Inner products on R n, and more


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1 Inner products on R n, and more Peyam Ryan Tabrizian Friday, April 12th, Introduction You might be wondering: Are there inner products on R n that are not the usual dot product x y = x 1 y x n y n? The answer is yes and no. For example, the following are inner products on R 2 : < x, y >= 2x 1 y 1 + 3x 2 y 2 < x, y >= x 1 y 1 + 2x 1 y 2 + 2x 2 y 1 + 3x 2 y 2 But the answer is: There are no fancier examples! In fact, this result is even true for finitedimensional vector spaces over F! Note: In the following, we will denote vectors in R n and C n by column vectors, not [ rowvectors 1. For example, instead of writing x = (1, 2) 2, we will 1 write x =. 2] 2 Inner products on R n In this section, we will prove the following result: Prop: < x, y > is an inner product on R n if and only if < x, y >= x T Ay, where A is a symmetric matrix whose eigenvalues are strictly positive 3 1 This will simplify matters later on 2 Here we mean the point, not the dot product 3 Such a matrix is called symmetric and positivedefinite 1
2 Example 1: For example, if n = 2, and A = [ ] 1 2, we get: 2 3 < x, y >= x 1 y 1 + 2x 1 y 2 + 2x 2 y 1 + 3x 2 y 2 Example 2: If A = I, then < x, y > just becomes the usual dot product! The point is that those are the fanciest examples we can get! Note: From now on, let us denote by (e 1,, e n ) the standard basis of R n. Proof: ( ) Suppose <, > is an inner product of R n, and let x, y R n. Then since (e 1,, e n ) is a basis for R n, there are scalars x 1,, x n and y 1,, y n such that: x = x 1 e x n e n y = y 1 e y n e n < x, y >= < x 1 e x n e n, y 1 e y n e n > n = x i y j < e i, e j > = i,j=1 n x i a ij y j i,j=1 Where we define a ij = < e i, e j >. Also, in the second line we used a distributive property similar to (a + b)(c + d) = ac + ad + bc + cd (but for n terms) Now if you let A to be the matrix whose (i, j)th entry is a ij, then the above becomes: < x, y >= x T Ay Moreover, a ij =< e i, e j >=< e j, e i >= a ji, so A is symmetric. And since A is symmetric, by a theorem in chapter 7, we know that A has n (possibly repeated) eigenvalues λ 1,, λ n. Let v 1,, v n be the corresponding eigenvectors, which are all nonzero. < v i, v i >= v T i Av i = v T i λ i v i = λ i v T i v i 2
3 However, < v i, v i >, hence λ i v T i v i > 0, but since v T i v i > 0 (since v T i v i is the usual dot product between v i and v i ), this implies λ i > 0, so all the eigenvalues of A are positive ( ) Suppose < x, y >= x T Ay. Then you can check that <, > is linear in each variable. Moreover: < y, x >= y T Ax = (x T A T y) T = (x T Ay) T = x T Ay = < x, y > Where the third equality follows from A T = A and the last equality follows because x T Ay is just a scalar. Finally: < x, x >= x T Ax Now since A is symmetric, A is normal (you will see that later), and hence there exists an invertible matrix P with P 1 = P T, such that A = P DP T (you will learn that later too, i.e. A is orthogonally diagonalizable), where D is the diagonal matrix of eigenvalues λ i of A, and by assumption λ i > 0 for all i. < x, x >= x T Ax = x T P DP T x = (P T x) T DP T x = ydy T Where y = P T x. But then if y = a 1 y a n y n and you calculate this out, you should get: < x, x >= ydy T = λ 1 a λ n a 2 n 0 (since λ i > 0), So < x, x > 0. Moreover, if < x, x >= 0, then λ 1 a λ n a 2 n = 0, but then a 1 = = a n = 0 (since λ i > 0), but then y = 0, and so x = (P T ) 1 y = (P T ) T y = P y = P 0 = 0. Hence <, > satisfies all the requirements for an inner product, hence (, ) is an inner product! 3
4 3 Inner products on C n In fact, a similar proof works for C n, except that you have to replace all the transposes by adjoints! (i.e. replace all the T with ). Hence, we get the following result: Prop: < x, y > is an inner product on C n if and only if < x, y >= x Ay, where A is a selfadjoint matrix whose eigenvalues are strictly positive 4 4 Inner products on finitedimensional vector spaces In fact, if V is a finitedimensional vector space over F, then a version of the above result still holds, using the following trick: Let n = dim(v ) and (v 1,, v n ) be a basis for V. Here, we will prove the following result gives an explicit description of all inner products on V : Theorem: < x, y > is an inner product on V if and only if: < x, y >= (Mx) AM(y) where A is a selfadjoint matrix with positive eigenvalues 5, where M : V F n is the usual coordinate map given by: a 2 M(v) = M(a 1 v a n v n ) =. a n a 1 Example: If V = P 2 (R), then the following is an inner product on V : < a 0 +a 1 x+a 2 x 2, b 0 +b 1 x+b 2 x 2 >= a 0 b 0 +2a 0 b 1 +3a 0 b 2 +2a 1 b 0 +2a 1 b 1 +4a 1 b 2 +3a 2 b 0 +4a 2 b 1 +8a 2 b Here A = Note that A selfadjoint implies that A has only real eigenvalues 5 If V is a vector space over R, then replace selfadjoint with symmetric and with T 4
5 Proof: ( ): Check that < x, y > is an inner product on V (this is similar to the proof in section 2) ( ): First of all, note from chapter 3 that M is invertible, with inverse: M 1 a 1. a n Now let <, > be an inner product on V. = a 1 v a n v n Lemma: Then (x, y ) :=< M 1 (x ), M 1 (y ) > is an inner product on F n Proof: The only tricky thing to prove is that (x, x ) = 0 implies x = 0. However: (x, x ) = 0 < M 1 (x ), M 1 (x ) >= 0 M 1 (x ) = 0 x = 0 Where in the second implication, we used that <, > is an inner product on V, and in the third implication, we used that M 1 is injective. But since (x, y ) is an inner product on F n, by sections 2 and 3, we get that: (x, y ) = (x ) Ay For some selfadjoint (or symmetric) matrix A with only positive eigenvalues. But then it follows that < M 1 (x ), M 1 (y ) >= (x ) Ay Now let x, y be arbitrary vectors in V. Then we can write x = M 1 M(x) and y = M 1 M(y) < x, y >=< M 1 M(x), M 1 M(y) >= M(x) AM(y) Where in the second equality, we used the above result with x = M(x) and y = M(y). 5
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