e f increases in some directions and decreases in other directions as you move away from x 0. Proof. We prove the first statement. We have h Ah > M h 2. If also h M/4. then R(h) h M h 2 (149) Therefore or f(x 0 + h) f(x 0 ) M h M h 2 (150) f(x 0 + h) f(x 0 ) M h 2 (151) 27

28 Thus f(x 0 + h) > f(x 0 ) for 0 < h M/4 which means we have a strict local maximum at x 0. Thus our problem is to decide whether or not A is positive or negative definite. For any symmetric matrix A one can show that A is positive definite if and only if all eigenvalues are postive and A is negative definite if and only if all eigenvalues are negative. Recall that λ is an eigenvalue if there is a vector v 0 such that Av = λv. One can find the eigenvalues by solving the equation where I is the identity matrix. det(a λi) = 0 (152) example: onsider the function f(x, y) = exp ) ( x2 2 y2 2 + x + y The critical points are solutions of ) f x =( x + 1) exp ( x2 2 y2 2 + x + y = 0 ) f y =( y + 1) exp ( x2 2 y2 2 + x + y = 0 (153) (154) Thus the only critical point is (x, y) = (1, 1). The second derivatives at this point are ) f xx =(x 2 2x) exp ( x2 2 y2 2 + x + y = e ) f xy =( x + 1)( y + 1) exp ( x2 2 y2 2 + x + y = 0 (155) ) f yy =(y 2 2y) exp ( x2 2 y2 2 + x + y = e Thus the Hessian is ( ) fxx f A = xy = f yx f yy ( e 0 0 e ) (156) It has eigenvalues e, e which are negative. Hence A is negative definite and the function has a local maximum at (1, 1). example: onsider the function f(x, y) = x sin y (157) 28

29 The critical points are solutions of f x = sin y = 0 f y =x cos y = 0 These are points with x = 0 and y = 0, ±π, ±2π,. The Hessian is ( ) ( ) fxx f A = xy 0 cos y = cos y x sin y f yx f yy (158) (159) At points (0, ±π), (0, ±3π),... this is At points (0, 0), (0, ±2π),... this is A = A = ( ( ) ) (160) (161) In either case the eigenvalues are solutions of ( ) λ ±1 det(a λi) = det = λ 2 1 = 0 (162) ±1 λ Thus the eigenvalues are λ = ±1. Since they have both signs every critical point is a saddle point differentiation under the integral sign Theorem 9 If f(t, x), ( f/ t)(t, x) exist and are continuous [ d b ] b f(t, x)dx = dt a a f (t, x) dx (163) t Proof. Form the difference quotient b f(t, x + h) b f(t, x) a a = h b a f(t, x + h) f(t, x) h (164) and take the limit h 0. The only issue is whether we can take the limit under the integral sign on the right. This can be justified under the hypotheses of the theorem. 29

30 example: [ d 1 ] 1 log(x 2 + t 2 2t )dx = dt 0 0 x 2 + t dx 2 [ ( x = 2 tan 1 ( ) t 1 =2 tan 1 t )] x=1 x=0 (165) problem: Find 1 0 x 1 dx (166) log x solution: We solve a more general problem which is to evaluate φ(t) = 1 0 x t 1 dx (167) log x Then φ(1/2) is the answer to the original problem. Differentiating under the integral sign and taking account that d(x t )/dt = x t log x we have Hence φ (t) = 1 0 [ x x t t+1 dx = t + 1 ] x=1 x=0 = 1 t + 1 (168) φ(t) = log(t + 1) + (169) for some constant. But we know φ(0) = 0 so we must have = 0. Thus φ(t) = log(t + 1) and the answer is φ(1/2) = log(3/2) Leibniz rule The following is a generalization of the previous result where we allow the endpoints to be functions of t. Theorem 10 [ ] d b(t) f(t, x)dx dt a(t) ( ) ( ) b(t) = f t, b(t) b (t) f t, a(t) a (t) + a(t) f (t, x) dx (170) t 30

