Notes
Sets
Slides by Christopher M. Bourke
Instructor: Berthe Y. Choueiry
Spring 2006
Computer Science & Engineering 235
Introduction to Discrete Mathematics
Sections 1.6 – 1.7 of Rosen
cse235@cse.unl.edu
Introduction I Notes
We’ve already implicitly dealt with sets (integers, Z; rationals (Q)
etc.) but here we will develop more fully the definitions, properties
and operations of sets.
Definition
A set is an unordered collection of (unique) objects.
Sets are fundamental discrete structures that form the basis of
more complex discrete structures like graphs.
Contrast this definition with the one in the book (compare bag,
multi-set, tuples, etc).
Definition
Introduction II Notes
The objects in a set are called elements or members of a set. A set
is said to contain its elements.
Recall the notation: for a set A, an element x we write
x∈A
if A contains x and
x 6∈ A
otherwise.
Latex notation: \in, \neg\in.
Terminology I Notes
Definition
Two sets, A and B are equal if they contain the same elements. In
this case we write A = B.
Example
{2, 3, 5, 7} = {3, 2, 7, 5} since a set is unordered.
Also, {2, 3, 5, 7} = {2, 2, 3, 3, 5, 7} since a set contains unique
elements.
However, {2, 3, 5, 7} =
6 {2, 3}.
Terminology II Notes
A multi-set is a set where you specify the number of occurrences of
each element: {m1 · a1 , m2 · a2 , . . . , mr · ar } is a set where m1
occurs a1 times, m2 occurs a2 times, etc.
Note in CS (Databases), we distinguish:
I a set is w/o repetition
I a bag is a set with repetition
Terminology III Notes
We’ve already seen set builder notation:
O = {x | (x ∈ Z) ∧ (x = 2k for some k ∈ Z)}
should be read O is the set that contains all x such that x is an
integer and x is even.
A set is defined in intension, when you give its set builder notation.
O = {x | (x ∈ Z) ∧ (x ≤ 8)}
A set is defined in extension, when you enumerate all the elements.
O = {0, 2, 6, 8}
Venn Diagram Notes
Example
A set can also be represented graphically using a Venn diagram.
x
A y B
z
a C
Figure: Venn Diagram
More Terminology & Notation I Notes
A set that has no elements is referred to as the empty set or null
set and is denoted ∅.
A singleton set is a set that has only one element. We usually
write {a}. Note the different: brackets indicate that the object is a
set while a without brackets is an element.
The subtle difference also exists with the empty set: that is
∅=
6 {∅}
The first is a set, the second is a set containing a set.
More Terminology & Notation II Notes
Definition
A is said to be a subset of B and we write
A⊆B
if and only if every element of A is also an element of B.
That is, we have an equivalence:
A ⊆ B ⇐⇒ ∀x(x ∈ A → x ∈ B)
More Terminology & Notation III Notes
Theorem
For any set S,
I ∅ ⊆ S and
I S⊆S
(Theorem 1, page 81.)
The proof is in the book—note that it is an excellent example of a
vacuous proof!
Latex notation: \emptyset, \subset, \subseteq.
More Terminology & Notation IV Notes
Definition
A set A that is a subset of B is called a proper subset if A 6= B.
That is, there is some element x ∈ B such that x 6∈ A. In this case
we write A ⊂ B or to be even more definite we write
A(B
Example
Let A = {2}. Let B = {x | (x ≤ 100) ∧ (x is prime)}. Then
A ( B.
Latex notation: \subsetneq.
More Terminology & Notation V Notes
Sets can be elements of other sets.
Example
{∅, {a}, {b}, {a, b}}
and
{{1}, {2}, {3}}
are sets with sets for elements.
More Terminology & Notation VI Notes
Definition
If there are exactly n distinct elements in a set S, with n a
nonnegative integer, we say that S is a finite set and the
cardinality of S is n. Notationally, we write
|S| = n
Definition
A set that is not finite is said to be infinite.
More Terminology & Notation VII Notes
Example
Recall the set B = {x | (x ≤ 100) ∧ (x is prime)}, its cardinality is
|B| = 25
since there are 25 primes less than 100. Note the cardinality of the
empty set:
|∅| = 0
The sets N, Z, Q, R are all infinite.
Proving Equivalence I Notes
You may be asked to show that a set is a subset, proper subset or
equal to another set. To do this, use the equivalence discussed
before:
A ⊆ B ⇐⇒ ∀x(x ∈ A → x ∈ B)
To show that A ⊆ B it is enough to show that for an arbitrary
(nonspecific) element x, x ∈ A implies that x is also in B. Any
proof method could be used.
