CURVATURE. 1. Problems (1) Let S be a surface with a chart (φ, U) so that. + g11 g 22 x 1 and g 22 = φ
|
|
- Dinah Harris
- 7 years ago
- Views:
Transcription
1 CURVTURE NDRÉ NEVES 1. Problems (1) Let S be a surface with a chart (φ, U) so that. = 0 x 1 x 2 for all (x 1, x 2 ) U. Show that [ 1 K φ = 2 g 11 g 22 x 2 where g 11 = x 1. x 1 ( ) x2 g 11 + g11 g 22 x 1 and g 22 = x 2. x 2 ( )] x1 g 22, g11 g 22 (2) Let S be a compact surface with no boundary and let P = {x S : K(x) 0}, where K is the gaussian curvature. a) Show that the the Gauss map of S restricted to P is surjective. In other words, for every p S 2 = {x 2 + y 2 + z 2 = 1}, there is x P so that N(x) = p. b) Show that S K + d 4π, where K + (x) = max{k(x), 0} for all x S. HINT: If N, M are two surfaces and F is a surjective smooth map F : N M, then area(m) detdf d. (3) (Do Carmo, 4.2, Ex 8) Let G : R 3 R 3 be a map such that N G(p) G(q) = p q for all p, q R 3. Show the existence of p 0 R 3 and B a matrix in O(3) such that (4) (Do Carmo, 4.2, Ex 11) G(p) = B(p) + p 0, for all p R 3. 1
2 2 NDRÉ NEVES (a) Let S R 3 be a regular surface and let F : R 3 R 3 be a distance preserving map (like the one in the previous exercise) such that F (S) S. Prove that the restriction of F to S is an isometry of S. (c) Use (a) to show that the group of isometries of {x 2 +y 2 +z 2 = 1} is contained in the group of orthogonal linear maps of R 3, which is O(3). (c) Give an example to show that there are isometries φ : S 1 S 2 which cannot be extended into distance preserving maps F : R 3 R 3. (5) (Do Carmo, 4.2, Ex 12) Let C = {x 2 + y 2 = 1}. Construct an isometry φ : C C such that the set of fixed points {p C : φ(p) = p} contains exactly two points. (6) (Do Carmo, 4.3, Ex 2) Show that if we have a parametrization of a surface such that g 11 = g 22 = λ(x 1, x 2 ) and g 12=0 then K = 1 (log λ). 2λ Conclude that if g 11 = g 22 = (x x2 2 + c) 2, then K = 4c, where c is a constant. (7) (Do Carmo, 4.3, Ex 3) Verify that the surfaces parametrized by x(u, v) = (u cos v, u sin v, log u) x(u, v) = (u cos v, u sin v, volume) have equal Gaussian curvature at points x(u, v) and x(u, v) but x x is not an isometry. This shows that the converse to Gauss Egregium s Theorem is not true. (8) Show that there exist no surface with g 11 = g 22 = 1 and 11 = 1, 22 = 1, and 12 = 0. (9) Let S be a compact surface in R 3 with no boundary which has positive Gaussian curvature and denote the Gauss map by N : S {x 2 + y 2 + z 2 = 1}. Show that if γ is a geodesic in S which divides S into a set and another set B (i.e., S = B and = B = γ), then area(n()) = area(n(b)). Hint: If you have a diffeomorphism F : S {x 2 + y 2 + z 2 = 1} R 3, what is the formula for area(f ()) in terms of DF and the set?
3 CURVTURE 3 (10) Let S be the cylinder {x 2 + y 2 = 1} in R 3, with the chart φ : [0, 2π] R S, φ(θ, z) = (cos θ, sin θ, z). and let γ be a simple closed curve in S. (a) Let γ be a simple closed curve in S for which there is a path α : [0, 1] [0, 2π] R so that α(0) = (0, z 0 ), α(1) = (2π, z 0 ), and γ = φ α. In other words, γ loops once around the z-axis. If ν denotes a unit normal vector to γ show that k. νdσ = 0. γ (b) Let γ be a simple closed curve in S for which there is a path α : [0, 1] [0, 2π] R so that α(0) = (θ 0, z 0 ) = α(1). In other words, γ is the boundary of a disc D in S. Show that γ is not a geodesic. (11) (Do Carmo, 4.4, Ex 5 a) and 6) Consider the torus of revolution generated by rotating the circle (x a) 2 + z 2 = r 2 0, y = 0 about the z-axis (a > r 0 > 0). The parallels generated by the points (a + r 0, 0), (a r 0, 0), (a, r 0 ) are called the maximum parallel, the minimum parallel, and the upper parallel, respectively. (a) Check which of these parallels is a geodesic (i.e., k g = 0). (b) Compute the geodesic curvature of the upper parallels. (12) (Do Carmo, 4.4, Ex 7) Intersect the cylinder {x 2 + y 2 = 1} with a plane passing through the x-axis and making an angle θ with the xy-plane. (a) Show that the intersecting curve is an ellipse C. (b) Compute the absolute value of the geodesic curvature of C in the cylinder at the point where C meets the axes. (13) Let S be a surface and suppose there is F : U R 2 S so that for every (t 0, s 0 ) U i) the curves α(t) = F (t, s 0 ) and α(s) = F (t 0, s) are geodesics and ii) t F (t 0, s 0 ). s F (t 0, s 0 ) = 0 for every s 0, t 0 fixed. Show that the Gaussian curvature is zero. (14) Consider the surface S parametrized by φ(u, v) = (u sin(v), u cos(v), v), (u, v) R 2.
