# Linear Algebra Basic Information

```MAT 188-WEEK 1 Sets, Vectors, Lines, Planes
(c)2022 C. Karimianpour
Learning Objectives:
•
•
•
•
Familiarity with set notation
Geometric understanding of vectors in two and three dimensions
Fluency in performing vector algebra and interpreting it geometrically
2
3
Geometric and Algebraic understanding of lines and planes in R and R .
Vocabulary:
set, vector, vector addition and scalar multiplication, dot product, orthogonal vectors,
parallel vectors, line, plane, vector form, parametric form. direction vector, normal vector.
: Appendix A up to cross product. Specically make sure to ponder
upon Denition A.1, Theorem A.2, Denition A.3, Denition A.4, Theorem A.5, Denition A.6,
Denition A.8. Sec 1.1
1
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
2
Sets
A
set
is a collection of objects. The objects in a set are called
elements
or
members
of that set. For
instance, the collection of all Toronto based sports teams is a set and the Blue Jays is a member of this
set, or the collection of all even numbers is a set and
2022
is a member of this set. Let
S
be a set. We
a ∈ S to signify that the object a is an element of the set S and write b ∈
/ S to signify that the
b is not an element of the set S . For example, suppose E denotes the set of all even numbers.
Then 4 ∈ E and 3 ∈
/ E . Since a set has no distinguishing feature other than its content, there is a unique
set containing no elements which is called the empty set and is denoted by ∅ or {}. Some other common
write
object
1
sets have specic names and notations:
R:
Z:
Q:
N:
C:
the set of all real numbers
the set of all integers or whole numbers
the set of all rational numbers
the set of all natural numbers
the set fo all complex numbers
In this course, we refer to elements of
R
scalars.
by
There are multiple ways of describing a set. One way is to describe the set in words by specifying its
members in words, like what we did for examples above. If the set is nite, and has manageable number
of elements, we can describe it by listing all its members, separated by a comma, inside a set of curly
brackets.
For instance the set
M
containing the names of the following four mathematicians: Gauss,
Noether, Mirzakhani and Viazovska can described as
M = {Noether,
Gauss, Mirzakhani, Viazovska}.
Note that the order in which the elements are listed does not matter. Often time, listing all the elements
is not an ecient, or even feasible, way of describing a set. For instance think about the set of all even
integers we described above. It is impossible to list them all.
A better way to describe sets is using the set builder notation. Let
set
P
S
be a set. We can dene another
using the following general format
P = {s ∈ S | s
satises a certain property}
To see this methods in action, let's describe the set
E
E = {z ∈ Z | z = 2k ,
Which reads E is the set of all
is the set of all
z
in
Z
such that
z
z
in
Z
such that
z
of even numbers
for some integer k}
is some integer multiple of two. In other words, E
is even!).
Another common way of describing a set is to give a parametric description of a typical element in
that set:
P = {an
expression describing a typical element in
Let's describe
E
P|
specifying the parameters used in the description}
again, using the second method
E = {2k | k ∈ Z},
Which reads E is the set of all
multiples of
2k 's
where
k
is an integer. In other word, E is the set of all integer
2.
Denition
(Subset). We say a set A is a subset of a set B , and write A ⊆ B , if all the elements of A
are also in B . In other words, A ⊆ B , if for every a ∈ A, a ∈ B .
Denition (Equality of Sets). We say sets A and B are equal if A is a subset of B and B is a subset
of A. That is A = B if A ⊆ B and B ⊆ A.
1You
are already familiar with some of these sets, like R and Z, you maybe less familiar with some others like Q and C.
Don't worry!
MAT 188-WEEK 1
Denition
(Union and Intersection of Sets)
Sets, Vectors, Lines, Planes
3
. Let X be a set and A and B be subsets of X . The union
of A and B is a set that contains all elements of A and B , that is
A ∪ B = {x ∈ X | x ∈ A or x ∈ B}
The intersection of A and B is the set of all common elements between A and B that is
A ∩ B = {x ∈ X | x ∈ A and x ∈ B}.
