Symmetry element and symmetry operation

A symmetry element (not to be confused with a group element) is a point, a line, a plane or an axis with respect to which a symmetry operation is carried out. For three-dimensional objects, there are only 5 types of symmetry elements that need to be considered:

Question

Is  a unique symmetry operation?

Answer

Yes, because an object with an  symmetry element may not have a symmetry element of the same . For example, a tetragonal disphenoid has an symmetry element but only a symmetry element.

 

If an object has more than one symmetry axis of rotation, the one with the highest  is called the principal axis, which is conventionally coincident with the -axis.

Consider an equilateral triangle in a three-dimensional real vector space (see figure I). It has a  axis of symmetry and three planes of reflection. Figure II has a  axis of symmetry, while figure III has an  axis of symmetry and a centre of inversion . All objects have the identity symmetry element.

A symmetry operation is a geometric transformation of an object with respect to a symmetry element in a real vector space such that the object looks the same after the transformation. However, the labelling of components of the object may be different post-transformation. For example, a reflection of the equilateral triangle in the -plane results in the triangle looking the same but the labels  and  being swapped.

Question

Why do we need to label the corners of the equilateral triangle?

Answer

Since the object is defined in a real vector space, each point on the object, and hence each label, is represented by a position vector. A symmetry operation preserves the magnitude of the position vector but changes its direction. For example, a reflection  in the -plane results in the triangle looking the same but transforms vector  to vector  (see diagram below). If we carry out two consecutive symmetry operations of  on the original triangle, vector  transforms into itself. This is equivalent to performing the symmetry operation  on the original triangle. Consequently, we are able to form mathematical groups, whose elements are symmetry operators, and use them to analyse the properties of objects like molecules.

 

In total, there are 6 symmetry elements and 6 associated symmetry operations for the equilateral triangle:

Question

What does the notation  mean and why is the reflection in the -plane not included in the set of symmetry operations for the equilateral triangle?

Answer

The angle of rotation is regarded as positive if the rotation is counter-clockwise when we look down the axis of rotation towards the origin (and hence the notation ).  and  can also be denoted as  and respectively, where  is counter-clockwise by default and  is the double application of . The reflection in the -plane is a symmetry operation for the equilateral triangle but has the same effect as  and hence not included in the set.

 

If we conduct all 6 symmetry operations on the triangle, we find one ordered triple invariant: the origin. The significance of the invariant point and the two definitions above, together with the definition of a group, leads to an important mathematical group – the point group – which we’ll discussed in the next article.

 

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Point group

A point group consists of geometric transformations known as symmetry operations, which preserve a single common point while transforming an object defined in a real vector space into physically indistinguishable replicas of itself.

Although an object undergoing a symmetry operation ends up looking the same after the transformation, the labelling of similar components of the object may change. In other words, to form a point group, all symmetry operations for an object must:

    1. Send the object into physically indistinguishable copies of itself.
    2. Combine with one another through binary operations such that the results are consistent with the 4 properties of a group.
    3. Leave one point invariant.

Point groups are determined by considering symmetry operations for different objects, beginning with simple shapes and moving on to more complex ones. According to the three abovementioned requirements, we start by inspecting the chosen object visually and finding all the symmetry elements (not to be confused with group elements) and their associated symmetry operations. For example, the only symmetry elements for the object in figure I, which is made up of two equally spaced right-angled triangles on a circle, are the identity symmetry element  and a 2-fold rotation axis . The corresponding symmetry operations are  and .

Next, we select a position vector  (see figure II) and perform the symmetry operations  and consecutively on the position vector. The results, in relation to the transformation of the position vector, are summarised in the multiplication table i:

Note that we have omitted the carets – i.e. – for simplicity. From the table, we can easily verify all four properties of the group, e.g. the identity element is and the inverse of an element of the group is the element itself. Therefore, the set of symmetry operations for the object forms a point group of order 2 under the binary operation of multiplication. We call this group the  point group. Similarly, the set of symmetry operations  for the object in figure III forms the point group  of order 3 (see multiplication table ii). In general, we have an infinite number of uniaxial point groups , each of which is called a cyclic group, whose elements are . For a cyclic group where is even, one of its elements is equivalent to the symmetry operation .

