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A portion of lecture notes from a university course, math348 - advanced engineering mathematics, focusing on the algebra of matrices and vectors. It covers definitions of matrices, vectors, scalar, equality, addition, scalar multiplication, transposition, conjugation, adjoint, and matrix multiplication. The document also mentions the rules for matrix addition and scalar multiplication, as well as the properties of matrix products.
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E. Kreyszig, Advanced Engineering Mathematics, 8
th ed. Section 7.1, pgs. 272-
Suggested Problem Set: Suggested Problems : {5, 7 } August 25, 2008
E. Kreyszig, Advanced Engineering Mathematics, 8
th ed. Section 7.2, pgs. 278-
Suggested Problem Set: Suggested Problems : {3, 5, 8, 13, 19, 20, 22 } August 25, 2008
Quote of Lecture 1
Remember where the thought is. I brought all this so you can survive when law is lawless.
Gorillaz: Clint Eastwood (2001)
We begin the course with the algebra of matrices. To do this we must do the following:
once this is completed we can begin to study how this algebra relates to linear systems and how we should
think about solutions to linear problems. You have studied linear equations before in the sense that you
have asked the question, for what values of x does the equation,
ax = b, a, b ∈ R, (1)
have a solution? It should be straightforward to see that for a 6 = 0 the problem has the unique solution,
x =
b
a
, a 6 = 0. (2)
What about the case where a = 0? Well, that is trickier. If b = 0 then the value of x doesn’t matter. We
always have equality, there are infinitely many choice for x! However, in the case where b 6 = 0 we have the
inconsistent equation, 0 cot x = b 6 = 0. There are no values of x, which will satisfy the equation.
However, we are getting ahead of ourselves. The question we need to ask now is given a lot of data how
can we systematically organize it? Also, once this is done how can we manipulate groups of them? Without
answers to these questions we have no hope of setting up equations (1)-(2) for large data. In what follows
will record the definitions of matrices and their algebraic structure.
1
1 From the perspective of abstract algebra, a field that studies collections of objects and their properties with respect
to binary operators, we would call this sort of structure a non-commutative ring.
Definition: Matrix - A matrix is a set of objects organized by two indices into a rectangular array. In the
case that these objects exist in the set of complex numbers we write A∈ C
m×n , where n, m ∈ N. At the
element level we have that:
a 11 a 12 a 13... a 1 n
a 21 a 22 a 23... a 2 n
a 31 a 32 a 33... a 3 n
am 1 am 2 am 3... amn
, where [A] ij
= aij , aij ∈ C, for i = 1, 2 , 3 , · · · , m and j = 1, 2 , 3 , · · · , n. (3)
diagonal entries.
Definition: Vector - A column vector, or just vector, is matrix of size, m × 1 where m ∈ N. A row vector
is matrix of size, 1 × n where n ∈ N and if v ∈ C
m× 1 or r ∈ C
1 ×n and at the element level we have that:
v =
v 1
v 2
v 3
vm
, where vi ∈ C for i = 1, 2 , 3 , · · · m. (4)
r =
h
r 1 r 2 r 3... rn
i
, where rj ∈ C, for j = 1, 2 , 3 , · · · n. (5)
Definition: Scalar - A scalar is a matrix whose size is 1 × 1. That is, a scalar is an element of the complex
number system, s ∈ C.
Defintion: Equality of Matrices - Two matrices A, B ∈ C
m×n are said to be equal if and only if aij = bij
for i = 1, 2 , 3 ,... , m and j = 1, 2 , 3 ,... , n.
Defintion: Addition and Scalar Multiplication of Matrices - Let A ∈ C
m×n and B∈ C
m×n then A + B = C
is defined such that C∈ C
m×n where cij = aij + bij for i = 1, 2 , 3 ,... , m and j = 1, 2 , 3 ,... , n. Also, let
s ∈ C, then sA = C, where cij = s · aij for i = 1, 2 , 3 ,... , m and j = 1, 2 , 3 ,... , n. Following from these
definitions we have the rules for matrix addition and scalar multiplication as:
where A,B,C∈ C
m×n and r, s ∈ C