Proving Commutativity of Linear Transformations Using Schur Decomposition

In summary: Now consider the linear transformation T:V->V. By the assumption that T is upper triangular, there exists a base V such that T is expressed as J and T* is expressed as \bar{J}. In that case, T*T=JT. Furthermore, by the assumption that T* is an eigenvector of T*, we have that T*T*=JT*T. Thus, T*T=JT*T*=JT. Finally, by the assumption that T is an eigenvector of T*T*, we have that T*T*=JT*T*=JT. Thus, T*T=JT*T*=JT.In summary
  • #1
daniel_i_l
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Homework Statement


V is a unitarian space of finite dimensions and T:V->V is a linear transformation.
Every eigenvector of T is an eigenvector of T* (where (Tv,u) = (v,T*u) for all u and v in V).
Prove that T(T*) = (T*)T.

Homework Equations


The Attempt at a Solution


First of all, since the space in unitarian both T and T* can be expressed as a jordan matrix. It's easy to show that if J is the jordan matrix of T then [tex]\bar{J}[/tex] is the jordan matrix of T*. My idea is that since every eigenvector of T is an eigenvector of T* then there exists some base of V where T is expressed as J and T* is expressed as [tex]\bar{J}[/tex]. If that where true than it would be easy to answer the question
(Since then there would be a matrix M to that [tex] [T] = M^{-1}JM [/tex] ,
[tex][T^{*}] = M^{-1} \bar{J} M [/tex] and then
[tex] [T^{*}][T] = M^{-1} \bar{J} M M^{-1} J M = M^{-1} \bar{J} J M =
J M^{-1} \bar{J} M = [T][T^{*}] [/tex]
but I can't prove that such a base exists. In all the examples I've tried there's a base like that.
Is this the right direction? If so, how do I prove that a base exists?
Is there a better way to approach the problem?
Thanks.
 
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  • #2
Could you clarify what you mean by a unitarian space?
 
  • #3
A unitarian space is a vector space over the complex field with a defined inner-product.
 
  • #4
In that case it would be better to use Schur decomposition rather than Jordan decomposition. That is, get a unitary matrix U such that U*TU is upper triangular. Note that T commutes with T* iff U*TU commutes with (U*TU)*=U*T*U. Moreover, note that x is an eigenvector of T iff U*x is an eigenvector of U*TU. Thus we may assume without loss of generality that T itself is upper triangular. In this case T has (1,0,..,0) as an eigenvector. Consequently, so does T*. Proceed inductively to conclude that T must be diagonal. (We've essentially 'chopped off' the upper left corners of both T and T*.)
 

Related to Proving Commutativity of Linear Transformations Using Schur Decomposition

1. What is a Jordan form in linear algebra?

A Jordan form is a specific type of matrix that is used to represent a linear transformation or operator in linear algebra. It is a diagonal matrix with additional blocks of ones above the main diagonal, called Jordan blocks. These blocks represent the eigenvalues and eigenvectors of the transformation.

2. How is a Jordan form useful in linear algebra?

A Jordan form helps to simplify the representation of a linear transformation by breaking it down into its eigenvalues and eigenvectors. This can make it easier to analyze and understand the properties and behavior of the transformation. It is also useful for solving systems of linear equations and finding the characteristic polynomial of a matrix.

3. What is the process for finding the Jordan form of a matrix?

The process for finding the Jordan form of a matrix involves finding the eigenvalues and eigenvectors of the matrix, and then organizing them into Jordan blocks. This can be done through techniques such as diagonalization, triangularization, or the use of the Jordan canonical form algorithm.

4. Can any matrix be transformed into a Jordan form?

Yes, any square matrix can be transformed into a Jordan form. However, not all matrices have a unique Jordan form. Some may have multiple possible Jordan forms, while others may not have a Jordan form at all.

5. What are some applications of Jordan forms in real-world problems?

Jordan forms have several applications in real-world problems, particularly in fields such as physics, engineering, and economics. They can be used to model and analyze systems with multiple variables and interactions, such as in systems of differential equations. They are also useful in signal processing, image compression, and data analysis.

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