Orthogonal Matrices. u v = u v cos(θ) T (u) + T (v) = T (u + v). It s even easier to. If u and v are nonzero vectors then

Similar documents
ISOMETRIES OF R n KEITH CONRAD

Lectures notes on orthogonal matrices (with exercises) Linear Algebra II - Spring 2004 by D. Klain

LINEAR ALGEBRA W W L CHEN

Chapter 17. Orthogonal Matrices and Symmetries of Space

α = u v. In other words, Orthogonal Projection

Lecture L3 - Vectors, Matrices and Coordinate Transformations

Unified Lecture # 4 Vectors

Linear Algebra Notes for Marsden and Tromba Vector Calculus

Chapter 6. Orthogonality

Section 1.1. Introduction to R n

Geometric Transformations

Vector Notation: AB represents the vector from point A to point B on a graph. The vector can be computed by B A.

Chapter 6. Linear Transformation. 6.1 Intro. to Linear Transformation

NOTES ON LINEAR TRANSFORMATIONS

9 MATRICES AND TRANSFORMATIONS

Equations Involving Lines and Planes Standard equations for lines in space

Lecture 14: Section 3.3

5.3 The Cross Product in R 3

Section 1.4. Lines, Planes, and Hyperplanes. The Calculus of Functions of Several Variables

Chapter 19. General Matrices. An n m matrix is an array. a 11 a 12 a 1m a 21 a 22 a 2m A = a n1 a n2 a nm. The matrix A has n row vectors

Adding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors

28 CHAPTER 1. VECTORS AND THE GEOMETRY OF SPACE. v x. u y v z u z v y u y u z. v y v z

MAT 1341: REVIEW II SANGHOON BAEK

Figure 1.1 Vector A and Vector F

Inner Product Spaces and Orthogonality

is in plane V. However, it may be more convenient to introduce a plane coordinate system in V.

12.5 Equations of Lines and Planes

Solutions to Math 51 First Exam January 29, 2015

9.4. The Scalar Product. Introduction. Prerequisites. Learning Style. Learning Outcomes

LS.6 Solution Matrices

Section Continued

Rotation Matrices and Homogeneous Transformations

Exam 1 Sample Question SOLUTIONS. y = 2x

Orthogonal Projections

Linear algebra and the geometry of quadratic equations. Similarity transformations and orthogonal matrices

Recall that two vectors in are perpendicular or orthogonal provided that their dot

L 2 : x = s + 1, y = s, z = 4s Suppose that C has coordinates (x, y, z). Then from the vector equality AC = BD, one has

1 Symmetries of regular polyhedra

Inner Product Spaces

3. INNER PRODUCT SPACES

MAT 200, Midterm Exam Solution. a. (5 points) Compute the determinant of the matrix A =

Linear Maps. Isaiah Lankham, Bruno Nachtergaele, Anne Schilling (February 5, 2007)

MATH APPLIED MATRIX THEORY

9 Multiplication of Vectors: The Scalar or Dot Product

MATH 304 Linear Algebra Lecture 20: Inner product spaces. Orthogonal sets.

1 VECTOR SPACES AND SUBSPACES

Similarity and Diagonalization. Similar Matrices

Matrix Representations of Linear Transformations and Changes of Coordinates

3. Let A and B be two n n orthogonal matrices. Then prove that AB and BA are both orthogonal matrices. Prove a similar result for unitary matrices.

University of Lille I PC first year list of exercises n 7. Review

VECTOR ALGEBRA A quantity that has magnitude as well as direction is called a vector. is given by a and is represented by a.

A QUICK GUIDE TO THE FORMULAS OF MULTIVARIABLE CALCULUS

Systems of Linear Equations

Physics 235 Chapter 1. Chapter 1 Matrices, Vectors, and Vector Calculus

TWO-DIMENSIONAL TRANSFORMATION

Recall the basic property of the transpose (for any A): v A t Aw = v w, v, w R n.