31 Proof. Let F (b, a, t) = b a f(t, x) dx (171) What we want is the derivative of F (b(t), a(t), t) and by the chain rule this is d [F (b(t), a(t), t)] dt = F b (b(t), a(t), t)b (t) + F a (b(t), a(t), t)a (t) + F t (b(t), a(t), t) (172) But F b (b, a, t) = f(t, b) F a (b, a, t) = f(t, a) F t (b, a, t) = which gives the result. b a f (t, x) dx (173) t example: [ ] d t 2 sin(t 2 + x 2 )dx dt t = sin(t 2 + x 2 d(t 2 ) ) x=t 2 sin(t 2 + x 2 d(t) ) x=t dt dt + = sin(t 2 + t 4 )2t sin(2t 2 ) + 2t t 2 t cos(t 2 + x 2 )dx t 2 t t sin(t2 + x 2 )dx (174) example: (forced harmonic oscillator). Suppose we want to find a function x(t) whose derivatives x (t), x (t) solve the ordinary differential equation with the inital conditions mx + kx = f(t) (175) x(0) = 0 x (0) = 0 (176) Here k, m are positive constants and f(t) is an arbitrary function. We claim that a solution is x(t) = 1 mω t To check this note first that x(0) = 0. Leibniz rule and find 0 sin(ω(t τ))f(τ)dτ ω = x (t) = 1 mω [sin(ω(t τ))f(τ)] τ=t + 1 mω 31 k m (177) Then take the first derivative using the t 0 ω cos(ω(t τ))f(τ)dτ (178)

32 The first term is zero and we also note that x (0) = 0. Taking one more derivative yields x (t) = 1 m [cos(ω(t τ))f(τ)] τ=t + 1 mω which is the same as t 0 ( ω 2 ) sin(ω(t τ))f(τ)dτ (179) x (t) = 1 m f(t) ω2 x(t) (180) Now multiply by m, use mω 2 = k and we recover the differential equation calculus of variations We consider the problem of finding maximima and minima for a functional - i.e. for a function of functions. As an example consider the problem of finding the curve between two points (x 0, y 0 ) and (x 1, y 1 ) which has the shortest length. We assume that there is such a curve and that it it is the graph of a function y = y(x) with y(x 0 ) = y 0 and y(x 1 ) = y 1. The length of such a curve is x1 I(y) = 1 + y (x) 2 dx (181) x 0 and we seek to find the function y = y(x) which gives the minimum value. More generally suppose we have a function F (x, y, y ) of three real variables (x, y, y ). For any differentiable function y = y(x) satisfying y(x 0 ) = y 0 and y(x 1 ) = y 1 form the integral x1 I(y) = F (x, y(x), y (x))dx (182) x 0 The question is which function y = y(x) minimizes (or maximizes) the functional I(y). We are looking for a local minimum (or maximum), that is we want to find a function y such that I(ỹ) I(y) for all functions ỹ near to y in the sense that ỹ(x) is close to y(x) for all x 0 x x 1. Theorem 11 If y = y(x) is a local maximum or minimum for I(y) with fixed endpoints then it satisfies F y (x, y(x), y (x)) d dx (F y (x, y(x), y (x)) = 0 (183) Remarks. 1. The equation is called Euler s equation and is abreviated as F y d dx F y = 0 (184) 32

33 2. Note that y, y mean two different things in Euler s equation. First one evaluates the partial derivatives F y, F y treating y, y as independent variables. Then in computing d/dx one treats y, y as a function and its derivative. 3. The converse may not be true, i.e solutions of Euler s equation are not necessarily maxima or minima. They are just candidates and one should decide by other criteria. proof: Suppose y = y(x) is a local minimum. Pick any differentiable function η(x) defined for x 0 x x 1 and satisfying η(x 0 ) = η(x 1 ) = 0. Then for any real number t the function y t (x) = y(x) + tη(x) (185) is differentiable with respect to x and satisfies y t (x 0 ) = y t (x 1 ) = 0. function J(t) = I(y t ) = x1 onsider the x 0 F (x, y t (x), y t(x))dx (186) Since y t is near y 0 = y for t small, and since y 0 is a local minimum for I, we have J(t) = I(y t ) I(y 0 ) = J(0) (187) Thus t = 0 is a local minimum for J(t) and it follows that J (0) = 0. To see what this says we differentiate under the integral sign and compute J (t) = = = x1 x 0 x1 x 0 x1 x 0 t F (x, y t(x), y t(x))dx ( F y (x, y t (x), y t(x)) t (y t(x)) + F y (x, y t (x), y t(x)) t (y t(x)) (F y (x, y t (x), y t(x))η(x) + F y (x, y t (x), y t(x))η (x)) dx ) dx (188) Here in the second step we have used the chain rule. Now in the second term integrate by parts taking the derivative off η (x) = (d/dx)η and putting it on F y (x, y t (x), y t(x)) The term involving the endpoints vanishes because η(x 0 ) = η(x 1 ) = 0. Then we have x1 ( J (t) = F y (x, y t (x), y t(x)) d ) dx F y (x, y t(x), y t(x)) η(x)dx (189) x 0 Now put t = 0 and get 0 = J (0) = x1 x 0 ( F y (x, y(x), y (x)) d ) dx F y (x, y(x), y (x)) η(x)dx (190) 33