Let’s suppose the object in figure I have complete arrow heads (see figure IV). Other than  and , the object has a plane of symmetry perpendicular to the axis of rotation (the horizontal plane is denoted by ) and a centre of inversion at the origin. The set of symmetry operations  forms the Abelian point group  (see multiplication table iii). The object in figure IVa belongs to the  point group (see multiplication table iv) with the set of elements , where . Similar to the point groups, if we apply the same logic to other related objects, we have an infinite number of point groups, each with symmetry operations of if  is even (one of the  symmetry operations is equivalent to ) and if is odd.

Question

Why aren’t and elements of the group ?

Answer

They are not unique elements, as they are equivalent to other ‘simpler’ elements of the group:

 

For an object that is made up of two equally spaced equilateral (or isosceles) triangles on a circle (see figure V above), we have the Abelian point group , whose elements are the symmetry operations , ,  and , where the symmetry elements for  and  are the vertical planes: -plane and -plane respectively. The multiplication table for this point group is shown in table v above. As before, we have an infinite number of  point groups. The cone depicted in figure VI is an example of an object that belongs to the  point group, where , i.e. the  point group, with the set of symmetry operations: .

Question

Are and point groups?

Answer

is a trivial point group whose sole element is the symmetry operation . An object of this group is considered to have no rotational symmetry.

and  have the same set of symmetry operations: . Since objects of both point groups have no rotational symmetry, the symbol for the reflection symmetry operation does not have a subscript. In fact, these point groups are so unique that they are collectively known as the point group (for Spiegel, the German word for mirror).

Another unique point group not mentioned above is , whose elements are the symmetry operations and .  and  are known as the non-axial point groups.

 

The next few related point groups are and (for dihedral). They are related in the sense that they have in common the elements  and , which serve as identifiers in categorising molecules by point groups.

Example

Symbol

Object Group elements

Notes

The object is cyclohexane in its twisted boat form. A rhombic disphenoid (tetrahedron with scalene triangles as its faces) also belong to this point group.
 

 

The object is a tetragonal disphenoid (faces are isosceles triangles). is a dihedral plane, i.e. a vertical plane that bisects the angle between two  axes.
1)   axis to screen and bisects .
2)    axes, i) bisects  and ; ii) bisects  and .
3)   planes to screen, i) through ; ii) though .

The  and point groups are identical to the  and point groups respectively in non-standard orientation, i.e. the principal axis is along the -axis. A dumbbell has symmetry elements that are associated with the symmetry operations of

which is a special point group like .

Next, we have the  point group, which is in general associated with the symmetry operations . When , we have the point group , which is the same as the point group . When , the point group  is identical to the point group . For , we need to analyse the point group  with odd and even  separately.

Consider an  point group . When  is odd, the symmetry operation . If , then we have or (according to the closure property of a group). Since , then . Moreover, , which implies that . We can rewrite the symmetry operations of the  point group ( is odd) as

which is equivalent to the set of elements of the point group . For example, the object in figure IVa belongs to the point group and hence to the point group .

When is even, the symmetry operation . Since , we have or . Similarly, or . This implies that . The elements of can be denoted by , where can be odd or even. If is even, then . For example,  and . If is odd, then . We can therefore express the symmetry operations for the  point group (when  is even) as . Figure VII depicts an object that belongs to the  point group.

Taking into account the above characteristics of the  point group, it is possible to relabel it as the  point group, where .

The rest of the point groups are the tetrahedral groups , the octahedral groups , the icosahedral groups  and the special orthogonal group in 3-dimensions  (also known as the full rotation group). The tetrahedral and octahedral groups are collectively called the cubic groups.

Symbol Object Group elements Notes
Each of the three  axes passes perpendicularly through the centre of one of the three depicted faces, e.g. . Each of the four  axes passes through one of four body diagonals, e.g. .

or simply

Same rotation axes as . Three  axes (each with 2 symmetry operations: ) coincident with the  axes. Each of the six  passes through two diagonally opposite edges of the cube.
Same rotation axes as . Four axes (each with 2 symmetry operations: ) coincident with the  axes. A centre of symmetry  and three : i) bisecting  and , ii) bisecting  and , iii) bisecting  and .

or simply

Same as , but the  axes are now  axes (each with 3 symmetry operations: ). Six  axes through mid-points of diagonal edges, e.g.  and .
Same as , but includes centre of inversion ,  axes of  and mirror planes of  and .
The object is a snub dodecahedron with 92 faces (12 pentagons, 80 equilateral triangles), 150 edges and 60 vertices.