Example. Let A = {2, 5, 7, π} and B = {4, π, 5} be subsets of R. Then A ∪ B = {2, 5, 7, π, 4} and
A ∩ B = {5, π}.
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
4
Vectors: Algebraic View
A
matrix
is a rectangular array of entries.
1 2 π
−1 0 4
M is a
 
1
2

2 &times; 3 matrix . A column vector is a matrix with only one column and multiple rows for instance 2 is a
3
column vector. A row vector is a matrix with one row and multiple columns for instance 1 2 3 π is
numbers. For instance
M =
For the purposes of our course, these entries are often
is a matrix with two rows and three columns. We say
a row vector. In this course, unless otherwise stated, by a vector we mean a column vector. The entries
components. The collection of all column vectors with n components is denoted
n-dimensional Euclidean vector space. That is
of a vector are called its
n
n
by R ; we will refer to R as the
 
a1
 a2 

Rn = {
 ...  | a1 , &middot; &middot; &middot; , an
are in
R}
an
Fix
n.
Rn . Given ⃗v , w
⃗ ∈ Rn
    

v1
w1
v1 + w1
 v2   w2   v2 + w2 
   

⃗v + w
⃗ =
.
 ...  +  ...  = 

.
.
We can add, and scale vectors in
vn
Given vector
⃗v ∈ R
n
and a scalar
wn
vn + wn
k∈R
   
v1
kv1
 v2   kv2 
  
k⃗v = k 
 ...  =  ... 
vn
kvn
You should read Denition A.1, and Theorem A.2 and the following
Examples in Appendix A to see the rules that govern vector addition and scalar multiplication.
2This
is a naive denition for a matrix that we go with for now; soon in this course we will learn that matrices are WAY
more than just an array of numbers.
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
5
Vectors: Geometric View
While it is dicult (or impossible) to visualize vectors with more that three components, We can
2
3
2
visualize vectors in R, R and R . Let's walk through visualizing vectors in R . Given two points
P = (p1 , p2 )
xy -plane), consider an
arrow, that is a directed line segment, denoted by
starting from P and ending at Q. This arrow
represents the displacement from P to Q. For instance, if P = (1, 1) and Q = (3, 2), P⃗Q can be
understood as describing a walk we take in the plane where we move two units
horizontally and
one unit
2
2
vertically. The vector P⃗Q encodes this walk, and is denoted by P⃗Q =
. Note that
is just a
1
1
and
Q = (q1 , q2 )
on the Cartesian plane (you might know this as
P⃗Q
direction that says move two units horizontally and one unit vertically. We can allow ourselves to begin
walking from any point in the plane, and our end points will depend on where we start. In particular if
we start from
P we end up on
the vector
2
1
Q.
Where do we end up if we start from the origin and take a ride with
?
We will most frequently begin at the origin.
The
x
standard representation of a vector ⃗x = 1
x2
in
the Cartesian coordinate plane is an arrow (a directed line segment) connecting the origin to the point
(x1 , x2 ).
We say
A vector in
⃗x
R2
is the
position vector
of the point
x = (x1 , x2 ).
(in standard representation) is uniquely determined by its endpoint. Conversely, with
each point in the plane we can associate its position vector, which connects the origin to the given point.
2
In this sense, we can identify R , the set of all vectors with two components, with the set of all points on
2
the Cartesian plane. That is, it is safe to think of R as the Cartesian plane!
With this set up, we can think of adding two vectors as walking along two vectors, one after the other.
Visualizing vector addition this ways is often referred to as the parallelogram Law.
Reading from the textbook: Read Geometrical representation of vectors in appendix A.
