Linear algerba: trace of square matrix is a linear functional

In summary, we discussed the definition of trace for square matrices and proved that it is a linear functional. We also explored the concept of linear functionals and how they relate to scalar products. We then discussed the properties that make a function into a linear functional and how the definition of trace satisfies these properties. Lastly, we looked at how the definition of trace can be extended to complex matrices and the additional complexities that arise in this case.
  • #1
skrat
748
8
Lets define trace for each square matrix [itex]A[/itex] its trace as sum of its diagonal elements, so [itex]tr_{n}(A)=\sum_{j=1}^{n}a_{j,j}[/itex]. Now proove that trace is a linear functional for all square matrix.

I would be happy to know what has to be true for anything to be a linear functional?

If I understand correctly, linear functional works on a vector but returns a real or complex number. So linear functional is a scalar product. Now what?
 
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  • #2
skrat said:
If I understand correctly, linear functional works on a vector but returns a real or complex number.
That is correct. The trace takes a matrix and returns a number. All matrices form a vector space, which you can show by checking the properties of a vector space. Or if you want to cheat a bit, you could write down all the entries of the matrix in one big vector and consider it an element of [itex]\mathbb{R}^{n^2}[/itex].

skrat said:
So linear functional is a scalar product. Now what?
I'm not sure what you mean by "scalar product", are you talking about the inner product [itex]\vec a \cdot \vec b = \sum a_i b_i[/itex]?

skrat said:
I would be happy to know what has to be true for anything to be a linear functional?
It has to satisfy that [itex]\operatorname{tr}_n(A + B) = \operatorname{tr}_n(A) + \operatorname{tr}_n(B)[/itex] and [itex]\operatorname{tr}_n(k A) = k \operatorname{tr}_n(A)[/itex] for all n x n matrices A, B and real numbers k. Those are the two properties that make an arbitrary function [itex]V \to \mathbb{R}[/itex] into a linear functional, see e.g. Wikipedia.
 
  • #3
CompuChip said:
I'm not sure what you mean by "scalar product", are you talking about the inner product [itex]\vec a \cdot \vec b = \sum a_i b_i[/itex]?

Exactly. That is how we defined linear functional.

So, you are trying to say that the following two rows are a complete proof that trace, defined as a sum of diagonal elements of square matrix, is a linear functional:
[itex]tr_{n}(A+B)=\sum_{j=1}^{n}(a_{j,j}+b_{j,j})=\sum_{j=1}^{n}a_{j,j}+\sum_{j=1}^{n}+b_{j,j}=tr_{n}(A)+tr_{n}(B)[/itex]
and
[itex]tr_{n}(\lambda A)=\sum_{j=1}^{n}\lambda a_{j,j}=\lambda \sum_{j=1}^{n}a_{j,j}=\lambda tr_{n}(A)[/itex]

Does anything change if [itex]\lambda \in \mathbb{C}[/itex]
 
  • #4
skrat said:
Exactly. That is how we defined linear functional.
Hmm, the scalar product is only a linear functional if you fix one of the vectors, e.g. for any fixed real vector [itex]\vec a[/itex] the functions
[tex]f_{\vec a}(\vec v): \mathbb{R}^n \to \mathbb{R}, \vec v \mapsto \vec a \cdot \vec v[/tex]
and
[tex]g_{\vec a}(\vec v): \mathbb{R}^n \to \mathbb{R}, \vec v \mapsto \vec v \cdot \vec a[/tex]
are both linear functionals (I'll leave it to you to prove it). The inner product [itex]\mathbb{R}^n \times \mathbb{R}^n \to \mathbb{R}[/itex] is what we call bilinear, where the bi- indicates that it is linear in both arguments.
skrat said:
So, you are trying to say that the following two rows are a complete proof that trace, defined as a sum of diagonal elements of square matrix, is a linear functional:
[itex]tr_{n}(A+B)=\sum_{j=1}^{n}(a_{j,j}+b_{j,j})=\sum_{j=1}^{n}a_{j,j}+\sum_{j=1}^{n}+b_{j,j}=tr_{n}(A)+tr_{n}(B)[/itex]
and
[itex]tr_{n}(\lambda A)=\sum_{j=1}^{n}\lambda a_{j,j}=\lambda \sum_{j=1}^{n}a_{j,j}=\lambda tr_{n}(A)[/itex]
Yes, that was what I was saying.

skrat said:
Does anything change if [itex]\lambda \in \mathbb{C}[/itex]
Not really, except that you have to make everything complex, e.g. since [itex]\lambda A[/itex] will be an n x n matrix with complex entries you have to extend the definition of trn those matrices. Note that the example of the scalar product becomes a bit more involved, as [itex]\vec v \cdot \vec w = \sum_i v_i^* w_i[/itex] has an additional complex conjugate compared to the real case.
 
  • #5
THANK YOU VERY MUCH. One more question:

How do I show that [itex]<A,B>=tr(AB^{*})[/itex] defines scalar product if A,B are both square matrix with complex elements.

Assuming * here means complex conjugation I started like this:

[itex]tr(AB^{*})=\sum_{i=1}^{n}[/itex][itex](\sum_{j=1}^{n}a_{i,j}[/itex][itex]\cdot {b_{j,i}^{*}}) [/itex] but how do i write [itex]<A,B>[/itex]?
 
  • #6
A scalar product (or inner product) should satisfy three properties, e.g. one of them is [itex]\langle A, A \rangle \ge 0[/itex] with equality if and only if A = 0. Can you give me the other two properties?
 
  • #7
I hope I can:

[itex]<A,B>=(<B,A>)^{*}[/itex] and [itex]<\lambda (A+B),C>=\lambda <A,C>+\lambda <B,C>[/itex]

So, the same goes for trace: [itex]tr(AA^{*})=0[/itex], [itex]tr(AB^{*})=tr(BA^{*})[/itex] and [itex]tr(\lambda (A+B^{*}))=\lambda tr(A)+\lambda tr(B^{*} )[/itex]?
 

Related to Linear algerba: trace of square matrix is a linear functional

1. What is a square matrix?

A square matrix is a matrix with an equal number of rows and columns. This means that it has the same number of elements in each row and column.

2. What is the trace of a square matrix?

The trace of a square matrix is the sum of the elements on the main diagonal (from the top left to the bottom right). It is denoted by tr(A) or Tr(A).

3. What is a linear functional?

A linear functional is a mathematical function that maps a vector space to its underlying field of scalars. In other words, it is a function that takes in a vector and returns a scalar value.

4. How is the trace of a square matrix a linear functional?

The trace of a square matrix can be thought of as a linear functional because it satisfies the properties of linearity. This means that it follows the rules of addition and scalar multiplication, making it a linear function.

5. What are some applications of the trace of a square matrix as a linear functional?

The trace of a square matrix as a linear functional has various applications in mathematics, physics, and engineering. It can be used to calculate the determinant and eigenvalues of a matrix, as well as in optimization problems and quantum mechanics.

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