4 4 NDRÉ NEVES (a) Show that φ is a chart, i.e., injective and u φ, v φ are linearly independent. (b) Compute the Gaussian curvature and mean curvature of S. (15) Show that if S is a connected surface with nonnegative Gaussian curvature and zero mean curvature, then it must be a plane. (16) Given a positive function r : [a, b] (0, + ) consider the surface of revolution S = {(r(s) cos θ, r(s) sin θ, s) : s [a, b], 0 < θ < 2π}. (a) Make a picture of S to make sure you understand what it is. (b) If the function r has a local minimum at s 0 with r (s 0 ) > 0, show that the Gaussian curvature of S at p = (r(s 0 ) cos θ, r(s 0 ) sin θ, θ) is negative. (c) Suppose the Gaussian curvature of S is nonnegative and r(s) r(a) = r(b) for all s. Show that S must be a cylinder. 2. Solutions (1) During the proof of Gauss s Egregium Theorem we saw that K = x 1 (φ T x 2 x 2 ).φ x1 x2 (φ T x 1 x 2 ).φ x1 g 11 g 22, where φ x1 = x1 φ, φ x1 x 2 = 2 x 1 x 2 φ and so on. The thing we want to show is [ 1 K = 2 g 11 g 22 x 2 ( ) x2 g 11 + ( )] x1 g 22. g11 g 22 x 1 g11 g 22 Let N denote a unit normal to the surface. We have and so φ T x 1 x 2 = φ x1 x 2 (φ x1 x 2.N)N x2 (φ T x 1 x 2 ) = φ x1 x 2 x 1 x2 (φ x1 x 2.N)N (φ x1 x 2.N) x2 N. Thus, using the fact that x1 φ, x2 φ are orthogonal x2 (φ T x 1 x 2 ).φ x1 = x2 (φ T x 1 x 2. x1 ) φ T x 1 x 2.φ x1 x 1 = x2 (φ x1 x 2. x1 ) φ T x 1 x 2.φ x1 x 1 = x2 (φ x1 x 2. x1 ) (φ x 1 x 2.φ x1 )(φ x1.φ x1 x 1 ) (φ x 1 x 2.φ x2 )(φ x2.φ x1 x 1 ) g 11 g 22 = 2 x 2 g 11 ( x 2 g 11 ) 2 ( x 2 g 22 ) g 11 4g 22 Now φ T x 2 x 2 = φ x1 x 2 (φ x1 x 2.N)N
5 CURVTURE 5 and so, using again the fact that x1 φ. x2 φ = 0, we have x1 (φ T x 2 x 2 ).φ x1 = x1 (φ x2 x 2.φ x1 ) φ T x 2 x 2.φ x1 x 1 = x1 x2 (φ x2.φ x1 ) x1 (φ x2.φ x2 x 1 ) φ T x 2 x 2.φ x1 x 1 = x1 (φ x2.φ x2 x 1 ) φ T x 2 x 2.φ x1 x 1 = x1 (φ x2.φ x2 x 1 ) (φ x 2 x 2.φ x1 )(φ x1.φ x1 x 1 ) (φ x 2 x 2.φ x2 )(φ x2.φ x1 x 1 ) g 11 g 22 = x1 (φ x2.φ x2 x 1 ) + (φ x 1 x 2.φ x2 )(φ x1.φ x1 x 1 ) + (φ x 2 x 2.φ x2 )(φ x1.φ x2 x 1 ) g 11 g 22 = 2 x 1 g x 2 g 22 x2 g 11 4g 22 + x 1 g 22 x1 g 11 4g 11. From this last two identities direct computation shows that [ ( ) 1 = 2 x2 g 11 + g 11 g 22 x 2 g11 g 22 x 1 x1 (φ T x 2 x 2 ).φ x1 x2 (φ T x 1 x 2 ).φ x1 g 11 g 22 ( )] x1 g 22. g11 g 22 (2) Let s solve (a) first. Choose N to be the exterior unit normal and this part is important because if N were the interior unit normal the argument would need to have some (trivial) changes. Choose p S 2 we want to find x S with N(x) = p. Let P t = { x R 3 : p.x = t}. For t very large and positive we have P t S = and clearly there is some s so that P s S is not empty. So we can consider t 0 = inf{t : P s S = for all s t}. We have P t0 S, otherwise we could find ε small so that P t0 ε S = and this contradicts the minimality property of t 0. Let x P t0 S. We want to argue that N(x) = p. From the graphical decomposition lemma we know that in a neighborhood of x we can write S as being the graph of a function defined on a set of T x S. More rigorously, there is an open set U T x S, an open neighborhood V of x in R 3, and a function f : U R so that S V = {f(y)n(x) + y + x : y U}. The function f has f(0, 0) = 0 and f(0, 0) = 0 necessarily (why?). Now we must have z.p t 0 for all z S and x.p = t 0 (why?). Thus using the graphical parametrization t 0 (f(y)n(x) + y + x).p = f(y)n(x).p + y.p + t 0. In other words g(y) = f(y)n(x).p + y.p + t 0 has a global maximum at y = 0 and, because f(0, 0) = 0, this implies p is orthogonal to T x S (because x g(0, 0) = y g(0, 0) = 0). The fact that N was the exterior unit normal implies N(x) = p instead of N(x) = p.
6 6 NDRÉ NEVES We just need to conclude that K(x) 0. Well, by what we have done we have T x S + s = P t0 and S lies all in one side of P t0. Thus if K(x) < 0, the surface would lie on both sides of P t0, which is impossible. For part (b) consider L ε = {x S : K(x) > ε} which is a surface as well (the set {x : K(x) 0}) is not surface strictus sensus). Restrict the Gauss map to N : L ε S 2 which is surjective. Then by the hint 4π det N d = K d = Kd + K d L ε L ε L 0 L ε\l 0 Kd + εarea(s) = K + d + εarea(s). L 0 S Making ε tend to zero the result follows. (3) The first step is to show that G is a linear map. Fix a point p and consider α 1 (t) = p + tx, where X is any vector in R 3. Set F (t) = G α 1 (t) G(q). By differentiation in the t variable when t = 0 we have F (t) 2 = tx + p q 2 = F (0).F (0) = X.(p q) = DG p (X).(G(p) G(q)) = X.(p q) = X.(DG p ) (G(p) G(q)) = X.(p q) for all X and all q. In the last line we use the fact that if is a 3 3 matrix with transpose T, then (X).Y = X. T (Y ) for every vectors X, Y. Because the identity we derived is valid for all X it is simple to conclude that (DG p ) (G(p) G(q)) = p q for every q R 3. In particular the linear map (DG p ) T is surjective and thus injective. Denoting by B its inverse we have from the above formula that G(p) = G(q) + B(p q)for every p, q R 3. This show that G is a linear map (just take p 0 = G(0) B(0) for some q fixed.) Using again the hypothesis we obtain that B(p q) = G(p) G(q) = p q. Thus B preserves distances and so it is an isometry of R 3 as well. It is easy to conclude that B O(3) by differentiating the identity below and set t = 0 B(X + ty ).B(X + ty ) = X + ty 2 = X 2 + 2tX.Y + t 2 Y 2. (4) First a). From the previous exercise we know that F is a linear isometry of R 3, which means that DF p = DF 0 for all p in R 3 (i.e. F is linear) and DF 0 (X).DF 0 (Y ) = X.Y for all vectors X, Y in