To show that A ( B you must show that A is a subset of B just
as before. But you must also show that
∃x((x ∈ B) ∧ (x 6∈ A))
Finally, to show two sets equal, it is enough to show (much like an
equivalence) that A ⊆ B and B ⊆ A independently.
Proving Equivalence II Notes
Logically speaking this is showing the following quantified
statements:
∀x(x ∈ a → x ∈ B) ∧ ∀x(x ∈ B → x ∈ A)
We’ll see an example later.
The Power Set I Notes
Definition
The power set of a set S, denoted P(S) is the set of all subsets of
S.
Example
Let A = {a, b, c} then the power set is
P(S) = {∅, {a}, {b}, {c}, {a, b}, {a, c}, {b, c}, {a, b, c}}
Note that the empty set and the set itself are always elements of
the power set. This follows from Theorem 1 (Rosen, p81).
The Power Set II Notes
The power set is a fundamental combinatorial object useful when
considering all possible combinations of elements of a set.
Fact
Let S be a set such that |S| = n, then
|P(S)| = 2n
Tuples I Notes
Sometimes we may need to consider ordered collections.
Definition
The ordered n-tuple (a1 , a2 , . . . , an ) is the ordered collection with
the ai being the i-th element for i = 1, 2, . . . , n.
Two ordered n-tuples are equal if and only if for each
i = 1, 2, . . . , n, ai = bi .
For n = 2, we have ordered pairs.
Cartesian Products I Notes
Definition
Let A and B be sets. The Cartesian product of A and B denoted
A × B, is the set of all ordered pairs (a, b) where a ∈ A and b ∈ B:
A × B = {(a, b) | (a ∈ A) ∧ (b ∈ B)}
The Cartesian product is also known as the cross product.
Definition
A subset of a Cartesian product, R ⊆ A × B is called a relation.
We will talk more about relations in the next set of slides.
Cartesian Products II Notes
Note that A × B 6= B × A unless A = ∅ or B = ∅ or A = B. Can
you find a counter example to prove this?
Cartesian Products III Notes
Cartesian products can be generalized for any n-tuple.
Definition
The Cartesian product of n sets, A1 , A2 , . . . , An , denoted
A1 × A2 × · · · × An is
A1 ×A2 ×· · ·×An = {(a1 , a2 , . . . , an ) | ai ∈ Ai for i = 1, 2, . . . , n}
Notation With Quantifiers Notes
Whenever we wrote ∃xP (x) or ∀xP (x), we specified the universe
of discourse using explicit English language.
Now we can simplify things using set notation!
Example
∀x ∈ R(x2 ≥ 0)
∃x ∈ Z(x2 = 1)
Or you can mix quantifiers:
∀a, b, c ∈ R ∃x ∈ C(ax2 + bx + c = 0)
Set Operations Notes
Just as arithmetic operators can be used on pairs of numbers,
there are operators that can act on sets to give us new sets.
Set Operators Notes
Union
Definition
The union of two sets A and B is the set that contains all
elements in A, B or both. We write
A ∪ B = {x | (x ∈ A) ∨ (x ∈ B)}
Latex notation: \cup.
Set Operators Notes
Intersection
Definition
The intersection of two sets A and B is the set that contains all
elements that are elements of both A and B We write
A ∩ B = {x | (x ∈ A) ∧ (x ∈ B)}
Latex notation: \cap.
Set Operators Notes
Venn Diagram Example
A B
Sets A and B
Set Operators Notes
Venn Diagram Example: Union
A B
Union, A ∪ B
Set Operators Notes
Venn Diagram Example: Intersection
A B
Intersection, A ∩ B
Disjoint Sets Notes
Definition
Two sets are said to be disjoint if their intersection is the empty
set: A ∩ B = ∅
A B
Figure: Two disjoint sets A and B.
Set Difference Notes
Definition
The difference of sets A and B, denoted by A \ B (or A − B) is
the set containing those elements that are in A but not in B.
A B
Figure: Set Difference, A \ B
Latex notation: \setminus.
Set Complement Notes
Definition
The complement of a set A, denoted Ā, consists of all elements
not in A. That is, the difference of the universal set and A; U \ A.
Ā = {x | x 6∈ A}
A A
Figure: Set Complement, A
Latex notation: \overline.