The object is a truncated icosahedron with 32 faces (12 pentagons, 20 hexagons), 90 edges and 60 vertices. Same symmetry elements as  with the addition of a centre of inversion, improper axes and mirror planes.

, all possible rotations The object is a sphere with an infinite number of rotation axes, each with all possible values of .

 

Question

Why are  and  called tetrahedral point group and octahedral point group respectively?

Answer

A regular tetrahedron and a regular octahedron have all the same symmetry elements as those used to derive the  point group and the  point group respectively. A tetrahedron with reduced symmetry (e.g. with figure IVa attached to its faces) belongs to the  point group. Similarly, an octahedron with attachments to its vertices belong to the  point group.

 

 

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Crystallographic point groups

Crystallographic point groups are point groups to which crystals are assigned according to their inherent symmetries. They are used for analysing and predicting physical properties of the assigned crystals.

Although there are an infinite number of three-dimensional point groups, only 32 of them are crystallographic point groups. This is due to the crystallographic restriction theorem, which can be proven with trigonometry or linear algebra. The linear algebra proof is as follows:

In crystallography, a lattice point in a three dimensional vector space is described by the position vector  in the form:

where the components , ,  are integers and , , are primitive translation vectors or basis vectors.

A symmetry operation , e.g. a rotation by , maps  to , where the components of  are again integers. Such an operation is always possible only if all the entries of are integers.

Hence, the trace of , i.e. , is also an integer. If we perform a similarity transformation on the rotation matrix , i.e. , such that  is with respect to an orthonormal basis for , is represented by the following matrices:

Since  is invariant under a similarity transformation,  must also be an integer. As we know, , and so, , which implies that . In other words, the rotational symmetry operations of a crystal are restricted to . This is known as the crystallographic restriction theorem.

To derive the 32 crystallographic point groups, we also need to consider the symmetry operation , as it has rotation components. Applying the same logic as above, the entries of the matrix  in the primitive translation vector basis must be integers. An example of the transformed matrix  with respect to an orthonormal basis for is:

Once again,  is an integer. We have, , which allows us to conclude that the  symmetry operations of a crystal are restricted to .

If we disregard point groups whose elements include , we are left with the following 32 crystallographic point groups:

    1. in non-standard orientation
    2. in non-standard orientation
    3. is an element of
    4. is an element of

Question

Are the symmetry operations  and affected by the crystallographic restriction theorem?

Answer

and the trace of a mirror plane is an integer, e.g. .

 

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Class

A class consists of elements of a group that are conjugate to one another. Two elements  are conjugate to each other if , where . If  is conjugate to , then  is conjugate to because

where  (note that  according to the inverse property of a group).

The identity element  is a class by itself since .

If the elements of  are represented by matrices,  is called a similarity transformation. Furthermore, if  and  are conjugate to each other, and  and  are conjugate to each other, then  and  are conjugate to each other. This is because  and  and therefore, , where .

Question

Show that the symmetry operators ,  and  of the point group belong to the same class.

Answer

With reference to the multiplication table,

we have

Similarly, . Since  and  are conjugate to each other, and and  are conjugate to each other, then  and  are conjugate to each other. Therefore,  form a class. Using the same logic, we find that  and  form another class.

 

All elements of the same class in a group  have the same order, which is defined as the smallest value of  such that , where . This is because if is conjugate to , we have

The above equation of  is valid if and only if . This means that the smallest value of  in and in  must be the same. Therefore, elements  and  of the same class in a group have the same order and is denoted by .

Question

Verify that the symmetry operators , and of the point group have the same order of 2.

Answer

It is clear that when the reflection operator  acts on a shape twice, it sends the shape into itself. The same goes for  and . Hence, .

 

As mentioned in an earlier article, the similarity transformation of a matrix  to a matrix  leaves the trace of , which is defined as , invariant. This implies that elements of the same class in a group have the same trace.

 

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Group representations

A representation of a group  is a collection of square matrices that multiply according to the multiplication table of the group.

Consider the multiplication table for the point group :

Clearly, the collections of  matrices  and  are representations of the  point group because they multiply according to the above multiplication table:

An example of a representation of the point group consisting of matrices is:

It is easy to verify that the elements of  multiply according to the multiplication table of .

These three representations of  can be summarised as follows:

Question

Show that there are three classes for the  point group.

Answer

This is easily accomplished by inspecting the elements of and noting that group elements of the same class have the same trace.

