A is similar to B is equivalent to A^k is similar to B^k?

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In summary: Thanks for clearing that up, it was a bit confusing.In summary, the statement is true if there exists an invertible matrix P such that B=P-1AP. However, I am not certain about the statement in the reverse direction.
  • #1
Raskolnikov
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Homework Statement


Given two matrices, [tex] A [/tex] and [tex] B.[/tex] Is the following statement true?

[tex] A [/tex] is similar to [tex] B [/tex] [tex] \Longleftrightarrow [/tex] [tex] A^k [/tex] is similar to [tex] B^k. [/tex]

Homework Equations


By definition, A and B are similar if there exists an invertible matrix P such that B = P-1AP.

The Attempt at a Solution



Clearly, the [tex] \Rightarrow [/tex] portion of the statement holds. For example, B2 = (P-1AP)(P-1AP) = P-1A(PP-1)AP = P-1A2P.

However, I am not certain about the statement in the reverse direction. I haven't spent a terrible amount of time on it, but I can't think of any counterexamples straight off the top of my head.

Any hints or suggestions? Thanks for your time.
 
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  • #2
I'm hesitant in replying to my own question, but I figured it out. My intuition was right: the two are not equivalent statements.

Let [tex]
A =
\left[ {\begin{array}{cc}
1 & -1 \\
1 & -1 \\
\end{array} } \right]
[/tex]. Then A^2 = 0. Let B=0. Then A^2 is similar to B^2. But A is not similar to B.
 
  • #3
Uhm, I don't think that logic is right.
I'm not sure whether or not similarity can be applied to the 0 matrix, but assuming it can;

As you said if A is similar to B, then [tex]B=P^{-1}AP[/tex] but remember that A is a diagonal matrix containing the eigenvalues of B.
Now if we consider this, we can see that [tex]A^{2}[/tex] is in fact similar to [tex]B^{2}[/tex] because both eigenvalues of [tex]B^{2}[/tex] will be 0 and [tex]A^{2}[/tex] is the 0 matrix, therefore technically it is a 'diagonal' matrix with eigenvalues of B along its diagonal.
Using this same logic, we can see that A IS in fact similar to B because both eigenvalues of A are also 0.

Now I'm hoping someone else will see this and confirm it but then again similarity might not apply to the 0 matrix in the same way that the 0 vector is not really an eigenvector. The main reason I question this is that if we use the 0 matrix, we get all eigenvalues being 0 and hence all eigenvectors being 0 (which are what make up the columns of P) and I would think that means that there isn't an invertable matrix P that exists and hence 0 is not similar to any matrix (using this same logic I'd think your matrix A isn't similar to anything either).
 
Last edited:
  • #4
As you said if A is similar to B, then [tex]B=P^{-1}AP[/tex] but remember that A is a diagonal matrix containing the eigenvalues of B.
The matrix A is diagonalizable if it is similar to a diagonal matrix, i.e., if [tex]P^{-1}AP[/tex] is a diagonal matrix. However, the condition A is similar to B by [tex]B=P^{-1}AP[/tex] in and of itself does not imply B is a diagonal matrix (or contains the eigenvalues of A).

Now if we consider this, we can see that [tex]A^{2}[/tex] is in fact similar to [tex]B^{2}[/tex] because both eigenvalues of [tex]B^{2}[/tex] will be 0 and [tex]A^{2}[/tex] is the 0 matrix, therefore technically it is a 'diagonal' matrix with eigenvalues of B along its diagonal.
Using this same logic, we can see that A IS in fact similar to B because both eigenvalues of A are also 0.

Now I'm hoping someone else will see this and confirm it but then again similarity might not apply to the 0 matrix in the same way that the 0 vector is not really an eigenvector. The main reason I question this is that if we use the 0 matrix, we get all eigenvalues being 0 and hence all eigenvectors being 0 (which are what make up the columns of P) and I would think that means that there isn't an invertable matrix P that exists and hence 0 is not similar to any matrix (using this same logic I'd think your matrix A isn't similar to anything either).

A is diagonalizable [tex] \Longleftrightarrow [/tex] A has n linearly independent eigenvectors.
However, that is not the case for the counterexample I provided. For [tex]

A =
\left[ {\begin{array}{cc}
1 & -1 \\
1 & -1 \\
\end{array} } \right]

[/tex] and B = 0, the zero vector is the only eigenvalue of A (it appears twice) and also clearly the only eigenvalue of B. Consequently, the sole eigenvector of A is v = <1,1>. Thus, A is not diagonalizable, i.e. it is not similar to a diagonal matrix.

I don't see why the zero matrix would not be subject to the same rules of similarity as other matrices. It's only special in the sense that the only matrix similar to the zero matrix is the zero matrix itself itself (hence my reason for choosing it for my counterexample).
 
  • #5
You are correct, the implication doesn't go both ways
 
  • #6
@Raskolnikov
Ah yep that makes sense.
 

FAQ: A is similar to B is equivalent to A^k is similar to B^k?

What does it mean for "A is similar to B"?

When we say that "A is similar to B," we mean that there is a relationship or resemblance between the two objects, often in terms of their characteristics or properties. This comparison suggests that A and B share some common features that make them alike.

How is similarity related to equivalence?

Similarity and equivalence are closely related concepts. When we say that "A is equivalent to B," we mean that A and B are essentially the same, despite potentially having some differences. This implies a stronger relationship than just similarity, as it suggests that A and B are interchangeable in certain contexts.

What does the exponent k represent in "A^k is similar to B^k"?

The exponent k represents the number of times that the base value, A or B, is multiplied by itself. This is known as the power or degree of the expression. In the context of similarity, raising both A and B to the same power allows for a more direct comparison of their properties or characteristics.

How is the concept of similarity applied in scientific research?

In scientific research, similarity is often used as a tool for comparison and analysis. By identifying similarities between different objects or phenomena, scientists can better understand their relationships and make predictions about their behavior. Similarity can also be used to group or categorize objects, which can be useful for organizing data and making sense of complex systems.

Are there any limitations to the statement "A is similar to B is equivalent to A^k is similar to B^k"?

While this statement can be useful for making comparisons and drawing conclusions, it is important to note that it is not always applicable in all situations. It assumes that A and B are similar in all aspects, which may not always be the case. Additionally, the value of k may also affect the degree of similarity between A and B. As with any scientific statement, it is important to consider the context and potential limitations before drawing conclusions.

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