Exercise. Pick two vectors ⃗v and w⃗ in R3 . Draw a picture that represents ⃗v and w⃗ geometrically. You
can use Figure 7 and Figure 8 in Appendix A as a model. Draw ⃗v + w
⃗ is two ways, one using the
parallelogram law and once by drawing w
⃗ from the tip of ⃗v . Explain why the two methods give us the same
result.
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
6
More on Vectors
Once we think about vectors geometrically as arrows, we can talk about geometric concepts such as the
length of vectors, parallel vectors and perpendicular vectors understood through our spacial conception
n
of these terms. Interestingly, we can make sense of these concepts for vectors in R , for any n, despite
3
the fact that we can not draw or visualize vectors above R .

x1
 x2 
n

The length or norm ∥⃗x∥ of a vector ⃗x = 
 ...  in R is

Denition (Norm of a Vector).
xn
∥⃗x∥ =
Denition
. We say that two vectors ⃗v and w⃗ in Rn are parallel if one of them is a
(Parallel Vectors)
scalar multiple of the other.
Exercise.
(1)
(2)
(3)
q
x21 + x22 + &middot; &middot; &middot; + x2n
Read Example 3 and Example 4 in Appendix A.
Draw a vector ⃗v in Rn . Visually justify how the denition of the norm of a vector matches your
intuition of the length of the vector you drew.
Draw a vector w
⃗ parallel to ⃗v . Move w
⃗ around. What do you notice.
Is ⃗v parallel to ⃗v ? Use the Denition of parallel vectors to answer.
Next we will make sense of perpendicular vectors and angle between vectors in
Rn .
Denition
(Dot Product). Let ⃗
v and w
⃗ be (row or column) vectors with components v1 , v2 , &middot; &middot; &middot; , vn and
w1 , w2 , &middot; &middot; &middot; , wn respectively. The dot product of ⃗v and w
⃗ is a scalar denoted by ⃗v &middot; w
⃗ and is dened as
⃗v &middot; w
⃗ = v1 w1 + v2 w2 + &middot; &middot; &middot; + vn wn .
Note that
∥⃗x∥ =
√
⃗x &middot; ⃗x.
Dot product can be interpreted geometrically. With some high school geometry, we can show that for
2
3
vectors in R and R
⃗v &middot; w
⃗ = cos θ∥⃗v ∥∥w∥,
⃗
where
θ
is the angle between
⃗v
and
w
⃗.
Denition (Angle Between Vectors). Given vectors ⃗v and w⃗ , the angle between ⃗v and w⃗ in Rn is dene
to be3
arccos(
⃗v &middot; w
⃗
).
∥⃗v ∥∥w∥
⃗
Now we can extend our intuition about perpendicular vectors to
Rn .
Denition (Orthogonal Vectors). We say two vectors ⃗v and w⃗ in Rn are perpendicular or orthogonal
if ⃗v &middot; w
⃗ = 0.
Exercise.
(2)
(3)
3this
Does the denition of orthogonal vectors match your intuition in R2 and R3 ?
If ⃗v and w
⃗ are orthogonal, what is the angle between them? Is there an exception to your answer?
Is there a vector in Rn that is orthogonal to all other vectors un Rn ?
(1)
denition makes sense thanks to Cauchy Schwartz inequality that guarantees −1 ≤
⃗
v &middot;w
⃗
∥⃗
v ∥∥w∥
⃗
≤ 1.
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
7
Lines and Planes
Lines in R2 .
You know from high school that the equation
plane that passes through the origin and has slope
to the (linear) equation
R2 in
y = ax.
in
y = ax.
a.
y = ax
represents a line
In other words,
ℓ
ℓ
on the Cartesian
represents the set of all solutions
We can identify points on the Cartesian plane with (the tips) of vectors
standard position. This way,
ℓ
can be thought of as the set of all vectors
x
y
in
R2
such that
x
l={
∈ R2 | y = ax}
y
Let
d⃗ be
a nonzero vector on the line
ℓ.