 

The dimension of a representation refers to the common order of its square matrices, e.g. the dimensions of  and  are 1 and 2 respectively. A particular element of a representation is denoted by , where  refers to the representation and  refers to the corresponding symmetry operator. For example, . The matrix element of a particular element of a representation is denoted by , e.g. .

There are other representations of the  point group, e.g.  with the following elements:

If we inspect these matrices, we realise that they are of the same form, in the sense that we can group the matrix elements into submatrices that lie along the diagonals as illustrated. We call such submatrices: blocks, and the matrices containing blocks along their diagonals: block diagonal matrices.

The consequence of the submatrices in each of the  matrices being isolated by zeros is that the smaller submatrices in the multiplication of any two  matrices do not interfere with the larger submatrices. Since the collection of elements in the 1×1 blocks is the same as the collection of elements of , and the 2×2 submatrices of the bigger blocks are the same as the collection of elements of , we can conclude that the collection of the  matrices multiply according to the multiplication table of  without actually having to work out all the multiplications.

Another consequence of the way block diagonal matrices multiply with one another is that an infinite number of representations can be formed by adding elements of and  to the matrices of  to form larger matrices. For example, the addition of the elements of  to the matrices gives , with the following elements:

We call this matrix operation of adding blocks to form a larger block diagonal matrix: direct sum, which is denoted by the symbol . So, and .

With an infinite number of representations, we have to figure out which handful of representations of a group is sufficient for classifying molecules by symmetry and for analysing molecular properties. To do so, we first need to understand the difference between a reducible representation and an irreducible representation.

 

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Reducible and irreducible representations

A reducible representation is a group representation whose elements either have the same block diagonal matrix form or can undergo similarity transformations with the same invertible matrix to form block diagonal matrices of the same form.

Consider the following representations of the  point group:

By inspection, all the elements of have the same block diagonal matrix form of . Therefore,  is a reducible representation of the  point group.

Let’s consider another representation  of the  point group:

The elements do not have the same block diagonal matrix form. However, all of them undergo similarity transformations  with the same invertible matrix , where  and  to give . Hence,  is also a reducible representation of the  point group. Representations that are associated with a similarity transformation are called equivalent representations, i.e.  is equivalent to . It is evident that the elements of a reducible representation may not be in the same block diagonal form, and will only have this form if the appropriate basis is chosen.

A final point about reducible representations is that an element of a reducible representation of a group  is composed of the direct sum of the matrices of other representations of  that correspond to the same element of . For example,  of the  point group is a result of the direct sum of  and . In other words, a reducible representation can be decomposed or reduced to representations of lower dimensions.

An irreducible representation is a group representation whose elements cannot undergo similarity transformations with the same invertible matrix to form block diagonal matrices of the same form. Hence, an irreducible representation cannot be decomposed or reduced further to a representation of lower dimension. and are examples of irreducible representations of the point group. Every point group has a trivial, one-dimensional irreducible representation with each element being 1.

 

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Unitary representation

A unitary representation of a group consists of elements that are unitary matrices.

Every representation of a group can be described in terms of unitary matrices. Specifically, matrices of a representation of a group can be expressed as unitary matrices via a common similarity transformation without any loss of generality. The proof is as follows:

Consider a group , where each element is an  matrix. Let’s construct a new matrix  out of the elements of , where .

Question

Proof the matrix identity .

Answer

 

Using the identity mentioned above,

Therefore,  is a Hermitian matrix, which can be diagonalised by a unitary matrix , i.e. . Using  and the above identity again, we have,

or

where .

The diagonal elements of  are  and hence, the diagonal elements of  are . Moreover, one of the elements  of  is the identity matrix, with  and . So, .

Question

Why is ?

Answer

implies that is a complex number. The modulus of a complex number is . Hence, .

 

Let  and  be

where ,  and .

Consider a new set of matrices , where . Since  is diagonal and  is real and positive, we have . Therefore,  and

Substitute eq1 in the above equation and changing the dummy index from  to ,

Question

Show that the set  is also a representation of .

Answer

If the set  is also a representation of , its elements must multiply according to the multiplication table of . Since, , we have . The third equality ensures that the closure property of  is satisfied for the set  and hence the set . In other words, the elements of  multiply according to the multiplication table of .

If , then . Since, , the only possibility is that  for . Therefore, the set  has the identity element.

To show that each  has an inverse, we have

Finally, the associativity property of the group is evident, due to the fact that the set consists of matrices, which are associative.

