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REVIEW
OF
MATHEMATICS
Review of Vectors Analysis
T
T
a
[
a
,
a
,
a
]
,
b
[
b
,
b
,
b
]
Given
x
y
z
x
y
z
T
Dot product: a b a b axbx a y by az bz a b cos( )
is the angle between the two vectors.
a
[1,3,5]T
Example:
a b 1(2) 3(4) 5(6) 44
T
b [2,4,6]
a 35
44
1
cos
b 56
35 56
2
2
2
a
a
a
a
a
a
Magnitude of vector:
x
y
z
Example: a [1,2,3]T a 12 22 32 14
Review of Vectors Analysis
Vectors a and b are said to be perpendicular or
orthogonal if
a b 0
Example:
T
T
a 1 0 0 ,
b 0 1 0
a b 0 a b
Note that the above vectors represent the unit vectors for the Xaxis and Y-axis. They are definitely perpendicular or orthogonal.
Review of Vectors Analysis
Cross product: a b a y bz a z by a z bx a x bz
eˆ1
a b det a x
bx
a b a b
is the angle between the two vectors.
eˆ2
ay
by
a b
x y
a y bx
eˆ3
az
bz
sin( )
Example:
T
a [1,3,5] a b 3(6) 5(4) eˆ1 5(2) 1(6) eˆ2 1(4) 3(2) eˆ3
T
T
b [2,4,6]
(2) (4) 2
a 35
24
1
b 56 sin
35 56
a b 24
Review of Vectors Analysis
The cross product of a and b provides
us with a vector
which is perpendicular to both a and b
T
T
Example:
a 1 0 0 ,
b 0 1 0
iˆ ˆj kˆ
0
a b det 1 0 0 0iˆ (0) ˆj (1)kˆ 0
0 1 0
1
Note that the above vectors represent the unit vectors for the Xaxis and Y-axis respectively. Their cross product is the unit
vector for the Z-axis, which is definitely perpendicular to both
the X-axis and the Y-axis.
Review of Vectors Analysis
Note that the unit vectors for the right handed Cartesian reference
frame are orthonormal basis vectors, i.e.
iˆ ˆj kˆ,
ˆj kˆ iˆ
kˆ iˆ ˆj
iˆ ˆj ˆj kˆ kˆ iˆ 0
iˆ ˆj kˆ 1
a b a b cos( ) 0 2n ,
n 1,2,
2
a b a b sin( ) 0 2n ,
n 0,1,2,
Review of Vectors Analysis
Vector triple product: a b c b a c c a b
Example:
T
a 0 1 0
0 0
T
b 1 0 0 a b c 1 1 0 0 0
T
c 0 0 1
0 0
T
b a c 1 0 0 (0 0 0) 0 0 0
T
c a b 0 0 1 (0 0 0) 0 0 0
Review of Vectors Analysis
Scalar triple product: ab c a b c a b c
a x
det bx
c x
ay
by
cy
az
bz
c z
Example:
T
a 0 1 0
0 1 0
T
b 1 0 0 ab c det 1 0 0 0 1(1 0) 0 1
T
c 0 0 1
0 0 1
T
T
a b c 0 1 0 0 1 0 1
a b c 0 0 1T 0 0 1T 1
Review of Vectors Analysis
d
a a x
dt
T
a
[
a
,
a
,
a
]
Given
x
y
z
Example:
Given
T
z
T
2
a t t 2t 1
d
T
a 2t 1 2
dt
d
(a ) a x
dt
a [ax , a y , a z ]T
where is a any constant
Example:
a y
a
a t 2 t
d
3a 32t
dt
2t 1
T
1 2
T
a y
a z
T
Review of Vectors Analysis
T
T
a
[
a
,
a
,
a
]
,
b
[
b
,
b
,
b
]
x
y
z
x
y
z
Given
d
a b a b a b
dt
Example:
2
2
2
t
2
t
3
t
T
2
a t t 2t 1 d
t 0
a
b
1
1
T
b 2t 3 1 t dt
2 t 2t 1 1
d
a b {(4t 2 6t ) 1 (2t )} {2t 2 2t 1} 6t 2 10t 2
dt
Review of Vectors Analysis
T
T
a
[
a
,
a
,
a
]
,
b
[
b
,
b
,
b
]
Given
x
y
z
x
y
z
d
a b a b a b
dt