7 CURVTURE 7 R 3. In particular for every p S we have that DF p = DF 0 and DF p (X).DF p (Y ) = X.Y for all X, Y in T p S, which means that F is an isometry of S. Technically speaking one should show that if F is an ambient map of R 3, then the restriction of F to S has the property that for every X T p S, then DF p (X) as it was defined in class is nothing but DF p (X) as it was defined in multivariable calculus. This is just the chain rule and so I will not say anything else to avoid the risk of just ending up making it more confusing. Now b). The orthogonal linear transformations are just those 3 3 matrices which have T = Id, i.e., 1 = T. In this case we have (X) 2 = (X).(x) = X. T (X) = X.X = X 2 and so send the unit sphere into the unit sphere. Moreover (X) (Y ) = (X Y ) = X Y, and so its distance preserving in the sense of Exercise 8. Thus a) can be applied to conclude is an isometry of the sphere. Finally c). Take F (x, y, 0) = (cos x, sin x, y) which is an isometry from part of the plane {z = 0} into part of the cylinder {x 2 +y 2 = 1}. F is not distance preserving because F (0, 0, 0) F (θ, 0, 0) = 2(1 cos θ) and (0, 0, 0) (θ, 0, 0) = θ. (5) Consider F (x, y, z) = (x, y, z). This is an isometry of the cylinder S = {x 2 + y 2 = 1} because F send the S into S and F is an orthogonal linear transformation. Finally F (x, y, z) = (x, y, z) then z = 0 and y = 0. But x 2 + y 2 = 1, which means x = ±1, i.e., the only fixed points are (1, 0, 0) and ( 1, 0, 0). (6) Let s use exercise 1. Set λ = e 2u for convenience. Then we have g11 g 22 = e 2u, x2 g 11 (g 11 g 22 ) 1/2 = 2 x 2 u, x1 g 22 (g 11 g 22 ) 1/2 = 2 x 1 u. Thus, recalling that h = x1 x 1 h + x2 x 2 h, we obtain K = e 2u u = 1 ln λ. 2λ (Recall that ln λ = 1 2 ln λ). To be consistent with the rest of the notation I should use λ = (x x2 2 + c) 2 for the last part. Then and xi ln λ = 2 xi ln(x x 2 4x i 2 + c) = x x2 2 + c x 2 4 i x i ln λ = x x2 2 + c + 8x 2 i (x x c)2
8 8 NDRÉ NEVES Thus 8 ln λ = x x2 2 + c + 8(x2 1 + x2 2 ) (x x2 2 + = 8c c)2 (x = 8cλ x2 2 + c)2 and so K = 1 ln λ = 4c. 2λ (7) We have x u = (cos v, sin v, u 1 ), x = ( u sin v, u cos v, 0) v and so g 11 = x u. x u = 1 + u 2, g 12 = x u. x v = 0, g 22 = x v. x v = u2. Because v g 11 = 0 we obtain from Exercise 1 ( ) 1 K = 2 2u 1 = 1 + u 2 u 1 + u 2 (1 + u 2 ) 2 We have x x = (cos v, sin v, 0), = ( u sin v, u cos v, 0) u v and so ḡ 11 = x. x u u = 1, ḡ 12 = x. x u v = 0, ḡ 22 = x. x v v = 1 + u2. Using the formula in Exercise 1 we have ( ) 1 K = 2 2u 1 = 1 + u 2 u 1 + u 2 (1 + u 2 ) 2. Therefore the surfaces have the same curvature. If the map x x 1 were an isometry then by Proposition 1, page 220, we would have g 11 = ḡ 11, g 12 = ḡ 12, and g 22 = ḡ 22. Because this is false the surfaces are not isometric. (8) If there was such a surface then from the definition of Gaussian curvature we would have K = ( )(g 11g 22 g 2 12 ) 1 = 1. But from Gauss s Theorem we know that K is intrinsic, i.e., depends only on g 11, g 12 and g 12. Thus K must be zero because we can surely put coordinates on a plane which have g 11 = g 22 = 1, g 12 = 0 and a plane has Gaussian curvature zero. (9) Because K > 0, we have from Gauss-Bonnet that S is diffeomorphic to a sphere. Thus andb are both diffeomorphic to a disc and
9 CURVTURE 9 = B = γ is a geodesic. Hence, if ν denotes the interior unit normal to we have Kd + k.νdσ = 2π = Kd = 2π. Likewise B Kd = 2π. I will show now that area(n()) = Kd = 2π and this completes the exercise. The first remark is that the Gaussian map N must be a diffeomorphism. Why? Well, because K = det = detdn, we have from the inverse function theorem that N is locally a diffeomorphism. nd it happens that locally diffeomorphisms from a sphere into a sphere must be global diffeomorphism. The second remark is to show the hint, namely that area(f ()) = detdf d. Suppose that φ : D R 2 is a chart for. Then ψ = F φ is a chart for N = {x 2 + y 2 + z 2 = 1} because F is a diffeomorphism. From the lectures we know that area(ψ(d)) = ψ x ψ y dxdy. D Now let {e 1, e 2 } be a basis for T φ(p) which has df p (e 1 ) = λ 1 e 1 and df p (e 2 ) = λ 2 e 2, i.e., {e 1, e 2 } is an eigenbasis for df. Then and thus x (p) = a 1e 1 + b 1 e 2 y (p) = a 2e 1 + b 2 e 2 x y = a 1b 2 a 2 b 1 e 1 e 2. Furthermore ( ) ψ x (p) = df φ(p) x (p) = λ 1 a 1 e 1 + λ 2 b 1 e 2 and ( ) ψ y (p) = df φ(p) y (p) = λ 1 a 2 e 1 + λ 2 b 2 e 2. Thus ψ x ψ y = λ 1λ 2 a 1 b 2 a 2 b 1 e 1 e 2 = detdf φ(p) x y.
10 10 NDRÉ NEVES (This formula was long to derive but it should be highly expected). Therefore we have area(f (φ(d))) = area(ψ(d)) = ψ D x ψ y dxdy. = detdf φ(p) x y dxdy = detdf d. D φ(d) Covering with a finite number of charts we arrive at area(f ()) = detdf d. Using this formula with F = N the Gauss map we obtain area(n()) = detdn d = K d = Kd = 2π. (10) If the path α(t) = (θ(t), z(t)), choose z 1 so that z(t) > z 1 for all t [0, 1] and consider the path β(t) = (2πt, z 1 ). Note that φ β is a geodesic and there is a region R in S so that S = γ φ β. To recognize this last fact just let R = φ(), where is the region in [0, 2π] R below α and above β. Note that R is diffemorphic to a cylinder and thus has zero Euler characteristic. Using Gauss-Bonnet we obtain that Kd + k. νdσ + k. νdσ = 0, R γ where ν is the interior unit normal. Because K = 0 and φ β is a geodesic we obtain that γ φ β k. νdσ = 0, which is what we wanted to show. For the second one note that the Euler characteristic of D is one and so by the Gauss-Bonnet Theorem we have Kd + k. νdσ = 2π = k. νdσ = 2π. D Thus, γ cannot be a geodesic. (11) Consider a parametrization of S γ φ(s, θ) = (r(s) cos θ, r(s) sin θ, z(s)). Then s = (r cos θ, r sin θ, z ), θ γ = ( r sin θ, r cos θ, 0),