 

Since the set  is also a representation of , we can express eq2 as:

Question

Explain why the 2nd equality in the above equation is valid.

Answer

According to the rearrangement theorem, each summand in  is a unique element of , which is denoted by . Therefore, the 2nd equality in the equation before the Q&A holds.

 

Repeating the steps from eq2 for , we have . Therefore,  is a unitary matrix.

Since ,  and , we have

In other words, every element  of a representation of  can undergo a similarity transformation that results in , which is unitary. This is necessary in proving Schur’s first lemma and Schur’s second lemma.

Question

Show that the set  is also a representation of .

Answer

If the set  is also a representation of , its elements must multiply according to the multiplication table of . Since , we have

The third equality ensures that the closure property of  is satisfied for the set  and hence the set . In other words, the elements of  multiply according to the multiplication table of .

 

 

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Elementary row operation and elementary matrix

An elementary row operation is a linear transformation , where the transformation matrix  performs one of the following on :

If is the identity matrix , the transformed matrix is called an elementary matrix, which is denoted by  in place of . In other words, an elementary matrix  is a square matrix that is related to an identity matrix by a single elementary row operation.

For example,

are elementary matrices, where  and  are obtained from  by

Type 1. Swapping rows 1 and 2 of .
Type 2. Multiplying row 2 of by 7
Type 3. Adding 4 times row 2 of to row 1 of

respectively.

Interestingly,  itself is a transformation matrix if  because . Therefore, when we multiply  by a matrix , we are performing an elementary row operation on . For example,

An elementary matrix of dimension  has an inverse if , where the inverse  is a matrix that reverses the transformation carried out by . Every elementary matrix has an inverse because

Type 1. Two successive row swapping operations of a matrix  returns , i.e. . Comparing  with , we have .

Type 2. It is always possible to satisfy  when  and  differ by one diagonal matrix element , with ,  and .

Type 3. It is always possible to satisfy  when  and differ by one matrix element , where  and  with , ,  and .

Thus, all elementary matrices have corresponding inverses, which are themselves elementary matrices. For example, the inverses of  and  are

Finally, a non-singular matrix can always be expressed as a product of elementary matrices. The proof is as follows:

Let . Since every elementary matrix is non-singular, we can multiply the inverses of the elementary matrices successively on the left of  to give:

Similarly, we can multiply the inverses of the elementary matrices successively on the right of  to give:

Combining eq3 and eq4, we have , where , which completes the proof.

 

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Determinant of a matrix

The determinant is a number associated with an  matrix . It is defined as

where

    1. is an element of in the first row of .
    2. is the cofactor associated with .
    3. , the minor of the element , is the determinant of the matrix obtained by removing the  row and -th column of .

In the case of , we say that the summation is a cofactor expansion along row 1. For example, the determinant of  is

For any square matrix, the cofactor expansion along any row and any column results in the same determinant, i.e.

To prove this, we begin with the proof that the cofactor expansion along any row results in the same determinant, i.e.  for . Consider a matrix , which is obtained from  by swapping row  consecutively with rows above it  times until it resides in row 1. According to property 8 (see below), we have

According to property 10, and therefore, the cofactor expansion along any column also results in the same determinant. This concludes the proof.

In short, to calculate the determinant of an matrix, we can carry out the cofactor expansion along any row or column. If we expand along a row, we have . We then select any row  to execute the summation. Conversely, if we expand along a column, we get .

The following are some useful properties of determinants:

    1. , where is the identity matrix. If one of the diagonal elements of is , then .
    2. If the elements of one of the columns of are all zero, .
    3. If is obtained from  by multiplying the -th row of  by , .
    4. If is obtained from  by swapping two rows or columns of , then
    5. If two rows or two columns of are the same, .
    6. The inverse of a matrix exists only if .
    7. If , then .
    8. If , then . If , then .
    9. If is diagonal, then .

 

Proof of property 1

We shall proof this property by induction.

For ,

For ,

Let’s assume that for , . Then for ,

We can repeat the above induction logic to prove that  if one of the diagonal elements of is .

 

Proof of property 2

Again, we shall proof this property by induction.

For ,

For ,

Let’s assume that for , we have . Then for ,

 

Proof of property 3

For , where , we have . For , the definition allows us to sum by row or by column. Suppose we sum by row, we have . Since we are allowed to choose any column  to execute the summation, we can always select the column  such that . Therefore,  if the elements of one of the columns of  are all zero.