34 Since this is true for an arbitrary function η it follows that the expression in parentheses must be zero which is our result. 1 example: We return to the problem of finding the curve between two points with the shortest length. That is we seek to minimize This has the form (182) with I(y) = x1 x y (x) 2 dx (191) F (x, y, y ) = 1 + (y ) 2 (192) The minimizer must satisfy Euler s equation. Since F y = 0 and F y = y / 1 + (y ) 2 this says [ ] F y d dx F y = d y = 0 (193) dx 1 + (y ) 2 Evaluating the derivatives yields 1 + (y ) 2 y (y ) 2 y / 1 + (y ) (y ) 2 = 0 (194) Now multiple by (1 + (y ) 2 ) 3/2 and get (1 + (y ) 2 )y (y ) 2 y = 0 (195) which is the same as y = 0. Thus the minimizer must have the form y = ax + b for some constants a, b. So the shortest distance between two points is along a straight line as expected. example: The problem is to find the function y = y(x) with y(0) = 0 and y(1) = 1 which minimizes the integral I(y) = (y(x) 2 + (y (x)) 2 )dx (196) and again we assume there is such a minimizer. The integral has the form (182) with F (x, y, y ) = 1 2 (y2 + (y ) 2 ) (197) 1 In general if f is a continuous function b f(x)dx = 0 does not imply that f 0. However it is a true if f(x) 0. If b f(x)η(x)dx = 0 for any continuous function η then we can take η(x) = f(x) a and get b a f(x)2 dx = 0, hence f(x) 2 = 0, hence f(x) = 0. This is not quite the situation above since we also restriced η to vanish at the endpoints. But the conclusion is stil valid. 34

35 Then Euler s equation says F y d dx (F y ) = y d dx y = y y = 0 (198) Thus we must solve the second order equation y y = 0. Since the equation has constant coefficients one can find solutions by trying y = e rx. One finds that r 2 = 1 and so y = e ±x are solutions The general solution is y(x) = c 1 e x + c 2 e x (199) The constants c 1, c 2 are fixed by the condition y(0) = 0 and y(1) = 1 and one finds y(x) = e e 2 1 (ex e x ) = 2e sinh x (200) e 2 1 example : Suppose that an object moves on a line and its position at time t is given by a function x(t). Suppose also we know that x(t 0 ) = x 0 and x(t 1 ) = x 1 and that it is moving in a force field F (x) = dv/dx determined by some potential function V (x). What is the trajectory x(t)? One way to proceed is to form a function called the Lagrangian by taking the difference of the kinetic and potential energy: For any trajectory x = x(t) one forms the action L(x, x ) = 1 2 m(x ) 2 V (x) (201) A(x) = t1 t 0 L(x(t), x (t))dt (202) According to D Alembert s principle the actual trajectory is the one that minimizes the action. This is also called the principle of least action. To see what it says we observe that this problem has the form (182) with new names for the variables. Euler s equation says But L x = dv/dx = F and L x = mx so this is L x d dt L x = 0 (203) F mx = 0 (204) which is Newton s second law. Thus the principle of least action is an alternative to Newton s second law. This turns out to be true for many dynamical problems. 35