2D Geometric Transformations

Cross product and determinants (Sect. 12.4) Two main ways to introduce the cross product

Section 9.5: Equations of Lines and Planes

Geometry of Vectors. 1 Cartesian Coordinates. Carlo Tomasi

[1] Diagonal factorization

5. Orthogonal matrices

MAT188H1S Lec0101 Burbulla

Au = = = 3u. Aw = = = 2w. so the action of A on u and w is very easy to picture: it simply amounts to a stretching by 3 and 2, respectively.

LINES AND PLANES CHRIS JOHNSON

Linear Algebra Notes

6. Vectors Scott Surgent (surgent@asu.edu)

Math 215 HW #6 Solutions

Solutions to old Exam 1 problems

v 1 v 3 u v = (( 1)4 (3)2, [1(4) ( 2)2], 1(3) ( 2)( 1)) = ( 10, 8, 1) (d) u (v w) = (u w)v (u v)w (Relationship between dot and cross product)

Geometric description of the cross product of the vectors u and v. The cross product of two vectors is a vector! u x v is perpendicular to u and v

MATH 304 Linear Algebra Lecture 18: Rank and nullity of a matrix.

Mathematics Course 111: Algebra I Part IV: Vector Spaces

by the matrix A results in a vector which is a reflection of the given

Vectors Math 122 Calculus III D Joyce, Fall 2012

BALTIC OLYMPIAD IN INFORMATICS Stockholm, April 18-22, 2009 Page 1 of?? ENG rectangle. Rectangle

Name: Section Registered In:

Section Inner Products and Norms

x = + x 2 + x

One advantage of this algebraic approach is that we can write down

Math 115A HW4 Solutions University of California, Los Angeles. 5 2i 6 + 4i. (5 2i)7i (6 + 4i)( 3 + i) = 35i + 14 ( 22 6i) = i.

13 MATH FACTS a = The elements of a vector have a graphical interpretation, which is particularly easy to see in two or three dimensions.

Identifying second degree equations

Vector Math Computer Graphics Scott D. Anderson

Vector Algebra II: Scalar and Vector Products

1.3. DOT PRODUCT If θ is the angle (between 0 and π) between two non-zero vectors u and v,

Inner product. Definition of inner product

1 Determinants and the Solvability of Linear Systems

The Dot and Cross Products

Data Mining: Algorithms and Applications Matrix Math Review

LINEAR ALGEBRA W W L CHEN

Lecture 3: Coordinate Systems and Transformations

Dot product and vector projections (Sect. 12.3) There are two main ways to introduce the dot product

Section 13.5 Equations of Lines and Planes

x1 x 2 x 3 y 1 y 2 y 3 x 1 y 2 x 2 y 1 0.

Vector has a magnitude and a direction. Scalar has a magnitude

Math 241, Exam 1 Information.

2 Session Two - Complex Numbers and Vectors

Solving Systems of Linear Equations

Problem Set 5 Due: In class Thursday, Oct. 18 Late papers will be accepted until 1:00 PM Friday.

Transcription:

Part 2. 1 Part 2. Orthogonal Matrices If u and v are nonzero vectors then u v = u v cos(θ) is 0 if and only if cos(θ) = 0, i.e., θ = 90. Hence, we say that two vectors u and v are perpendicular or orthogonal (in symbols u v) if u v = 0. A vector u is a unit vector if u = 1. Now rotate the vectors, i.e., apply T. Let T : R 2 R 2 be rotation around the origin by θ degrees. This is a linear transformation. To see this, consider the addition of u and v as in the following diagram. This diagram shows that T (u) + T (v) = T (u + v). It s even easier to