 

Proof of property 4

Let’s suppose  is obtained from  by multiplying the -th row of  by . If we expand  and  along row , cofactor  is equal to cofactor . Therefore,

 

Proof of property 5

For a type I elementary matrix,  transforms  by swapping two rows of . So,  due to property 8. Since  is obtained from by swapping two rows of , we have  according to property 1 and property 8, which implies that . Therefore, .

For a type II elementary matrix,  due to property 4 and  because of property 1. So, .

For a type III elementary matrix,

is computed by expanding along row . The equation  means that when  is computed by expanding along row , it has the same cofactor as when  is computed by expanding along row . This implies that . Since the definition of the determinant of  is , which in our case is equivalent to , we have . Thus , which according to property 9, gives:

Since ,

according to eq5 and property 1.

Comparing eq5 and eq6, .

 

Proof of property 6

Case 1

If  is singular, where , then  is also singular according to property 12. So, .

Case 2

If  is non-singular, it can always be expressed as a product of elementary matrices: . So,

Since property 5 states that ,

Similarly, . Substitute this in the above equation, .

 

Proof of property 7

Using property 6 and then property 2,

 

Proof of property 8

We shall proof this property by induction.  is the trivial case, where is the rank of a square matrix.

For , let and , which is obtained from  by swapping two adjacent rows. Furthermore, let  and . Clearly,

Let’s assume that for , when two adjacent rows are swapped. For , we have:

Case 1: Suppose that the first row of  is not swapped when making .

is the determinant of a rank  matrix, which is the same as  except for two adjacent rows being swapped. Therefore,  and .

Case 2: If the first two rows of  are swapped when making ,

We have  and . The minors and  can be expressed as

where  is  with the first two rows, and the -th and -th columns removed.

Question

Why is each of the minors expressed as two separate sums?

Answer

The minor  is the determinant of a submatrix of with the first row and the -th column of  removed. If , the term with the Kronecker delta disappears and , where  is the determinant of  with the first two rows, and the -th and 1st columns removed. If , one of the columns between the 1st and the last columns of  is removed in forming the submatrix. Therefore, both summations are employed in determining , with the first from  to  and the second from  to . The two summations also apply to the case when . Finally, the same logic can be used to explain the formula for . You can validate the formulae of both minors using the example of:

 

Therefore,

For any pair of values of and , where , the terms in are , which differ from the terms in , i.e. , by a factor of -1. Similarly, for any pair of values of and , where , the terms in  are , which again differ from the terms in , i.e. , by a factor of -1. Since all terms in differ from all corresponding terms in  by a factor of -1, .

In general, the swapping of any two rows and of , where , is equivalent to the swapping of  adjacent rows of , with each swap changing  by a factor of -1. Therefore,

 

Question

How do we get ?

Answer

Firstly, we swap row  consecutively with each row below it until row is swapped, resulting in swaps. Then, swap the previous row , which now resides in what was row , consecutively with each row above it until it becomes what used to be row , resulting in swaps. These two actions combined are equivalent to the swapping of  with , with a total of  swaps of adjacent rows. The diagram below illustrates an example of the swaps:

 

Finally, the swapping of any two columns is proven in a similar way.

 

Proof of property 9

Consider the swapping of two equal rows of  to form , resulting in  and . However, property 8 states that  if any two rows of  are swapped. Therefore,  if two rows of  are equal. The same logic applies to proving  if there are two equal columns of .

 

Proof of property 10

Case 1:

If , then according to property 13. So, .

Case 2:

Let’s first consider elementary matrices . A type I elementary matrix is symmetrical about its diagonal, while a type II elementary matrix has one diagonal element equal to . Therefore,  and thus for type I or II elementary matrices. A type III elementary matrix is an identity matrix with one of the non-diagonal elements replaced by a constant . Therefore, if  is a type III elementary matrix, then  is also one. According to eq6,  for a type III elementary matrix. Hence,  for all elementary matrices.

Next, consider an invertible matrix , which (as proven in the previous article) can be expressed as . Thus,  (see Q&A in the proof of property 13). According to property 5,

and

Therefore, .