36 2 vector calculus 2.1 vectors We continue our survey of multivariable caculus but now put special emphasis on R 3 which is a model for physical space. Vectors in R 3 will now be indicated by arrows or bold face type as in u = (u 1, u 2, u 3 ). Any such vector can be written u =(u 1, u 2, u 3 ) =u 1 (1, 0, 0) + u 2 (0, 1, 0) + u 3 (0, 0, 1) =u 1 i + u 2 j + u 3 k (205) where we have defined i = (1, 0, 0) j = (0, 1, 0) k = (0, 0, 1) (206) Any vector can be written as a linear combination of the independent vectors i, j, k so these form a basis for R 3 called the standard basis. We consider several products of vectors: dot product: The dot product is defined either by u v = u 1 v 1 + u 2 v 2 + u 3 v 3 (207) or by u v = u v cos θ (208) where θ is the angle between u and v. Note that u u = u 2. Also note that u is orthogonal (perpendicular) to v if and only if u v = 0. The dot product has the properties u v =v u (αu) v =α(u v) = u (αv) (u 1 + u 2 ) v =u 1 v + u 2 v (209) Examples are i i = 1 j j = 1 j j = 1 i j = 0 j k = 0 k i = 0 (210) This says that i, j, k are orthogonal unit vectors. They are an example of an orthonormal basis for R 3. 36

37 Figure 6: cross product cross product: (only in R 3 ) The cross product of u and v is a vector u v which has length u v = u v sin θ (211) Here θ is the positive angle between the vectors. The direction of u v is specified by requiring that it be perpendicular to u and v in such a way that u, v, u v form a right-handed system. (See figure 6) The length u v is interpreted as the area of the parallelogram spanned by u, v. This follows since the parallelogram has base u and height v sin θ (See figure 6) and so area = base height = u v sin θ = u v An alternate definition of the cross-product uses determinants. Recall that a 1 a 2 a 3 ( ) ( ) ( det b 1 b 2 b 3 b2 b =a 1 det 3 b1 b a c c 1 c 2 c 2 c 2 det 3 b1 b + a 3 c 1 c 3 det 2 3 c 1 c 2 3 =a 1 (b 2 c 3 b 3 c 2 ) + a 2 (b 3 c 1 b 1 c 3 ) + a 3 (b 1 c 2 b 2 c 1 ) 37 (212) ) (213)

38 The other definition is i j k u v = det u 1 u 2 u 3 = i(u 2 v 3 u 3 v 2 ) + j(u 3 v 1 u 1 v 3 ) + k(u 1 v 2 u 2 v 1 ) (214) v 1 v 2 v 3 The cross product has the following properties u u =0 u v = v u (αu) v =α(u v) = u (αv) (u 1 + u 2 ) v =u 1 v + u 2 v (215) Examples are i j = k j k = i k i = j (216) triple product: The triple product of vectors w, u, v is defined by w (u v) =w 1 (u 2 v 3 u 3 v 2 ) + w 2 (u 3 v 1 u 1 v 3 ) + w 3 (u 1 v 2 u 2 v 1 ) w 1 w 2 w 3 = det u 1 u 2 u 3 v 1 v 2 v 3 (217) The absolute value w (u v) is the volume of the parallelopiped spanned by u, v, w (see figure 16). This is so because if φ is the angle between w and u v then volume = (area of base) height ( )( ) = u v w cos φ = w (u v) (218) problem: Find the volume of the parallelopiped spanned by i + j, j, i + j + k. solution: det = 1 (219) 38

39 Figure 7: triple product Planes: A plane is determined by a particular point R 0 = x 0 i + y 0 j + z 0 k in the plane and a vector N = N 1 i + N 2 j + N 3 k perpendicular to the plane, called a normal vector. If R = xi + yj + zk is any other point in the plane, then R R 0 lies in the plane, hence it is orthogonal to N and hence (see figure 8) N (R R 0 ) = 0 (220) In fact this is the equation of the plane. That is a point R lies on the plane if and only if it satisfies the equation. Written out it says N 1 (x x 0 ) + N 2 (y y 0 ) + N 3 (z z0 ) = 0 (221) problem: Find the plane determined by the three points a = i, b = 2j, c = 3k. solution: b a = i + 2j and c a = i + 3k both lie in the the plane. Their cross product is orthogonal to both, hence to the plane, and can be take as a normal vector: i j k N = (b a) (c a) = det = 6i + 3j + 2k (222) For the particular point take R 0 = a = i. Then the equation of the plane is N (R R 0 ) = 6(x 1) + 3y + 2z = 0 (223) 39

