0. Prerequisites
We give a brief overview of some key concepts from group theory and linear algebra that will be used throughout the module. This is not an exhaustive list; there may well be other facts we will need!
0.0.1. Group theory
Definition of a group
Definition 0.0.1.
A group is a triple , where is a set, “” is a binary operation on (i.e. a function from to ), and is a distinguished element such that for all .
Furthermore, we require that
-
(i)
is associative, i.e. ; this lets us write (normally we just write ).
-
(ii)
For every , there exists some s.t. . (It follows that , and that such an element is unique).
We often just write for a group, as it is normally clear from the context which binary operation is being used for the definition. We will generally either use additive or multiplicative notation for the binary operation, i.e. or .
Examples of groups
Example 0.0.2.
, , , , , , , ,
These are all Abelian! Some non-Abelian examples are as follows:
Example 0.0.3.
Let . Here and .
Example 0.0.4.
Let be a set, and denote by the set of invertible (i.e. bijective) functions from to . Then is a group (here “” denotes composition of functions and is the identity map for all ).
The group is called the permutation or symmetric group on . In the special case , we simply write .
A number of groups that will be important for us arise from linear algebra:
Example 0.0.5.
Let be a vector space over a field . We denote by the group of invertible linear maps from to . In the case , we write .
Observe that consists of invertible matrices with entries from . If has dimension , we may identify with by choosing a basis of and using coordinate transformations.
Presentations of groups
A convenient way of defining a group is in terms of generators and relations; we write
here are called the generators and the are finite words in the generators, called relations. The elements of the group are all finite words in the generators modulo the equivalence relation that two words are equivalent if one can be obtained from the other by substituting in or out the relations; we say that a word is reduced if it cannot be made any shorter by using the relations.
Example 0.0.6.
This is just the cyclic group ; denotes the word ( times). Then using the relation , we see that . Thus, consists of the elements .
Example 0.0.7.
Since both elements have order 2, we see that any word in and can be reduced to just alternating words with each element having order one, for example . From this, we obtain that the only possible reduced words are of the form or . However, for any of these words of length greater than 3, we can use the relation to shorten the word:
(and similarly for words of the form ). Thus, any reduced word has length at most 3. The elements of are therefore
Example 0.0.8.
Let . This is called the dihedral group of order , and can be viewed as the group of symmetries of the regular -gon.
Example 0.0.9.
Let . This is called the dicyclic group of order .
Homomorphisms and isomorphisms
Definition 0.0.10.
Let and be two groups. A function is called a (group) homomorphism if
for all .
Note that it follows from the definition that and .
Example 0.0.11.
For any groups and , the trivial map , for all is a homomorphism.
Example 0.0.12.
The determinant map is a homomorphism.
An invertible (bijective) homomorphism is called an isomorphism. If there exists an isomorphism from a group to a group , then and are said to be isomorphic, and we write . Isomorphic groups are essentially identical from a group-theoretic point of view.
Example 0.0.13.
Given , let and . The map given by is an isomorphism.
Subgroups and cosets
Definition 0.0.14.
Given a group , a subset is called a subgroup of if is a group with the same binary operation as the group .
In order to check whether a subset is a subgroup, one needs to check that , and that is closed under multiplication and when taking inverses.
Example 0.0.15.
Let be a group homomorphism. Then is a subgroup of and .
A nice example of this is the following: is the subgroup of consisting of all matrices with determinant one.
Given a group with a subgroup , one can form the coset space . The elements of are called cosets; these are subsets of of the form
If a subgroup has the property that for all , then is said to be normal. The coset space has a natural group structure, with multiplication given by . You can check this is a well-defined group operation due to being normal.
Theorem 0.0.16 (First Isomorphism Theorem).
Given a homomorphism , is a normal subgroup of and
with the map being an isomorphism between the two groups.
Group actions and conjugacy classes
Definition 0.0.17.
A group action of a group on a set is a function with the following properties:
-
(i)
for all .
-
(ii)
for all and .
We normally don’t write out the function “”; instead, we simply denote by . In this notation, property (2) reads
Example 0.0.18.
