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

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1 Lectures notes on orthogonal matrices (with exercises) 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 matrix if or, equivalently, if A T = A 1. If we view the matrix A as a family of column vectors: A = A T A = I, (1) A 1 A 2 A n then A T A = A T 1 A T 2. A 1 A 2 A 3 = A T 1 A 1 A T 1 A 2 A T 1 A n A T 2 A 1 A T 2 A 2 A T 2 A n A T n A T n A 1 A T n A 2 A T n A n So the condition (1) asserts that A is an orthogonal matrix iff A T i A j = { 1 if i = j 0 if i j that is, iff the columns of A form an orthonormal set of vectors. Orthogonal matrices are also characterized by the following theorem. Theorem 1 Suppose that A is an n n matrix. The following statements are equivalent: 1. A is an orthogonal matrix. 2. AX = X for all X R n. 3. AX AY = X Y for all X, Y R n. In other words, a matrix A is orthogonal iff A preserves distances and iff A preserves dot products. Proof: We will prove that

2 1. 2. : Suppose that A is orthogonal, so that A T A = I. For all column vectors X R n, we have so that AX = X. AX 2 = (AX) T AX = X T A T AX = X T IX = X T X = X 2, : Suppose that A is a square matrix such that AX = X for all X R n. Then, for all X, Y R n, we have X + Y 2 = (X + Y ) T (X + Y ) = X T X + 2X T Y + Y T Y = X 2 + 2X T Y + Y 2 and similarly, A(X + Y ) 2 = AX + AY 2 = (AX + AY ) T (AX + AY ) = AX 2 + 2(AX) T AY + AY 2. Since AX = X and AY = Y and A(X +Y ) = X +Y, it follows that (AX) T AY = X T Y. In other words, AX AY = X Y : Suppose that A is a square matrix such that AX AY = X Y for all X, Y R n Let e i denote the i-th standard basis vector for R n, and let A i denote the i-th column of A, as above. Then { A T i A j = (Ae i ) T 1 if i = j Ae j = Ae i Ae j = e i e j = 0 if i j so that the columns of A are an orthonormal set, and A is an orthogonal matrix. We conclude this section by observing two useful properties of orthogonal matrices. Proposition 2 Suppose that A and B are orthogonal matrices. 1. AB is an orthogonal matrix. 2. Either det(a) = 1 or det(a) = 1. The proof is left to the exercises. Note: The converse is false. There exist matrices with determinant ±1 that are not orthogonal. 2. The n-reflections Theorem Recall that if u R n is a unit vector and W = u then H = I 2uu T is the reflection matrix for the subspace W. Since reflections preserve distances, it follows from Theorem 1 that H must be an orthogonal matrix. (You can also verify condition (1) directly.) We also showed earlier in the course that H = H 1 = H T and H 2 = I. It turns out that every orthogonal matrix can be expressed as a product of reflection matrices. 2

3 Theorem 3 (n-reflections Theorem) Let A be an n n orthogonal matrix. There exist n n reflection matrices H 1, H 2,..., H k such that A = H 1 H 2 H k, where 0 k n. In other words, every n n orthogonal matrix can be expressed as a product of at most n reflections. Proof: The theorem is trivial in dimension 1. Assume it holds in dimension n 1. For the n-dimensional case, let z = Ae n, and let H be a reflection of R n that exchanges z and e n. Then HAe n = Hz = e n, so HA fixes e n. Moreover, HA is also an orthogonal matrix by Proposition 2, so HA preserves distances and angles. In particular, if we view R n 1 as the hyperplane e n, then HA must map R n 1 to itself. By the induction assumption, HA must be expressible as a product of at most n 1 reflections on R n 1, which extend (along the e n direction) to reflections of R n as well. In other words, either HA = I or HA = H 2 H k, where k n. Setting H 1 = H, and keeping in mind that HH = I (since H is a reflection!), we have A = HHA = H 1 H 2 H k. Proposition 4 If H is a reflection matrix, then det H = 1. Proof: See Exercises. Corollary 5 If A is an orthogonal matrix and A = H 1 H 2 H k, then det A = ( 1) k. So an orthogonal matrix A has determinant equal to +1 iff A is a product of an even number of reflections. 3. Classifying 2 2 Orthogonal Matrices Suppose that A is a 2 2 orthogonal matrix. We know from the first section that the columns of A are unit vectors and that the two columns are perpendicular (orthonormal!). Any unit vector u in the plane R 2 lies on the unit circle centered at the origin, and so can be expressed in the form u = (cos θ, sin θ) for some angle θ. So we can describe the first column of A as follows: cos θ?? A = sin θ?? What are the possibilities for the second column? Since the second column must be a unit vector perpendicular to the first column, there remain only two choices: cos θ sin θ cos θ sin θ A = or A = sin θ cos θ sin θ cos θ 3