40 2.2 vector-valued functions Figure 8: A vector-valued function is a function from R to R 3 (or more generally R n ). It is written R(t) = (x(t), y(t), z(t)) = x(t)i + y(t)j + z(t)k (224) To say R(t) has limit means that If R 0 = x 0 i + y 0 j + z 0 j then lim t t 0 R(t) = R 0 (225) lim R(t) R 0 = 0 (226) t t 0 R(t) R 0 = (x(t) x 0 ) 2 + (y(t) y 0 ) 2 + (z(t) z 0 ) 2 (227) Hence lim t t0 R(t) = R 0 is the same as the three limits lim t t 0 x(t) = x 0 The function R(t) is continuous if lim t t0 y(t) = y 0 lim z(t) = z 0 (228) t t0 lim R(t) = R 0 (229) t t 0 40

41 Figure 9: tangent vector to a curve This is the same as saying that the components x(t), y(t), z(t) are all continuous. The function R(t) is differentiable if exists. Since R(t + h) R(t) h = R (t) = dr dt = lim R(t + h) R(t) h 0 h x(t + h) x(t) i + h y(t + h) y(t) j + h (230) z(t + h) z(t) k (231) h this is the same as saying that the components x(t), y(t), z(t) are all differentiable, in which case the derivative is R (t) = x (t)i + y (t)j + z (t)k (232) 41

42 Figure 10: some examples In other words you find the derivative by differentiating the components The range of a continuous function R(t) is a curve. The derivative R (t) has the interpretation of being a tangent to the curve as figure 9 shows. A common application is that R(t) is the location of some object at time t. Then R (t) is the velocity and the magnitude of the velocity R (t) is the speed. The second derivative R (t) is the acceleration. Here are some examples illustrated in figure 10 example: straight line. onsider R(t) = at + b (233) 42

43 Then R(0) = b and R (t) = a so it is a straight line through b in the direction a. example: circle. Let R(t) be the point on a circle of radius a with polar angle t. As t increases it travels around the circle at uniform speed. The point has artesian coordinates x = a cos t, y = a sin t so R(t) = a cos t i + a sin t j (234) The velocity is and the speed is R = a. R (t) = a sin t i + a cos t j (235) example: helix. To the previous example we add a constant velocity in the z-direction This describes a helix and we have 2.3 other coordinate systems R(t) = a cos t i + a sin t j + btk (236) R (t) = a sin t i + a cos t j + bk (237) We next describe vector valued functions using other coordinate systems. A. Polar: First some general remarks about vectors and polar coordinates in R 2. Let R(r, θ) be the point with polar coordinates r, θ. This has artesian coordinates x = r cos θ, y = r sin θ so R(r, θ) = r cos θi + r sin θj (238) If we vary r with θ fixed we get an r-line. The tangent vector to this line is R (r, θ) = cos θi + sin θj (239) r If we vary θ with r fixed we get an θ-line. The tangent vector to this line is R (r, θ) = r sin θi + r cos θj (240) θ We also consider unit tangent vectors to these coordinate lines: e r (θ) = R/ r R/ r e θ (θ) = R/ θ R/ θ = cos θi + sin θj = sin θi + cos θj (241) See figure

44 Figure 11: polar and cylindrical basis vectors Note that e r (θ) e θ (θ) = cos θ( sin θ) + sin θ cos θ = 0 (242) Thus for any θ the vectors e r (θ), e θ (θ) form an orthonormal set of vectors in R 2 and hence an orthonormal basis. Also note for future reference that de r dθ =e θ de θ dθ = e r (243) Now a curve is specified in polar coordinates by a pair of functions r(t), θ(t). The artesian coordinates are x(t) = r(t) cos θ(t) and y(t) = r(t) sin θ(t). So the curve is 44

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