 

Proof of property 11

We have , or in terms of matrix components:

Consider the matrix  that is obtained from the matrix  by replacing the -th column of  with the -th column, i.e.  for and . According to property 9,  because  has two equal columns. Furthermore, cofactor is equal to cofactor  for . Therefore,

When , the last summation in eq8 becomes

Combining eq8 and eq9, we have , which when substituted in eq7 gives:

Therefore, , which implies that the inverse of a matrix is undefined if . In other words, the inverse of a matrix  is undefined if . We call such a matrix, a singular matrix, and a matrix with an associated inverse, a non-singular matrix.

 

Proof of property 12

We shall prove by contradiction. According to property 11,  has no inverse if . If  has no inverse and  has an inverse, then . This implies that  has an inverse , where , which contradicts the initial assumption that  has no inverse. Therefore, if  has no inverse, then  must also have no inverse.

 

Proof of property 13

Question

Show that .

Answer

because

because

which can be extended to .

 

Using the identity in the above Q&A, . If is invertible, then . This implies that  is the inverse of and therefore that is invertible if is invertible.

The last part shall be proven by contradiction. Suppose  is singular and  is non-singular, there would be a matrix  such that , Furthermore, , which implies that . This contradicts our initial assumption that  is singular. Therefore, if  is singular,  must also be singular.

 

Proof of property 14

We shall proof this property by induction. For ,

Let’s assume that for . Then for , the cofactor expansion along the first row is

 

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Schur’s first lemma

Schur’s first lemma states that a non-zero matrix that commutes with all matrices of an irreducible representation of a group is a multiple of the identity matrix.

The proof of Schur’s first lemma involves the following steps:

    1. Consider a representation of a group , i.e. , where each element of is an  matrix, which can be regarded as a unitary matrix  according to a previous article.
    2. Proof that a Hermitian matrix that commutes with the irreducible representation element , where , is a constant multiple of the identity matrix.
    3. Infer from step 2 that any arbitrary non-zero matrix that commutes with the irreducible representation element  is a multiple of the identity matrix.

Step 1 is self-explanatory. For step 2, we begin with a Hermitian matrix  that commutes with :

Multiplying the above equation on the left and right by  and  respectively,

Since

or

where .

Question

Show that is also a representation of .

Answer

If  is also a representation of , its elements must multiply according to the multiplication table of . Since , we have

The third equality ensures that the closure property of  is satisfied for  and hence . In other words, the elements of  multiply according to the multiplication table of .

 

As a Hermitian matrix can undergo a similarity transformation by a unitary matrix to give another Hermitian matrix  which is diagonal, i.e. , we have

Rewriting  in terms of its matrix elements, we have  or , which can be rearranged to

Consider the following cases for the above equation:

Case 1: All diagonal elements of are distinct, i.e.  if .

We have  for , which means that all off-diagonal elements of are zero. In other words, is an element of a reducible representation that is a direct sum of elements of one-dimensional matrix representations. Furthermore, the definition of a reducible representation implies that  is also an element of a reducible representation of  because .

Case 2: All diagonal elements of  are equal, i.e. .

can be any finite number, and consequently  may be either an element of a reducible or an irreducible representation. However, the diagonal matrix  must be a multiple of the identity matrix if .

Case 3: Some but not all diagonal elements of  are equal.

Instead of considering all possible permutations of equal and unequal diagonal entries in , we rearrange the columns of  such that equal diagonal entries of  are in adjacent columns of . This is always possible as the order of the columns of  corresponds to the order of the diagonal entries in  (see this article). Let’s suppose the first  diagonal entries are the same, while the rest are distinct, i.e. . With reference to Case 1 and Case 2,  must be an element of a reducible representation with the block diagonal form:

For example, if  in the following  matrix,

then  can be any finite number, while all other off-diagonal elements are zero.

Combining all three cases, if  is an irreducible representation, the diagonal matrix  must be a multiple of the identity matrix. Since , where  is Hermitian, we have proven step 2.

For the last step, let’s consider an arbitrary non-zero matrix  that commutes with :

Since  is unitary,  and so , which when multiplied from the left and right by  gives . This implies that if commutes with , then also commutes with .

Question

i) Show that if  and commutes with , then any linear combination of  and  also commutes with .
ii) Show that the linear combinations  and are Hermitian.
iii) Show that .

Answer

i)

ii)



iii) Substitute and in , we get .

 

With reference to step 2, must be a constant multiple of the identity matrix and so must . Therefore,  is also a constant multiple of the identity matrix. This concludes that proof of Schur’s first lemma, which together with Schur’s second lemma, is used to proof the great orthogonality theorem.

 

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