Set Identities Notes
There are analogs of all the usual laws for set operations. Again,
the Cheat Sheet is available on the course web page.
http://www.cse.unl.edu/cse235/files/
LogicalEquivalences.pdf
Let’s take a quick look at this Cheat Sheet
Proving Set Equivalences Notes
Recall that to prove such an identity, one must show that
1. The left hand side is a subset of the right hand side.
2. The right hand side is a subset of the left hand side.
3. Then conclude that they are, in fact, equal.
The book proves several of the standard set identities. We’ll give a
couple of different examples here.
Proving Set Equivalences Notes
Example I
Let A = {x | x is even} and B = {x | x is a multiple of 3} and
C = {x | x is a multiple of 6}. Show that
A∩B =C
Proof.
(A ∩ B ⊆ C): Let x ∈ A ∩ B. Then x is a multiple of 2 and x is a
multiple of 3, therefore we can write x = 2 · 3 · k for some integer
k. Thus x = 6k and so x is a multiple of 6 and x ∈ C.
(C ⊆ A ∩ B): Let x ∈ C. Then x is a multiple of 6 and so x = 6k
for some integer k. Therefore x = 2(3k) = 3(2k) and so x ∈ A
and x ∈ B. It follows then that x ∈ A ∩ B by definition of
intersection, thus C ⊆ A ∩ B.
We conclude that A ∩ B = C
Proving Set Equivalences Notes
Example II
An alternative prove uses membership tables where an entry is 1 if
it a chosen (but fixed) element is in the set and 0 otherwise.
Example
(Exercise 13, p95): Show that
A ∩ B ∩ C = Ā ∪ B̄ ∪ C̄
Proving Set Equivalences Notes
Example II Continued
A B C A ∩ B ∩ C A ∩ B ∩ C Ā B̄ C̄ Ā ∪ B̄ ∪ C̄
0 0 0 0 1 1 1 1 1
0 0 1 0 1 1 1 0 1
0 1 0 0 1 1 0 1 1
0 1 1 0 1 1 0 0 1
1 0 0 0 1 0 1 1 1
1 0 1 0 1 0 1 0 1
1 1 0 0 1 0 0 1 1
1 1 1 1 0 0 0 0 0
1 under a set indicates that an element is in the set.
Since the columns are equivalent, we conclude that indeed,
A ∩ B ∩ C = Ā ∪ B̄ ∪ C̄
Generalized Unions & Intersections I Notes
In the previous example we showed that De Morgan’s Law
generalized to unions involving 3 sets. Indeed, for any finite
number of sets, De Morgan’s Laws hold.
Moreover, we can generalize set operations in a straightforward
manner to any finite number of sets.
Definition
The union of a collection of sets is the set that contains those
elements that are members of at least one set in the collection.
n
[
Ai = A1 ∪ A2 ∪ · · · ∪ An
i=1
Generalized Unions & Intersections II Notes
Latex notation: \bigcup.
Definition
The intersection of a collection of sets is the set that contains
those elements that are members of every set in the collection.
n
\
Ai = A1 ∩ A2 ∩ · · · ∩ An
i=1
Latex notation: \bigcap.
Computer Representation of Sets I Notes
There really aren’t ways to represent infinite sets by a computer
since a computer is has a finite amount of memory (unless of
course, there is a finite representation).
If we assume that the universal set U is finite, however, then we
can easily and efficiently represent sets by bit vectors.
Specifically, we force an ordering on the objects, say
U = {a1 , a2 , . . . , an }
For a set A ⊆ U , a bit vector can be defined as
0 if ai 6∈ A
bi =
1 if ai ∈ A
for i = 1, 2, . . . , n.
Computer Representation of Sets II Notes
Example
Let U = {0, 1, 2, 3, 4, 5, 6, 7} and let A = {0, 1, 6, 7} Then the bit
vector representing A is
1100 0011
What’s the empty set? What’s U ?
Set operations become almost trivial when sets are represented by
bit vectors.
In particular, the bit-wise Or corresponds to the union operation.
The bit-wise And corresponds to the intersection operation.
Example
Computer Representation of Sets III Notes
Let U and A be as before and let B = {0, 4, 5} Note that the bit
vector for B is 1000 1100. The union, A ∪ B can be computed by
1100 0011 ∨ 1000 1100 = 1100 1111
The intersection, A ∩ B can be computed by
1100 0011 ∧ 1000 1100 = 1000 0000
What sets do these represent?
Note: If you want to represent arbitrarily sized sets, you can still
do it with a computer—how?
Conclusion Notes
Questions?