Part 2. 3 Part 2. check that T (sv) = st (v), so T is linear. We need to find T (e 1 ), T (e 2 ). If we rotate e 1 by θ, we get (cos(θ), sin(θ)) by the unit circle definition of sin and cos. Recall the addition laws for sin and cos. cos(a + B) = cos(a) cos(b) sin(a) sin(b) cos(a B) = cos(a) cos(b) + sin(a) sin(b) sin(a + B) = sin(a) cos(b) + cos(a) sin(b) sin(a B) = sin(a) cos(b) cos(a) sin(b) Recall also that cos( θ) = cos(θ) sin( θ) = sin(θ) The vector w = ( sin(θ), cos(θ)) is a unit vector and w T (e 1 ) = 0, so T (e 2 ) must be either w or w. Considering the sign of the first component shows that T (e 2 ) = w. Thus, the matrix of T is R(θ) = cos θ sin(θ). sin(θ) cos(θ) Exercise: Rotating by θ and then by ϕ should be the same as rotating θ + ϕ. Thus, we have R(θ)R(ϕ) = R(θ + ϕ) = R(ϕ)R(θ). Use this and matrix multiplication to derive the addition laws for sin and cos. What is R(θ) 1?. Exercise: What is the matrix of reflection through the x-axis?

Part 2. 5 Part 2. A matrix A is orthogonal if Av = v for all vectors v. If A and B are orthogonal, so is AB. If A is orthogonal, so is A 1. Clearly I is orthogonal.. Rotation matrices are orthogonal. The set of orthogonal 2 2 matrices is denoted by O(2). Classification of Orthogonal Matrices If A is orthogonal, then Au Av = u v for all vectors u and v. (It follows that A preserves the angles between vectors). To see this, recall that 2u v = u 2 + v 2 u v 2. Hence, if A is orthogonal, we have 2Au Av = Au 2 + Av 2 Au Av 2 = Au 2 + Av 2 A(u v) 2 = u 2 + v 2 u v 2 = 2u v so Au Av = u v. If A is orthogonal, we must have Ae 1 2 = Ae 1 Ae 1 = e 1 e 1 = 1 ( ) Ae 2 2 = Ae 1 Ae 2 = e 2 e 2 = 1 Ae 1 Ae 2 = e 1 e 2 = 0. Exercise: If A satisfies ( ), A is orthogonal. Equations ( ) say that the columns of A are orthogonal unit vectors. Thus, if the first column of A is a, a 2 + b 2 = 1 b

Part 2. 7 Part 2. the possibilities for the second column are b, a b a So A must look like one of the matrices a b, a b. b a b a Since a 2 + b 2 = 1, we can find θ so that a = cos(θ) and b = sin(θ). Thus, in one case, we get A = a b = cos θ b a sin(θ) sin(θ) = R(θ), cos(θ) so A is rotation by θ degrees. In the other case we have A = a b = cos θ b a sin(θ) sin(θ) cos(θ) Call this matrix S(θ). What does this represent geometrically? We claim that there is a line L through the origin so that A = S(θ) leaves every point on L fixed. To see this, it will suffice to find a unit vector u = cos(ϕ) sin(ϕ) so that Au = u. If we do this, any other vector along L will have the form tu for some scalar t, and we will have A(tu) = tau = tu. Since we only need to consider each line through the origin once, we can suppose 0 ϕ < 180. We may as well suppose that 0 θ < 360. Thus, we are trying to find ϕ so that Au = u, i.e. cos θ sin(θ) cos(ϕ) = cos(ϕ) sin(θ) cos(θ) sin(ϕ) sin(ϕ)

