Kevin James. MTHSC 3110 Section 2.1 Matrix Operations

Transcription

1 MTHSC 3110 Section 2.1 Matrix Operations

2 Notation Let A be an m n matrix, that is, m rows and n columns. We ll refer to the entries of A by their row and column indices. The entry in the i th row and j th column is denoted by a ij, and is called the (i, j)-entry of A. a a 1j... a 1n... a i1... a ij... a in... a m1... a mj... a mn

3 Notation The columns of A are vectors in R m, and are denoted in the book by a 1, a 2,..., a n and in my notes by a 1, a 2,..., a n. In order to focus attention on the columns we write A = [ a 1 a 2... a n ]

4 Definition Suppose that A = [ a 1 a 2... a n ] and B = [ b 1 b2... bn ] are both m n matrices. Then we define their sum as A + B = [ a 1 a 2... a n ] + [ b 1 b2... bn ] = [( a 1 + b 1 ) ( a 2 + b 2 )... ( a n + b n )] Alternative Definition Given two m n matrices A and B, we can define their sum C = A + B as the m n matrix whose entries are c ij = a ij + b ij.

5 Example =

6 Definition We denote by 0 the matrix all of whose elements are zero. Theorem Let A, B, C be matrices of the same size, and let r, s be scalars. Then 1 A + B = B + A 2 (A + B) + C = A + (B + C) 3 A + 0 = A 4 r(a + B) = ra + rb 5 (r + s)a = ra + sa 6 r(sa) = (rs)a Note This is very similar to the corresponding theorem for vector addition.

7 Composition of Functions Recall If f and g are functions and the image of g is contained in the domain of f, then we define the composition of f and g by Example f g(x) = f (g(x)) Suppose that f, g : R R are defined by f (y) = y 2 and g(x) = sin(x), then f g(x) = f (g(x)) = (sin(x)) 2

8 Composition of Linear Transformations Note 1 We will specialize our attention to linear transformations from R n to R m. 2 We have seen that a linear transformation T : R n R m corresponds to multiplying a vector x R n by an m n matrix A. 3 We will consider linear transformations R p U R n T R m, where U : R p R n, and T : R n R m. So, since U( x) is in the domain of T, we can compute T (U( x)).

9 Fact The composition of two linear transformations is again a linear transformation and thus can be written as multiplication by a matrix.

10 Matrix Multiplication Suppose that R p U R n T R m. Let B be the n p matrix corresponding to the transformation U. Let A be the m n matrix corresponding to the transformation T. (-i.e. U( x) = B x, and T ( v) = A v). Then we have T U( x) = T (U( x)) = A(B x) = A(x 1 b1 + x 2 b2 + + x p bp ) = Ax 1 b1 + Ax 2 b2 + + Ax p bp = x 1 A b 1 + x 2 A b x p A b p = [A b 1 A b 2... A b p ] x Thus, T U( x) = [A b 1 A b 2... A b p ] x.

11 Matrix Multiplication Defined Definition If A is an m n matrix, and if B = [ b 1, b 2..., b p ] is a n p matrix, then the matrix product AB is the following m p matrix. AB = [A b 1 A b 2... A b p ] Example ( ) 1 2 Let A = and let B = 2 3 ( ). Compute AB.

12 Multiplying Matrices Row-Column Rule If A is m n and if B is n p the (i, j)-entry of AB is given by (AB) ij = Note Row i (AB) = Row i (A) B. n a ik b kj k=1

13 Definition We define the m m identity matrix as I m = [ e 1, e 2,... e m ] = Theorem With A, B and C appropriately sized matrices and r a scalar 1 (AB)C = A(BC) 2 A(B + C) = AB + AC 3 (B + C)A = BA + CA 4 r(ab) = (ra)b = A(rB) 5 I m A = A = AI n

14 Properties Not held by Matrix Multiplication Example ( ) 1 2 Let A = ( ) 2 6 Let B =. 1 3 Compute AB, BA, and A 0. Note 1 It is NOT in general true that AB = BA. 2 It is NOT in general true when AC = AB that C = B.

15 Exponents Definition We define non negative powers of an m m matrix as follows. A 0 = I m, A 1 = A, A n = A n 1 A for n > 1. Example ( 1 2 Let A = 0 1 ). Compute A 3.

16 The Transpose of a Matrix Definition The transpose of a m n matrix A is the matrix A T having (i, j)-entry a ji. That is, Example For example, A = Note ( (A T ) ij = a ji. ) has transpose A T = The rows of A become the columns of A T and vice versa

17 Theorem Let A and B be matrices whose sizes are appropriate for the following sums and products to be defined 1 (A T ) T = A 2 (A + B) T = A T + B T. 3 For any scalar r, (ra) T = ra T. 4 (AB) T = B T A T

18 Example ( ) 1 2 A =, and B = 3 4 AB = ( 7 5 ) ( ) then (AB) T = 5 11 = B T A T 3 5 but A T is 2 2 and B T is 3 2, so A T B T isn t even defined.