11 CURVTURE 11 2 φ ( s) 2 = (r cos θ, r sin θ, z ), 2 φ = ( r cos θ, r sin θ, 0), ( θ) 2 and so 2 φ θ s = ( r sin θ, r cos θ, 0). g 11 = (r ) 2 + (z ) 2 = 1, g 12 = 0, g 22 = r 2, where I am assuming, without loss of generality, that (r(s), z(s)) is parametrized by arc-length. If α(t) = φ(s(t), θ(t)) is a curve parametrized by arc-length then, denoting differentiation with respect to t with a dot, we have α = d dt (ṡ sφ + θ θ φ) = s s φ + θ θ φ + (ṡ) 2 2 ssφ + 2ṡ θ 2 sθ φ + ( θ) 2 2 θθ φ. Consider the orthonormal basis X 1, X 2 for the tangent plane of S where We have X 1 = s, X 1 2 = r = ( sin θ, cos θ, 0). θ α.x 1 = s + (ṡ) 2 (r r + z z ) ( θ) 2 rr = s ( θ) 2 rr, where we use the fact that and (r ) 2 + (z ) 2 = 1 = r r + z z = 0, α.x 2 = θ + 2ṡ θrr. The normal vector N is given by N = X 1 X 2 and E = N α is given by E = N (ṡ s φ + θ θ φ) = N (ṡx 1 + θrx 2 ) Therefore = (X 1 X 2 ) (ṡx 1 + θrx 2 ) = ṡx 2 θrx 1. k g = α.e = ṡ( θ + 2ṡ θrr ) θr( s ( θ) 2 rr ) For the case in point we have r(s) = a + r 0 cos(s/r 0 ), z(s) = r 0 sin(s/r 0 ), where (r ) 2 + (z ) 2 = 1. The curves are maximum parallel: α 1 (t) = φ(0, t/r 0 ), minimum parallel: α 2 (t) = φ(r 0 π, t/r 0 ), upper parallel: α 3 (t) = φ(r 0 π/2, t/r 0 )
12 12 NDRÉ NEVES I use the term t/r 0 instead of t because I want the curves to be parametrized by arc-length. In any case we have θ = r0 1, ṡ = θ = 0, and r = sin(s/r 0 ). Using the formula above for k we get k g (α 1 ) = r 3 0 r(0)r (0) = 0 and k g (α 1 ) = r 3 0 r(r 0π)r (r 0 π) = 0 k g (α 3 ) = r0 3 r(r 0π/2)r (r 0 π/2) = a r0 2. Note that k g (α 3 ) compute the geodesic curvature of the upper parallels. (12) We use the coordinate chart k g = d du φ(std, z) = (cos s, sin s, z) which makes it into a local isometry with Euclidean plane, i.e., g 11 = 1 = g 22, F = 0. The plane which intersects the cylinder is P = {z = y tan θ} and so the curve C is parametrized by C(t) = (cos t, sin t, sin t tan θ) = φ(t, sin t tan θ). If e 1 = (1, 0, 0), e 2 = (0, cos θ, sin θ) is an orthonormal basis for P, then C = {xe 1 + ye 2 x 2 + cos 2 θy 2 = 1} and so we see that C is indeed an ellipse in the plane P. To compute its curvature vector we have the little annoying thing that C(t) is not a parametrization by arc-length. Suppose then that t = t(u) is a change of variable which makes C(u) parametrized by arc length. Then, if E denotes a unit vector lying in the tangent plane to to the cylinder which is orthogonal to C (t), we know that k g = k.e. Now and thus ( C (t) dt du dc du = C (t) dt du = ).E = = C (t) 2 C (t).e. dt du = C (t) 1 ( ) dt 2 C (t).e + d2 t du (du) 2 C (t).e = ( ) dt 2 C (t).e du Let s use this formula. C intersects the axis when t = 0, t = π. In this case C (t) = C(t) and so C (0) = (1, 0, 0), C (π) = (1, 0, 0). In both cases C has the direction of the normal vector to the cylinder and so the term C (t).e is zero because E lies in the tangent plane of the cylinder. Thus k g = 0 at those points.
13 CURVTURE 13 (13) One obvious consequence of i) is that s F and t F are never zero. Hence from ii) we get that s F (t, s), t F (t, s) are linearly independent which means that for every (t 0, s 0 ) U, there is a small neighborhood V so that F restricted to V is a chart. Therefore we can use the formula from the first exercise in this problem sheet. From problem 1) it suffices to see that s g 11 = t g 22 = 0. Condition ii) is saying that g 12 = 0. The fact that t F (t, s) is a geodesic in S implies that 2 ttf.e = 0, where E is a unit vector lying in the tangent plane of S and perpendicular to t F (the tangent vector to the curve). Thus, because g 12 = 0 we have that E is a multiple of s F and so 2 ttf. s F = 0. Therefore s g 11 = 2 2 F st. t F = 0. Same reasoning shows that t g 22 = 0. (14) (a) φ is clearly smooth. Suppose φ(u, v) = φ(u, v ). Then we must have v = v by looking at the third component of φ. Thus sin(v) = sin(v ) and cos(v) = cos(v ). If sin(v) 0 we conclude that u = u by looking at the first component of φ. If sin(v) = 0 then cos(v) 0 and so we conclude that u = u by looking at the second component of φ. Note that = (sin v, cos v, 0), u v = (u cos v, u sin v, 1) and thus u φ. v φ = 0. Because u φ, v φ are never zero, this implies they must be linearly independent if they are orthogonal. (b) The matrix that represents the metric g is given by [ ] g11 g 12 = g 21 g 22 The unit normal vector is We have N = uφ v φ u φ v φ [ ] u 2. ( cos v, sin v, u) =. 1 + u 2 2 uuφ = (0, 0, 0), 2 uvφ = (cos v, sin v, 0), 2 vvφ = ( u sin v, u cos v, 0) and so the matrix that represents the second fundamental form is [ ] [ ] = u 2 1 0
14 14 NDRÉ NEVES Therefore, the matrix that represents dn in the basis u φ, v φ is given by [ ] [ ] σ = g = 1 + u 2 0 (1 + u 2 ) [ ] = 1 + u 2 (1 + u 2 ) 1. 0 Hence K = detσ = (1 + u 2 ) 3/2, H = 1 trσ = 0. 2 (15) If H = 0, then λ 1 + λ 2 = 0, which means λ 1 = λ 2. Thus the Gaussian curvature becomes K = λ 1 λ 2 = λ The condition K 0 implies then that K = λ 2 1 = 0, which means λ 1 = λ 2 = 0. Therefore, the surface is umbilic and by a Theorem in the notes we know that it must be contained in a plane (the case of being contained in a sphere it is impossible because K = 0.) (16) (b) We have r φ = (r (s) cos θ, r (s) sin θ, 1), θ φ = ( r(s) sin θ, r(s) cos θ, 0) and so the matrix that represents the metric g is given by [ ] [ g11 g 12 (r = ) 2 ] g 21 g 22 0 r 2. The normal vector is given by Finally, N = rφ θ φ r φ θ φ = ( cos θ, sin θ, r ) 1 + (r ) 2. rr φ = (r cos θ, r sin θ, 0), rθ φ = ( r sin θ, r cos θ, 0) θθ φ = ( r cos θ, r sin θ, 0) and so the matrix that represents the second fundamental form is [ ] [ ] r 0 = (r ) 2 0 r The matrix that represents dn is given by [ σ = g 1 1 ((r = ) 2 + 1) 1 ] [ ] 0 r (r ) 2 0 r 2 0 r [ 1 ((r = ) 2 + 1) 1 r ] (r ) 2 0 r 1.