Part 2. 9 Part 2. 1 If we carry out the multiplication, this becomes cos(θ) cos(ϕ) + sin(θ) sin(ϕ) = cos(ϕ) sin(θ) cos(ϕ) cos(θ) sin(ϕ) sin(ϕ) Using the addition laws, this is the same as cos(θ ϕ) = cos(ϕ) sin(θ ϕ) sin(ϕ) Because of our angle restrictions, we must have θ ϕ = ϕ, so ϕ = θ/2. Thus, Au = u for u = cos(θ/2) sin(θ/2). Consider the vector v = sin(θ/2), cos(θ/2) which is a unit vector orthogonal to u. Calculate Av as follows Av = cos θ sin(θ) sin(θ/2) sin(θ) cos(θ) cos(θ/2) cos(θ) sin(θ/2) + sin(θ) cos(θ/2) = sin(θ) sin(θ/2) cos(θ) cos(θ/2) sin(θ θ/2) = cos(θ θ/2) = sin(θ/2) cos(θ/2) = v, So Av = v. A little thought shows that A is reflection through the line L that passes through the origin and is parallel to u. Any vector w can be written as w = su + tv for scalars s and t and A(su + tv) = sau + tav = su tv.

Part 2. 11 Part 2. 1 Isometries of the Plane Exercise: Verify the following equations. R(θ)S(ϕ) = S(θ + ϕ) S(ϕ)R(θ) = S(ϕ θ) S(θ)S(ϕ) = R(θ ϕ). If a point has coordinates (x, y), the vector with components (x, y) is the vector from the origin to the point. This is called the position vector of the point. Generally, we use the same notation for the point and its position vector. The distance between two points p and q in R 2 is given by d(p, q) = p q. A transformation T : R 2 R 2 is called an isometry if it is 1-1 and onto and d(t (p), T (q)) = d(p, q) for all points p and q. One kind of isometry we have discovered is T (p) = Ap, where A is an orthogonal matrix. Another kind of isometry is translation. Let v be a fixed vector and define T v by

Part 2. 13 Part 2. 1 T v (p) = p + v. Translations are not linear. The identity mapping id: R 2 R 2 defined by id(p) = p is obviously an isometry. The composition of two isometries is an isometry. The inverse of an isometry is an isometry. Let x, y and z be noncollinear points. Then a point p is uniquely determined by the three numbers d(x, p), d(y, p) and d(z, p). If x, y and z are three noncollinear points and T is an isometry such that T (x) = x, T (y) = y and T (z) = z, then T = id. To see this, suppose that p is any point. Then p is determined by the numbers d(x, p), d(y, p) and d(z, p). Since T is an isometry d(x, p) = d(t (x), T (p)) = d(x, T (p)). Similarly, d(y, T (p)) = d(y, p) and d(z, T (p)) = d(z, p). Thus, we must have T (p) = p. Problem: If points x,y and z form a right triangle with the right angle at x, then T (x), T (y) and T (z) form a right triangle with the right angle at T (x).

Part 2. 15 Part 2. 1 Theorem If T is an isometry, there is a vector v and an orthogonal matrix A so that T (p) = Ap + v for all p, i.e., T is the composition of a rotation or reflection at the origin and a translation. Proof: The points 0, e 1 and e 2 are noncollinear and form a right triangle, so the points T (0), T (e 1 ) and T (e 2 ) form a right triangle. Let v = T (0). Let T v be translation by v. Then T = T v T is an isometry and T (0) = 0. We must have d(0, T (e 1 )) = d(0, e 1 ) = 1, so we can rotate T (e 1 ) around the origin so it coincides with e 1. Let R be the rotation matrix for this rotation. Then T = RT v T is an isometry with T (0) = 0 and T (e 1 ) = e 1. Since d(t (e 2 ), 0) = 1 and the points 0, e 1 and T (e 2 ) form a right triangle, T (e 2 ) must be either e 2 or e 2. If it s e 2 let M = I and if it s e 2 let M be the matrix of reflection through the x axis. In either case M is an orthogonal matrix and MT (e2) = e 2. Let T = MRT v T. Then T (0) = 0, T (e 1 ) = e 1 and T (e 2 ) = e 2, so T must be the identity. If we set A = MR, A is orthogonal and we have T = AT v T = id. Thus, for any point point p, AT v T (p) = p. Multiplying by A 1 we get T v T (p) = A 1 p. Apply T v to both sides to get T (p) = T v A 1 p = A 1 p v. Since A 1 is orthogonal, the proof is complete. Every isometry can be written in the form T (x) = Ax + u. To abbreviate this, we ll simply denote this isometry by (A u), so the rule is (A u)(x) = Ax + u In this notation, (I 0) = id, (I u) is translation by u, and (A 0) is