We say
⃗
l = {⃗x ∈ R2 | ⃗x = k d,
d⃗ is
for some
a direction vector for
k ∈ R}
or
ℓ,
and we can describe
ℓ
as
l = {k d⃗ | k ∈ R}
⃗x = k d⃗ as the vector form or vector parametric form of the line ℓ. The scalar k is a
varies over R. It is allowed to take any value so that all the points on the line ℓ can be
We often refer to
parameter that
obtained from this equation. When we express a line in its vector form, we always think of vectors in
their standard position.
m
R2 ,
a, that does not go through the origin. Such
⃗ be a nonzero
described as the set of all solutions to y = ax + b, for some nonzero b. Let d
and let p
⃗ be the position vector of a point p on m. Then we can describe m as
Now consider a line
in
with slope
m = {⃗x ∈ R2 | ⃗x = k d⃗ + p⃗,
We refer to
⃗x = k d⃗ + p⃗
as the
vector form
that goes through the tip of vector
p⃗
for some
or
k ∈ R}
or
lines can be
vector on
m
m = {k d⃗ + p⃗ | k ∈ R}
vector parametric form
of a line with
direction vector
in the standard position. If we describe
d
d⃗ = 1
d2
, and
p⃗ =
p1
p2
d⃗
in
terms of their components, then the vector form can be broken into an equation of each component.
x1 = kd1 + p1
x2 = kd2 + p2
(0.1)
That system of equations
(0.1) are called the
Another way to describe the line
ℓ
ℓ
parametric form
with equation
y = ax
is any none-zero vector perpendicular to ℓ. Pick a vector
Rn that are perpendicular to ⃗n. In other words,
of the line
m.
is via a normal vector. A normal vector for
⃗n
perpendicular to
ℓ.
Then
ℓ
is the set of
vectors in
l = {⃗x ∈ R2 | ⃗x &middot; ⃗n = 0}.
Lines in R3 .
We can describes the vector form and the parametric from of a line in
R
3
exactly the same

d1
2
3
⃗ = d2 that is d⃗ is a
way as we did for lines in R . Consider a line ℓ in R , with direction vector d
d
 3
p1

vector on line ℓthat passes that goes thorough the point p = (p1 , p2 , p3 ). Let p
⃗ = p2  be the position
p3
vector of p. Then ℓ can be described as
l = {⃗x ∈ R3 | ⃗x = k d⃗ + p⃗,
with the vector form
⃗x = k d⃗ + p⃗
for some
k ∈ R}
and the parametric equation
or
l = {k d⃗ + p⃗ | k ∈ R}
MAT 188-WEEK 1
Sets, Vectors, Lines, Planes
8
x1 = kd1 + p1
x2 = kd2 + p2
x3 = kd3 + p3 .
(0.2)
Exercise. Does the equation y = ax represent a line in R3 ?
Lines in R2 and R3 . The only plane in R2 is the Cartesian plane R2 itself. So there is not much there
3
to look at. Let's think about a plane p in R that passes through the origin. Can we come up with
a vector equation, a parametric equation and a way to describe
a vector perpendicular to the plane
the plane
p.
p?
⃗n
in terms of it's normal vector, that is
⃗n
p is perpendicular to ⃗n
p can be described as
The position vector of every point on
position that is perpendicular to
p
is on
p.
hence,
be a vector perpendicular to
and every vector in standard
p = {⃗x ∈ R3 | ⃗x &middot; ⃗n = 0}.
 
n1

Let ⃗
n = n2 
n3
and
 
x1

⃗x = x2 .
x3
The
normal equation ⃗x &middot; ⃗n = 0 unpacks into
n1 x1 + n2 x2 + n3 x3 = 0.
Exercise. Let b be a non-zero scalar. Explain what the solution to n1 x1 + n2 x2 + n3 x3 = b describes R3 ?
Does it pass through the origin?
To describe the vector form and the parametric equation of
linear combination.
We will see this next week!
m,
we need to understand the concept of
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