15 CURVTURE 15 Therefore K = (r ) 2 + 1) 3/2 r 1 r. t a local minimum with r (s 0 ) > 0 we have K = r (s 0 ) r(s 0 ) < 0. If the Gaussian curvature is nonnegative we have K = (r ) 2 + 1) 3/2 r 1 r 0 = r 0. Thus r is a concave function with r(a) = r(b) and this implies that r(s) = r(a) for all a s b. quick way to see this is to note that r 0 implies r is nonincreasing, which means r (a) r (s) r (b). But r(s) r(a) and r(s) r(b) implies r (a) 0 and r (b) 0. Therefore r (s) = 0 for all s and this means r is constant.
88 CHAPTER 2. VECTOR FUNCTIONS. . First, we need to compute T (s). a By definition, r (s) T (s) = 1 a sin s a. sin s a, cos s a
88 CHAPTER. VECTOR FUNCTIONS.4 Curvature.4.1 Definitions and Examples The notion of curvature measures how sharply a curve bends. We would expect the curvature to be 0 for a straight line, to be very small
More informationChapter 2. Parameterized Curves in R 3
Chapter 2. Parameterized Curves in R 3 Def. A smooth curve in R 3 is a smooth map σ : (a, b) R 3. For each t (a, b), σ(t) R 3. As t increases from a to b, σ(t) traces out a curve in R 3. In terms of components,
More informationMATH 425, PRACTICE FINAL EXAM SOLUTIONS.
MATH 45, PRACTICE FINAL EXAM SOLUTIONS. Exercise. a Is the operator L defined on smooth functions of x, y by L u := u xx + cosu linear? b Does the answer change if we replace the operator L by the operator
More informationChapter 17. Orthogonal Matrices and Symmetries of Space
Chapter 17. Orthogonal Matrices and Symmetries of Space Take a random matrix, say 1 3 A = 4 5 6, 7 8 9 and compare the lengths of e 1 and Ae 1. The vector e 1 has length 1, while Ae 1 = (1, 4, 7) has length
More information14.11. Geodesic Lines, Local Gauss-Bonnet Theorem
14.11. Geodesic Lines, Local Gauss-Bonnet Theorem Geodesics play a very important role in surface theory and in dynamics. One of the main reasons why geodesics are so important is that they generalize
More informationThe Math Circle, Spring 2004
The Math Circle, Spring 2004 (Talks by Gordon Ritter) What is Non-Euclidean Geometry? Most geometries on the plane R 2 are non-euclidean. Let s denote arc length. Then Euclidean geometry arises from the
More informationSolutions to old Exam 1 problems
Solutions to old Exam 1 problems Hi students! I am putting this old version of my review for the first midterm review, place and time to be announced. Check for updates on the web site as to which sections
More informationLectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain
Lectures notes on orthogonal matrices (with exercises) 92.222 - Linear Algebra II - Spring 2004 by D. Klain 1. Orthogonal matrices and orthonormal sets An n n real-valued matrix A is said to be an orthogonal
More informationInner Product Spaces
Math 571 Inner Product Spaces 1. Preliminaries An inner product space is a vector space V along with a function, called an inner product which associates each pair of vectors u, v with a scalar u, v, and
More informationSolutions to Homework 10
Solutions to Homework 1 Section 7., exercise # 1 (b,d): (b) Compute the value of R f dv, where f(x, y) = y/x and R = [1, 3] [, 4]. Solution: Since f is continuous over R, f is integrable over R. Let x
More informationMath 241, Exam 1 Information.
Math 241, Exam 1 Information. 9/24/12, LC 310, 11:15-12:05. Exam 1 will be based on: Sections 12.1-12.5, 14.1-14.3. The corresponding assigned homework problems (see http://www.math.sc.edu/ boylan/sccourses/241fa12/241.html)
More informationLecture 1: Schur s Unitary Triangularization Theorem
Lecture 1: Schur s Unitary Triangularization Theorem This lecture introduces the notion of unitary equivalence and presents Schur s theorem and some of its consequences It roughly corresponds to Sections
More informationUnified Lecture # 4 Vectors
Fall 2005 Unified Lecture # 4 Vectors These notes were written by J. Peraire as a review of vectors for Dynamics 16.07. They have been adapted for Unified Engineering by R. Radovitzky. References [1] Feynmann,
More informationComputing Euler angles from a rotation matrix
Computing Euler angles from a rotation matrix Gregory G. Slabaugh Abstract This document discusses a simple technique to find all possible Euler angles from a rotation matrix. Determination of Euler angles
More informationScalar Valued Functions of Several Variables; the Gradient Vector
Scalar Valued Functions of Several Variables; the Gradient Vector Scalar Valued Functions vector valued function of n variables: Let us consider a scalar (i.e., numerical, rather than y = φ(x = φ(x 1,
More informationTOPIC 4: DERIVATIVES
TOPIC 4: DERIVATIVES 1. The derivative of a function. Differentiation rules 1.1. The slope of a curve. The slope of a curve at a point P is a measure of the steepness of the curve. If Q is a point on the
More informationLecture L3 - Vectors, Matrices and Coordinate Transformations
S. Widnall 16.07 Dynamics Fall 2009 Lecture notes based on J. Peraire Version 2.0 Lecture L3 - Vectors, Matrices and Coordinate Transformations By using vectors and defining appropriate operations between
More informationSolutions for Review Problems
olutions for Review Problems 1. Let be the triangle with vertices A (,, ), B (4,, 1) and C (,, 1). (a) Find the cosine of the angle BAC at vertex A. (b) Find the area of the triangle ABC. (c) Find a vector
More information3 Contour integrals and Cauchy s Theorem
3 ontour integrals and auchy s Theorem 3. Line integrals of complex functions Our goal here will be to discuss integration of complex functions = u + iv, with particular regard to analytic functions. Of
More informationLinear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices
MATH 30 Differential Equations Spring 006 Linear algebra and the geometry of quadratic equations Similarity transformations and orthogonal matrices First, some things to recall from linear algebra Two
More informationCurves and Surfaces. Lecture Notes for Geometry 1. Henrik Schlichtkrull. Department of Mathematics University of Copenhagen
Curves and Surfaces Lecture Notes for Geometry 1 Henrik Schlichtkrull Department of Mathematics University of Copenhagen i ii Preface The topic of these notes is differential geometry. Differential geometry
More informationChapter 6. Linear Transformation. 6.1 Intro. to Linear Transformation
Chapter 6 Linear Transformation 6 Intro to Linear Transformation Homework: Textbook, 6 Ex, 5, 9,, 5,, 7, 9,5, 55, 57, 6(a,b), 6; page 7- In this section, we discuss linear transformations 89 9 CHAPTER
More informationMathematical Physics, Lecture 9
Mathematical Physics, Lecture 9 Hoshang Heydari Fysikum April 25, 2012 Hoshang Heydari (Fysikum) Mathematical Physics, Lecture 9 April 25, 2012 1 / 42 Table of contents 1 Differentiable manifolds 2 Differential
More informationL 2 : x = s + 1, y = s, z = 4s + 4. 3. Suppose that C has coordinates (x, y, z). Then from the vector equality AC = BD, one has
The line L through the points A and B is parallel to the vector AB = 3, 2, and has parametric equations x = 3t + 2, y = 2t +, z = t Therefore, the intersection point of the line with the plane should satisfy:
More informationSolutions to Math 51 First Exam January 29, 2015
Solutions to Math 5 First Exam January 29, 25. ( points) (a) Complete the following sentence: A set of vectors {v,..., v k } is defined to be linearly dependent if (2 points) there exist c,... c k R, not
More informationv w is orthogonal to both v and w. the three vectors v, w and v w form a right-handed set of vectors.
