Lecture 4 chapter 1 review section 2-1

2
2.1
© 2012 Pearson Education, Inc.
MATH 337
Lecture 4

1- 2© 2012 Pearson Education, Inc.
MATRIX Notation
 A is an mxn matrix
 m rows
 n columns
 scalar entry in the ith row and jth column of A is denoted
by aij and is called the (i, j)-entry of A.
 Each column of A is a list of m real numbers, which
identifies a vector in Rm
.

MATRIX Notation
 The columns are denoted by a1, …, an, and the matrix A
is written as
A = [a1a2 … an]
 The number aij is the ith entry (from the top) of the jth
column vector aj.
 Diagonal entries in an mxn matrix are
a11, a22, a33, …, and they form the main diagonal of A.
 A diagonal matrix is a square nxn matrix whose
nondiagonal entries are zero.
 e.g., In

Zero Matrix
 zero matrix
 All entries are zero
 written as 0
 Does not have to be square

Equal Matrices
 matrices are equal if
 same size (i.e., the same #of rows & columns)
 corresponding entries equal.
Slide 2.1- 5© 2012 Pearson Education, Inc.

Adding Matrices
 If A and B are mxn matrices, then the sum A + B is
the mxn matrix whose entries are the sums of the
corresponding entries in A and B.
 The sum A + B is defined only when A and B are the
same size.
 Example 1: Let
and . Find A + B and A + C.
4 0 5 1 1 1
, ,
1 3 2 3 5 7
A B
   
= =   −   
2 3
0 1
C
− 
=  
 

SCALAR MULTIPLES
 scalar multiple rA is the matrix whose entries are r
times the corresponding entries in A.

Algebraic Properties of Matrices
 Theorem 1: Let A, B, and C be matrices of the
same size, and let r and s be scalars.
a. A + B = B + A
b. (A + B) + C = A + (B + C)
c. A + 0 = A
d. r(A + B) = rA + rB
e. (r + s)A = rA + sA
f. r(sA) = (rs)A

Matrix Multiplication - Definition
 Definition: If A is an mxn matrix, and if B is
an nxp matrix with columns b1, …, bp, then the
product AB is the mxp matrix whose columns
are Ab1, …, Abp.
 That is,
 Multiplication of matrices corresponds to
composition of linear transformations. Slide 2.1- 9© 2012 Pearson Education, Inc.
1 2 1 2
b b b b b bpp
AB A A A A   = =   L L

MATRIX MULTIPLICATION – Row-Column Rule

PROPERTIES OF MATRIX MULTIPLICATION
Theorem 2: Let A be an mxn matrix, and let B and C
have sizes for which the indicated sums and products
are defined.
a. (associative law of
multiplication)
b. (left distributive law)
c. (right distributive law)
d. for any scalar r
e. (identity for matrix
multiplication)
( ) ( )A BC AB C=
( )A B C AB AC+ = +
( )B C A BA CA+ = +
( ) ( ) ( )r AB rA B A rB= =
m n
I A A AI= =

PROPERTIES OF MATRIX MULTIPLICATION
 If , we say that A and B commute with
one another.
 Warnings:
1. In general, .
2. The cancellation laws do not hold for matrix
multiplication. That is, if , then it is
not true in general that .
3. If a product AB is the zero matrix, you cannot
conclude in general that either or .
AB BA=
AB BA≠
AB AC=
B C=
0A = 0B =

POWERS OF A MATRIX
 If A is an matrix and if k is a positive integer,
then Ak
denotes the product of k copies of A:
 If A is nonzero and if x is in Rn
then Ak
x is the result
of left-multiplying x by A repeatedly k times.
 If , then A0
x should be x itself.
 Thus A0
is interpreted as the identity matrix.
n n×
{
k
k
A A A= L
0k =

THE TRANSPOSE OF A MATRIX
 Given an matrix A, the transpose of A is the
matrix, denoted by AT
, whose columns are
formed from the corresponding rows of A.
m n×
n m×

Properties of Transposes
Theorem 3: Let A and B denote matrices whose sizes
are appropriate for the following sums & products.
a. (AT
)T
= A
b. (A + B)T
= AT
+ BT
c. For any scalar r, (rA)T
= rAT
d. (AB)T
= BT
AT
 The transpose of a product of matrices equals the
product of their transposes in the reverse order.

Lecture 4 chapter 1 review section 2-1

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Lecture 4 chapter 1 review section 2-1