Part 2. 17 Part 2. 1 transformation x Ax. Given isometries (A u) and (B v), we have (A u)(b v)(x) = (A u)(bx + v) = A(Bx + v) + u = ABx + Av + u = (AB u + Av)(x), so the rule for combining these symbols is (A u)(b v) = (AB u + Av). Exercise: Verify that (A u) 1 = (A 1 A 1 u) A Glide Reflection is reflection in some line followed by translation in a direction parallel to the line. Theorem Every isometry falls into one of the following mutually exclusive cases. I. The identity. II. A translation. III. Rotation about some point (i.e., not necessarily the origin). IV. Reflection about some line. V. A glide reflection. We already know that (I 0) is the identity (Case I), (0 u) is translation (Case II) and that (A 0) is either a rotation about the origin or a reflection about a line through the origin. Consider (A w) where A is a rotation matrix, A = R(θ). A Rotation leaves fixed the rotation center, so we expect (A w) to have a fixed point, i.e., a point p so that Ap + w = p. Rearranging this equation gives w = p Ap = (I A)p, so p is the

Part 2. 19 Part 2. 2 solution, if any, of the equation ( ) w = (I A)p. The matrix I A is given by I A = 1 cos(θ) sin(θ). sin(θ) 1 cos(θ) so det(a) = (1 cos(θ)) 2 + sin 2 (θ) = 1 2 cos θ + cos 2 (θ) + sin 2 (θ) = 2 2 cos θ. This is nonzero as long as cos(θ) 1. If cos(θ) = 1, then A = I, and we ve already dealt with that case. So, we can assume det(i A) 0. This means that I A is invertible, so equation ( ) has the unique solution p = (I A) 1 w. Now that we know the fixed point p exists, we can write (A w) = (A p Ap). Then we have (A p Ap)(x) = Ax + p Ap = A(x p) + p. If we think about this geometrically, this shows that (A p Ap) is rotation about the point p: We take the vector x p that points from p to x, take the arrow at the origin, rotate the arrow by A and move the arrow back to p. This gives us the rotation of x about p. Consider the case (A w) where A is a reflection matrix. Recall that for a reflection matrix, there are orthogonal unit vectors u and v so that Au = u and Av = v. Consider first the case where w = sv. Let p = sv/2. We claim that p is

Part 2. 21 Part 2. 2 fixed. (A sv)(p) = Ap + sv so all the points on L are fixed. We can also write = A(sv/2) + sv = (s/2)av + sv = (s/2)( v) + sv = (s s/2)v = sv/2 = p. Thus, sv = 2p and we can write (A w) = (A 2p). Note Ap = p. As t runs over all real numbers, the point p + tu run along the line, call it L, that passes through p and is parallel to u. For points on L, we have (A 2p)(p + tu) = A(p + tu) + 2p = Ap + tu + 2p = p + tu + 2p = p + tu, (A 2p)(x) = Ax + 2p = Ax + p + p = Ax ( p) + p = Ax Ap + p = A(x p) + p. By the same reasoning as before, this shows that (A 2p) is reflection through L. Exercise: Show that (A w) has fixed points if and only if w = sv. Consider finally the case (A w) where w is not a multiple of v. Then we can write w = αu + βv. We then have (A w) = (A αu + βv) = (I αu)(a βv)

Part 2. 23 We know that (A βv) is reflection about some line parallel to u and (I αu) is translation in a direction parallel to u, so (A w) is a glide reflection. A glide reflection has no fixed points.