3. Cross product Definition 3.1. Let v and w be two vectors in R 3. The cross product of v and w, denoted v w, is the vector defined as follows: the length of v w is the area of the parallelogram with
More informationLecture 14: Section 3.3
Lecture 14: Section 3.3 Shuanglin Shao October 23, 2013 Definition. Two nonzero vectors u and v in R n are said to be orthogonal (or perpendicular) if u v = 0. We will also agree that the zero vector in
More information[1] Diagonal factorization
8.03 LA.6: Diagonalization and Orthogonal Matrices [ Diagonal factorization [2 Solving systems of first order differential equations [3 Symmetric and Orthonormal Matrices [ Diagonal factorization Recall:
More information1 VECTOR SPACES AND SUBSPACES
1 VECTOR SPACES AND SUBSPACES What is a vector? Many are familiar with the concept of a vector as: Something which has magnitude and direction. an ordered pair or triple. a description for quantities such
More informationSOLUTIONS TO EXERCISES FOR. MATHEMATICS 205A Part 3. Spaces with special properties
SOLUTIONS TO EXERCISES FOR MATHEMATICS 205A Part 3 Fall 2008 III. Spaces with special properties III.1 : Compact spaces I Problems from Munkres, 26, pp. 170 172 3. Show that a finite union of compact subspaces
More information5.3 Improper Integrals Involving Rational and Exponential Functions
Section 5.3 Improper Integrals Involving Rational and Exponential Functions 99.. 3. 4. dθ +a cos θ =, < a
More informationA QUICK GUIDE TO THE FORMULAS OF MULTIVARIABLE CALCULUS
A QUIK GUIDE TO THE FOMULAS OF MULTIVAIABLE ALULUS ontents 1. Analytic Geometry 2 1.1. Definition of a Vector 2 1.2. Scalar Product 2 1.3. Properties of the Scalar Product 2 1.4. Length and Unit Vectors
More information4.5 Linear Dependence and Linear Independence
4.5 Linear Dependence and Linear Independence 267 32. {v 1, v 2 }, where v 1, v 2 are collinear vectors in R 3. 33. Prove that if S and S are subsets of a vector space V such that S is a subset of S, then
More informationMatrix Representations of Linear Transformations and Changes of Coordinates
Matrix Representations of Linear Transformations and Changes of Coordinates 01 Subspaces and Bases 011 Definitions A subspace V of R n is a subset of R n that contains the zero element and is closed under
More informationAB2.5: Surfaces and Surface Integrals. Divergence Theorem of Gauss
AB2.5: urfaces and urface Integrals. Divergence heorem of Gauss epresentations of surfaces or epresentation of a surface as projections on the xy- and xz-planes, etc. are For example, z = f(x, y), x =
More informationa 11 x 1 + a 12 x 2 + + a 1n x n = b 1 a 21 x 1 + a 22 x 2 + + a 2n x n = b 2.
Chapter 1 LINEAR EQUATIONS 1.1 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,..., a n, b are given
More informationChapter 20. Vector Spaces and Bases
Chapter 20. Vector Spaces and Bases In this course, we have proceeded step-by-step through low-dimensional Linear Algebra. We have looked at lines, planes, hyperplanes, and have seen that there is no limit
More informationRecall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.
ORTHOGONAL MATRICES Informally, an orthogonal n n matrix is the n-dimensional analogue of the rotation matrices R θ in R 2. When does a linear transformation of R 3 (or R n ) deserve to be called a rotation?
More informationSOLUTIONS TO HOMEWORK ASSIGNMENT #4, MATH 253
SOLUTIONS TO HOMEWORK ASSIGNMENT #4, MATH 253 1. Prove that the following differential equations are satisfied by the given functions: (a) 2 u + 2 u 2 y + 2 u 2 z =0,whereu 2 =(x2 + y 2 + z 2 ) 1/2. (b)
More informationGeometric Transformations
Geometric Transformations Definitions Def: f is a mapping (function) of a set A into a set B if for every element a of A there exists a unique element b of B that is paired with a; this pairing is denoted
More informationSolutions to Practice Problems for Test 4
olutions to Practice Problems for Test 4 1. Let be the line segmentfrom the point (, 1, 1) to the point (,, 3). Evaluate the line integral y ds. Answer: First, we parametrize the line segment from (, 1,
More informationSimilarity and Diagonalization. Similar Matrices
MATH022 Linear Algebra Brief lecture notes 48 Similarity and Diagonalization Similar Matrices Let A and B be n n matrices. We say that A is similar to B if there is an invertible n n matrix P such that
More informationMATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set.
MATH 304 Linear Algebra Lecture 9: Subspaces of vector spaces (continued). Span. Spanning set. Vector space A vector space is a set V equipped with two operations, addition V V (x,y) x + y V and scalar
More information4. Expanding dynamical systems
4.1. Metric definition. 4. Expanding dynamical systems Definition 4.1. Let X be a compact metric space. A map f : X X is said to be expanding if there exist ɛ > 0 and L > 1 such that d(f(x), f(y)) Ld(x,
More informationUniversity of Lille I PC first year list of exercises n 7. Review
University of Lille I PC first year list of exercises n 7 Review Exercise Solve the following systems in 4 different ways (by substitution, by the Gauss method, by inverting the matrix of coefficients
More informationIntroduction to Topology
Introduction to Topology Tomoo Matsumura November 30, 2010 Contents 1 Topological spaces 3 1.1 Basis of a Topology......................................... 3 1.2 Comparing Topologies.......................................