4 The first case is the rotation matrix, which rotates R 2 counterclockwise around the origin by the angle θ. The second case is a reflection across the line that makes an angle θ/2 from the x-axis (counterclockwise). But which is which? You can check that the rotation matrix (on the left) has determinant 1, while the reflection matrix (on the right) has determinant -1. This is consistent with Proposition 4 and Corollary 5, since a rotation of R 2 can always be expressed as a product of two reflections (how?). 4. Classifying 3 3 Orthogonal Matrices The n-reflection Theorem 3 leads to a complete description of the 3 orthogonal matrices. In particular, a 3 3 orthogonal matrix must a product of 0, 1, 2, or 3 reflections. Theorem 6 Let A be a 3 3 orthogonal matrix. 1. If det A = 1 then A is a rotation matrix. 2. If det A = 1 and A T = A, then either A = I or A is a reflection matrix. 3. If det A = 1 and A T A, then A is a product of 3 reflections (that is, A is a non-trivial rotation followed by a reflection). Proof: By Theorem 3, A is a product of 0, 1, 2, or 3 reflections. Note that if A = H 1 H k, then det A = ( 1) k. If det A = 1 then A must be a product of an even number of reflections, either 0 reflections (so that A = I, the trivial rotation), or 2 reflections, so that A is a rotation. If det A = 1 then A must be a product of an odd number of reflections, either 1 or 3. If A is a single reflection then A = H for some Householder matrix H. In this case we observed earlier that H T = H so A T = A. Conversely, if det A = 1 and A T = A then det( A) = 1 (since A is a 3 3 matrix) and A T = A = A 1 as well. It follows that A is a rotation that squares to the identity. If A I, then the only time this happens is when we rotate by the angle π (that is, 180 o ) around some axis. But if A is a 180 o rotation around some axis, then A = ( A) must be the reflection across the equatorial plane for that axis (draw a picture!). So A is a single reflection. Finally if det A = 1 and A T A, then A cannot be a rotation or a pure reflection, so A must be a product of at least 3 reflections. Corollary 7 Let A be a 3 3 orthogonal matrix. 1. If det A = 1 then A is a rotation matrix. 2. If det A = 1 then A is a rotation matrix. 4

5 Proof: If det A = 1 then A is a rotation matrix, by Theorem 6. If det A = 1 then det( A) = ( 1) 3 det A = 1. Since A is also orthogonal, A must be a rotation. Corollary 8 Suppose that A and B are 3 3 rotation matrices. Then AB is also a rotation matrix. Proof: If A and B are 3 3 rotation matrices, then A and B are both orthogonal with determinant +1. It follows that AB is orthogonal, and det AB = det A det B = 1 1 = 1. Theorem 6 then implies that AB is also a rotation matrix. Note that the rotations represented by A, B, and AB may each have completely different angles and axes of rotation! Given two rotations A and B around two different axes of rotation, it is far from obvious that AB will also be a rotation (around some mysterious third axis). But this is true, by Corollary 8. Later on we will see how to compute precisely the angle and axis of rotation of a rotation matrix. Exercises: 1. (a) Suppose that A is an orthogonal matrix. Prove that either det A = 1 or det A = 1. (b) Find a 2 2 matrix A such that det A = 1, but also such that A is not an orthogonal matrix. 2. Suppose that A and B are orthogonal matrices. Prove that AB is an orthogonal matrix. 3. Suppose that H = I 2uu T is a reflection matrix. Let v 1,..., v n 1 be an orthonormal basis for the subspace u. (Here u denotes the orthogonal complement to the line spanned by u.) (a) What is Hu =? (b) What is Hv i =? (c) Let M be the matrix with columns as follows M = v 1 v n 1 u What is HM =? What are the columns of HM? (d) By comparing det HM to det M prove that det H = (a) Give a geometric description (and sketch) of the reflection performed by the matrix cos θ sin θ H 1 = sin θ cos θ 5

6 (b) Give a geometric description (and sketch) of the reflection performed by the matrix H 2 = [ ] (c) Show that a rotation in R 2 is a product of two reflections by showing that H 1 H 2 is a rotation matrix. Give a geometric description (and sketch) of the rotation performed by the matrix H 1 H Let u, v, w be an orthonormal basis (of column vectors) for R 3, and let A be the matrix given by A = uu T + vv T + ww T. (a) What is Au =? What is Av =? What is Aw =? (b) Let P be the matrix P = u v w What is AP =? (c) Prove that A = I (the identity!). 6. Give geometric descriptions of the what happens when you multiply a vector in R 3 by each of the following orthogonal matrices: cos θ sin θ 0 cos θ 0 sin θ sin θ cos θ cos θ sin θ sin θ 0 cos θ 0 sin θ cos θ Sketch what happens in each case. 6

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