Example:
5t 2
10t
sin( t )
cos(t )
2
3 T
d
a 5t t t
1 cos(t ) t sin( t )
a
b
T
b sin( t ) cos(t ) 0 dt
2
3
3t 0 t 0
ˆj
ˆj
iˆ
iˆ
kˆ
kˆ
d
a b det 10t
1
3t 2 det 5t 2
t
t3
dt
sin( t ) cos(t )
cos(t ) sin( t ) 0
0
3t 2 cos(t )
t 3 sin( t )
d
3
a b
3t 2 sin( t )
t
cos(
t
)
dt
2
{10t cos(t ) sin( t )} 5t sin( t ) t cos(t )
t 3 sin( t ) 3t 2 cos(t )
d
a b {t 3 cos(t ) 3t 2 sin( t )}
dt
5t 2 sin( t ) 11t cos(t ) sin( t )
Review of Vectors Analysis
d
Ab A b Ab
dt
where A is a matrix of dimension comparable to the vector
being multiplied
Given b [bx , by , bz ]T
Example:
b t2
d
T
t 1 b 2t 1 0
dt
2t 1 0
2 0 0
d
A 0 t 3 A 0 1 0
dt
2 0 3t
0 0 3
2
2
2
2
2 0 0 t 2t 1 0 2t 2t 4t 1 6t 1
d
Ab 0 1 0 t 0 t 3 1 t t 2t
dt
0 0 3 1 2 0 3t 0 3 4t 4t 3
T
Eigenvalues and Eigenvectors
Let A be an nn matrix. If there exists a and a nonzero n1 vector x
such that
Ax x
then is called an eigenvalue of A and x is called an eigenvector
of A corresponding to the eigenvalue
Let In be a nn identity matrix. The eigenvalues of nn matrix A
can be obtained from:
det( A I n ) 0
A nn matrix A has at least one and at most “n” distinct eigenvalues
Example 1: Eigenvalues and Eigenvectors
Find the eigenvalues of
Solution:
1 2 3
A 0 4 2
0 0 7
2
3
1 2 3
1 0 0 1
A I n 0 4 2 0 1 0 0
4
2
0
7
0 0 7
0 0 1 0
2
3
1
det( A I n ) 0 det 0
4
2 0
0
7
0
1 4 7 0
1, 4, 7
Example 2: Eigenvalues and Eigenvectors
What is the eigenvector of
4 36 33 at =1?
1
A
48
9
4
49
9 32 36
Ax x A I x 0
4 36 33 1 0
1
48
9
4 10 1
49
9 32 36 0 0
36
(4 49)
1
48
(9 49)
49
32
9
0 x1 0
0 x2 0
1 x3 0
33 x1 0
x 0
4
2
(36 49) x3 0
45 36 33 x1 0
1
48 40 4 x2 0
49
32 13 x3 0
9
Example 2: Eigenvalues and Eigenvectors
45 x1 36 x2 33x3 0
48 x1 40 x2 4 x3 0
9 x1 32 x2 13x3 0
Multiply 3rd eqn by -5 and add it to 1st eqn to eliminate x1
45 x1 36 x2 33x3 0
45 x1 160 x2 65 x3 0
196 x2 98 x3 0
x3
2
x2
Example 2: Eigenvalues and Eigenvectors
45 x1 36 x2 33x3 0
48 x1 40 x2 4 x3 0
9 x1 32 x2 13x3 0
Divide 2nd eqn by x2 and simplify using the known result:
48
x
x1
40 4 3 0
x2
x2
x1
48 40 4(2) 0
x2
x1 32 2
x2 48 3
Example 2: Eigenvalues and Eigenvectors
Story so far:
x3
2,
x2
x x1
x2
x1 2
x2 3
x3
T
2
x2 1 2
3
T
We can obtain a normalized eigenvector using:
2 1 2
x
2
x
3
xn
2
x
2
x2 12 2 2
3
T
1
3 2
T
xn 1 2 2 3 6
7 3
7
Trigonometric Functions
sin 2 ( ) cos 2 ( ) 1
sin( ) sin( )
cos( ) cos( )
tan( ) tan( )
sin(1 2 ) sin(1 ) cos( 2 ) cos(1 ) sin( 2 )
cos(1 2 ) cos(1 ) cos( 2 ) sin(1 ) sin( 2 )
tan(1 ) tan( 2 )
tan(1 2 )
1 tan(1 ) tan( 2 )
Trigonometric Functions
sin( )
tan( )
cos( )
d
sin( ) cos( )
d
d
cos( ) sin( )
d
d
d
sin( ) cos( ) cos( )
dt
dt
d
d
cos( ) sin( ) sin( )
dt
dt
sin( )d cos( )
cos( )d sin( )