More information1.3. DOT PRODUCT 19. 6. If θ is the angle (between 0 and π) between two non-zero vectors u and v,
1.3. DOT PRODUCT 19 1.3 Dot Product 1.3.1 Definitions and Properties The dot product is the first way to multiply two vectors. The definition we will give below may appear arbitrary. But it is not. It
More informationAdding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors
1 Chapter 13. VECTORS IN THREE DIMENSIONAL SPACE Let s begin with some names and notation for things: R is the set (collection) of real numbers. We write x R to mean that x is a real number. A real number
More informationDifferentiation of vectors
Chapter 4 Differentiation of vectors 4.1 Vector-valued functions In the previous chapters we have considered real functions of several (usually two) variables f : D R, where D is a subset of R n, where
More informationSurface Normals and Tangent Planes
Surface Normals and Tangent Planes Normal and Tangent Planes to Level Surfaces Because the equation of a plane requires a point and a normal vector to the plane, nding the equation of a tangent plane to
More informationIntroduction to Algebraic Geometry. Bézout s Theorem and Inflection Points
Introduction to Algebraic Geometry Bézout s Theorem and Inflection Points 1. The resultant. Let K be a field. Then the polynomial ring K[x] is a unique factorisation domain (UFD). Another example of a
More informationTHE HELICOIDAL SURFACES AS BONNET SURFACES
Tδhoku Math. J. 40(1988) 485-490. THE HELICOIDAL SURFACES AS BONNET SURFACES loannis M. ROUSSOS (Received May 11 1987) 1. Introduction. In this paper we deal with the following question: which surfaces
More informationα = u v. In other words, Orthogonal Projection
Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v
More informationTHREE DIMENSIONAL GEOMETRY
Chapter 8 THREE DIMENSIONAL GEOMETRY 8.1 Introduction In this chapter we present a vector algebra approach to three dimensional geometry. The aim is to present standard properties of lines and planes,
More informationLinear Algebra Notes for Marsden and Tromba Vector Calculus
Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of
More informationOutline Lagrangian Constraints and Image Quality Models
Remarks on Lagrangian singularities, caustics, minimum distance lines Department of Mathematics and Statistics Queen s University CRM, Barcelona, Spain June 2014 CRM CRM, Barcelona, SpainJune 2014 CRM
More informationFundamental Theorems of Vector Calculus
Fundamental Theorems of Vector Calculus We have studied the techniques for evaluating integrals over curves and surfaces. In the case of integrating over an interval on the real line, we were able to use
More informationNOTES ON LINEAR TRANSFORMATIONS
NOTES ON LINEAR TRANSFORMATIONS Definition 1. Let V and W be vector spaces. A function T : V W is a linear transformation from V to W if the following two properties hold. i T v + v = T v + T v for all
More informationSection 11.1: Vectors in the Plane. Suggested Problems: 1, 5, 9, 17, 23, 25-37, 40, 42, 44, 45, 47, 50
Section 11.1: Vectors in the Plane Page 779 Suggested Problems: 1, 5, 9, 17, 3, 5-37, 40, 4, 44, 45, 47, 50 Determine whether the following vectors a and b are perpendicular. 5) a = 6, 0, b = 0, 7 Recall
More informationLINEAR ALGEBRA W W L CHEN
LINEAR ALGEBRA W W L CHEN c W W L Chen, 1997, 2008 This chapter is available free to all individuals, on understanding that it is not to be used for financial gain, and may be downloaded and/or photocopied,
More informationWalrasian Demand. u(x) where B(p, w) = {x R n + : p x w}.
Walrasian Demand Econ 2100 Fall 2015 Lecture 5, September 16 Outline 1 Walrasian Demand 2 Properties of Walrasian Demand 3 An Optimization Recipe 4 First and Second Order Conditions Definition Walrasian
More informationMATH10212 Linear Algebra. Systems of Linear Equations. Definition. An n-dimensional vector is a row or a column of n numbers (or letters): a 1.
MATH10212 Linear Algebra Textbook: D. Poole, Linear Algebra: A Modern Introduction. Thompson, 2006. ISBN 0-534-40596-7. Systems of Linear Equations Definition. An n-dimensional vector is a row or a column
More informationMathematics Course 111: Algebra I Part IV: Vector Spaces
Mathematics Course 111: Algebra I Part IV: Vector Spaces D. R. Wilkins Academic Year 1996-7 9 Vector Spaces A vector space over some field K is an algebraic structure consisting of a set V on which are
More informationMA106 Linear Algebra lecture notes
MA106 Linear Algebra lecture notes Lecturers: Martin Bright and Daan Krammer Warwick, January 2011 Contents 1 Number systems and fields 3 1.1 Axioms for number systems......................... 3 2 Vector
More informationExam 1 Sample Question SOLUTIONS. y = 2x
Exam Sample Question SOLUTIONS. Eliminate the parameter to find a Cartesian equation for the curve: x e t, y e t. SOLUTION: You might look at the coordinates and notice that If you don t see it, we can
More information2.3 Convex Constrained Optimization Problems
42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions
More informationNotes on Orthogonal and Symmetric Matrices MENU, Winter 2013
Notes on Orthogonal and Symmetric Matrices MENU, Winter 201 These notes summarize the main properties and uses of orthogonal and symmetric matrices. We covered quite a bit of material regarding these topics,
More information5.3 The Cross Product in R 3
53 The Cross Product in R 3 Definition 531 Let u = [u 1, u 2, u 3 ] and v = [v 1, v 2, v 3 ] Then the vector given by [u 2 v 3 u 3 v 2, u 3 v 1 u 1 v 3, u 1 v 2 u 2 v 1 ] is called the cross product (or
More informationDECOMPOSING SL 2 (R)
DECOMPOSING SL 2 R KEITH CONRAD Introduction The group SL 2 R is not easy to visualize: it naturally lies in M 2 R, which is 4- dimensional the entries of a variable 2 2 real matrix are 4 free parameters
More information1 if 1 x 0 1 if 0 x 1
Chapter 3 Continuity In this chapter we begin by defining the fundamental notion of continuity for real valued functions of a single real variable. When trying to decide whether a given function is or
More informationReview Sheet for Test 1
Review Sheet for Test 1 Math 261-00 2 6 2004 These problems are provided to help you study. The presence of a problem on this handout does not imply that there will be a similar problem on the test. And
More informationF Matrix Calculus F 1
F Matrix Calculus F 1 Appendix F: MATRIX CALCULUS TABLE OF CONTENTS Page F1 Introduction F 3 F2 The Derivatives of Vector Functions F 3 F21 Derivative of Vector with Respect to Vector F 3 F22 Derivative
More informationSolutions to Practice Problems
Higher Geometry Final Exam Tues Dec 11, 5-7:30 pm Practice Problems (1) Know the following definitions, statements of theorems, properties from the notes: congruent, triangle, quadrilateral, isosceles
More informationElasticity Theory Basics
G22.3033-002: Topics in Computer Graphics: Lecture #7 Geometric Modeling New York University Elasticity Theory Basics Lecture #7: 20 October 2003 Lecturer: Denis Zorin Scribe: Adrian Secord, Yotam Gingold
More information160 CHAPTER 4. VECTOR SPACES
160 CHAPTER 4. VECTOR SPACES 4. Rank and Nullity In this section, we look at relationships between the row space, column space, null space of a matrix and its transpose. We will derive fundamental results
More informationFOUNDATIONS OF ALGEBRAIC GEOMETRY CLASS 22
FOUNDATIONS OF ALGEBRAIC GEOMETRY CLASS 22 RAVI VAKIL CONTENTS 1. Discrete valuation rings: Dimension 1 Noetherian regular local rings 1 Last day, we discussed the Zariski tangent space, and saw that it
More informationMicroeconomic Theory: Basic Math Concepts
Microeconomic Theory: Basic Math Concepts Matt Van Essen University of Alabama Van Essen (U of A) Basic Math Concepts 1 / 66 Basic Math Concepts In this lecture we will review some basic mathematical concepts
More informationSection 13.5 Equations of Lines and Planes
Section 13.5 Equations of Lines and Planes Generalizing Linear Equations One of the main aspects of single variable calculus was approximating graphs of functions by lines - specifically, tangent lines.