The group acts on the set : for all and . More generally, acts on the set by evaluation.
Example 0.0.19.
The group acts on the regular -gon inscribed in the plane centred at the origin with a vertex at . The element acts by rotating the polygon counterclockwise radians, and mirrors the polygon in the -axis.
Example 0.0.20.
The group acts on itself by conjugation: for all (note that here the “” does not mean the group multiplication, but instead the conjugation action).
Given a group action of a group on a set , we let denote the orbit of a point under , that is . The stabiliser of a point is defined as .
In the special case of a group acting on itself by conjugation, the orbits are called conjugacy classes, and are instead denoted , i.e. .
Example 0.0.21.
Every element of may be written as a product of disjoint cycles. For example, let be . Then the conjugacy class of consists of all elements with the same cycle type. For the given element , we have that consists of all permutations of five elements that may be written as a disjoint 2-cycle and 3-cycle.
0.0.2. Linear algebra
Vector spaces and linear maps
Recall that a vector space over a field is a set combined with two operations:
-
(i)
vector addition: given two vectors , we can “add” them together to obtain a new vector .
-
(ii)
scalar multiplication: given a vector and an element , we can “multiply” by to obtain a new vector .
These two operations must be compatible with each other and satisfy some natural properties, as listed in the axioms for vector spaces.
We will almost always just consider vector spaces over , however other examples that one might consider are (here where is prime and , and denotes the field with elements).
Given two vector spaces (over the same field ), a function is said to be linear if
for all and . Two vector spaces are said to isomorphic if there exists an invertible linear map from one to the other.
The set of all linear maps from to is denoted , and we write instead of . These spaces carry a natural vector space structure inherited from and ; given and , we define and by
for all .
Subspaces and sums and quotients of vector spaces
Definition 0.0.22.
A subset of a vector space is said to be a subspace of if it is a vector space with the same addition and scalar multiplication operations as on .
Note that to show that a nonempty subset is a subspace, one simply needs to check that it is closed under vector addition and scalar multiplication.
Example 0.0.23.
Given , recall that
Then is a subspace of and is a subspace of .
A vector space is an Abelian group with respect to the vector addition operation. A subspace of is therefore a normal subgroup with respect to this operation, allowing us to consider the quotient group . We can then define a scalar multiplication on this quotient by the formula
for all and . This definition gives the quotient group a vector space structure, and we call this the quotient space of with respect to .
Given , define a new map by
for all . Then is an isomorphism of vector spaces.
The external direct sum of two vector spaces is the set
with the addition and scalar multiplication operations being defined component-wise.
If and are subspaces of a vector space such that and every element of may be written as the sum of an element of and an element of , then we say that is the internal direct sum of and , and the map is an isomorphism from to .
One can define sums of multiple vector spaces in a similar way.
Eigenvalues and eigenvectors
Definition 0.0.24.
A number is said to be an eigenvalue of if there exists a non-zero vector such that
The vector is said to be a -eigenvector of .
A basis , of is said to be an eigenbasis of if every element of the basis is an eigenvector of . If has an eigenbasis, then is said to be diagonalisable. We will make use of the following result at a few key moments in the module:
Proposition 0.0.25.
Let be two commuting elements of , where is a finite dimensional vector space. If and are both diagonalisable, then there is a joint eigenbasis of , i.e. a basis of such that every element of the basis is an eigenvector of both and .
0.0.3. Exercises
Problem 1. Let be a group. Given , define by
Show that is a group, where .
Problem 2. Find all subgroups of .
Problem 3. Compute the conjugacy classes of .
Problem 4. Compute the conjugacy classes of .
Problem 5. Find the conjugacy classes of .
Problem 6. Let a group act on a set . Show that the stabiliser of a point , , is a subgroup of . Note that it is often called the stabiliser subgroup.
Problem 7. Show that . Hint: Label the vertices of the triangle by and consider the action of on them (cf. Example 0.0.19).
Problem 8. Identify, with proof, the group given in Example 0.0.7.
Problem 9. Show that any finite group is isomorphic to a subgroup of .
Problem 10. For a prime , compute the orders of and .
Problem 11. Prove Proposition 0.0.25.