More informationMath 181 Handout 16. Rich Schwartz. March 9, 2010
Math 8 Handout 6 Rich Schwartz March 9, 200 The purpose of this handout is to describe continued fractions and their connection to hyperbolic geometry. The Gauss Map Given any x (0, ) we define γ(x) =
More informationSystems of Linear Equations
Systems of Linear Equations Beifang Chen Systems of linear equations Linear systems A linear equation in variables x, x,, x n is an equation of the form a x + a x + + a n x n = b, where a, a,, a n and
More informationPROJECTIVE GEOMETRY. b3 course 2003. Nigel Hitchin
PROJECTIVE GEOMETRY b3 course 2003 Nigel Hitchin hitchin@maths.ox.ac.uk 1 1 Introduction This is a course on projective geometry. Probably your idea of geometry in the past has been based on triangles
More informationLecture L5 - Other Coordinate Systems
S. Widnall, J. Peraire 16.07 Dynamics Fall 008 Version.0 Lecture L5 - Other Coordinate Systems In this lecture, we will look at some other common systems of coordinates. We will present polar coordinates
More informationStirling s formula, n-spheres and the Gamma Function
Stirling s formula, n-spheres and the Gamma Function We start by noticing that and hence x n e x dx lim a 1 ( 1 n n a n n! e ax dx lim a 1 ( 1 n n a n a 1 x n e x dx (1 Let us make a remark in passing.
More informationLecture 2: Homogeneous Coordinates, Lines and Conics
Lecture 2: Homogeneous Coordinates, Lines and Conics 1 Homogeneous Coordinates In Lecture 1 we derived the camera equations λx = P X, (1) where x = (x 1, x 2, 1), X = (X 1, X 2, X 3, 1) and P is a 3 4
More informationA characterization of trace zero symmetric nonnegative 5x5 matrices
A characterization of trace zero symmetric nonnegative 5x5 matrices Oren Spector June 1, 009 Abstract The problem of determining necessary and sufficient conditions for a set of real numbers to be the
More informationMATH 304 Linear Algebra Lecture 20: Inner product spaces. Orthogonal sets.
MATH 304 Linear Algebra Lecture 20: Inner product spaces. Orthogonal sets. Norm The notion of norm generalizes the notion of length of a vector in R n. Definition. Let V be a vector space. A function α
More informationDIFFERENTIABILITY OF COMPLEX FUNCTIONS. Contents
DIFFERENTIABILITY OF COMPLEX FUNCTIONS Contents 1. Limit definition of a derivative 1 2. Holomorphic functions, the Cauchy-Riemann equations 3 3. Differentiability of real functions 5 4. A sufficient condition
More information0 <β 1 let u(x) u(y) kuk u := sup u(x) and [u] β := sup
456 BRUCE K. DRIVER 24. Hölder Spaces Notation 24.1. Let Ω be an open subset of R d,bc(ω) and BC( Ω) be the bounded continuous functions on Ω and Ω respectively. By identifying f BC( Ω) with f Ω BC(Ω),
More informationAlgebra. Exponents. Absolute Value. Simplify each of the following as much as possible. 2x y x + y y. xxx 3. x x x xx x. 1. Evaluate 5 and 123
Algebra Eponents Simplify each of the following as much as possible. 1 4 9 4 y + y y. 1 5. 1 5 4. y + y 4 5 6 5. + 1 4 9 10 1 7 9 0 Absolute Value Evaluate 5 and 1. Eliminate the absolute value bars from
More informationThe Matrix Elements of a 3 3 Orthogonal Matrix Revisited
Physics 116A Winter 2011 The Matrix Elements of a 3 3 Orthogonal Matrix Revisited 1. Introduction In a class handout entitled, Three-Dimensional Proper and Improper Rotation Matrices, I provided a derivation
More informationGeometric Transformation CS 211A
Geometric Transformation CS 211A What is transformation? Moving points (x,y) moves to (x+t, y+t) Can be in any dimension 2D Image warps 3D 3D Graphics and Vision Can also be considered as a movement to
More information6.2 Permutations continued
6.2 Permutations continued Theorem A permutation on a finite set A is either a cycle or can be expressed as a product (composition of disjoint cycles. Proof is by (strong induction on the number, r, of
More informationMA 408 Computer Lab Two The Poincaré Disk Model of Hyperbolic Geometry. Figure 1: Lines in the Poincaré Disk Model
MA 408 Computer Lab Two The Poincaré Disk Model of Hyperbolic Geometry Put your name here: Score: Instructions: For this lab you will be using the applet, NonEuclid, created by Castellanos, Austin, Darnell,
More informationBALTIC OLYMPIAD IN INFORMATICS Stockholm, April 18-22, 2009 Page 1 of?? ENG rectangle. Rectangle
Page 1 of?? ENG rectangle Rectangle Spoiler Solution of SQUARE For start, let s solve a similar looking easier task: find the area of the largest square. All we have to do is pick two points A and B and
More informationVector and Matrix Norms
Chapter 1 Vector and Matrix Norms 11 Vector Spaces Let F be a field (such as the real numbers, R, or complex numbers, C) with elements called scalars A Vector Space, V, over the field F is a non-empty
More informationComputing Orthonormal Sets in 2D, 3D, and 4D
Computing Orthonormal Sets in 2D, 3D, and 4D David Eberly Geometric Tools, LLC http://www.geometrictools.com/ Copyright c 1998-2016. All Rights Reserved. Created: March 22, 2010 Last Modified: August 11,
More informationTWO-DIMENSIONAL TRANSFORMATION
CHAPTER 2 TWO-DIMENSIONAL TRANSFORMATION 2.1 Introduction As stated earlier, Computer Aided Design consists of three components, namely, Design (Geometric Modeling), Analysis (FEA, etc), and Visualization
More informationTHE COMPLEX EXPONENTIAL FUNCTION
Math 307 THE COMPLEX EXPONENTIAL FUNCTION (These notes assume you are already familiar with the basic properties of complex numbers.) We make the following definition e iθ = cos θ + i sin θ. (1) This formula
More informationCalculus with Parametric Curves
Calculus with Parametric Curves Suppose f and g are differentiable functions and we want to find the tangent line at a point on the parametric curve x f(t), y g(t) where y is also a differentiable function
More informationVector Math Computer Graphics Scott D. Anderson
Vector Math Computer Graphics Scott D. Anderson 1 Dot Product The notation v w means the dot product or scalar product or inner product of two vectors, v and w. In abstract mathematics, we can talk about
More information