Can Any Matrix Be Similar to Itself Without Being a Scalar Multiple?

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In summary, the question of whether we can always find a Q such that Q^{-1}BQ=A, given S^{-1}BS=A, depends on whether A is nonidentically similar to itself. In some special cases, such as when A is in Jordan normal form, we can find a family of transformations that satisfy this condition. This is relevant in representation theory and is known as Schur's lemma.
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td21
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If [itex]S^{-1}BS=A[/itex], can we always find Q ,such that [itex]Q^{-1}BQ=A[/itex]? Q different form S and no scalar multiple of S.
If not, what are some special cases in which we can find one?
i can only find one when A is nonidenityly similar to itself.
 
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This is equivalent to the question of when we can find a similarity transformation that leaves a matrix A invariant: P such that [itex]P^{-1}A P=A[/itex], with P not proportional to the identity ([itex]P=Q^{-1}S[/itex]). For complex matrices, the easiest way to answer the question is to put the matrix in Jordan normal form. The similarity transformations P are then a combination of:

i) Transformations that exchange the order of the Jordan blocks, and
ii) Self-similarity transformations within a single Jordan block.

The second type consists of all upper triangular matrices with entries constant on each diagonal, and nonzero on the main diagonal (so it's invertible). This creates quite a big family of transformations.

[This is often very relevant in the representation theory of groups and other algebraic structures when asking when there are P's which do this for many matrices simultaneously; look up Schur's lemma.]
 

1. What is a Matrix Similarity Question?

A Matrix Similarity Question is a type of question that involves comparing two matrices to determine if they are similar or not. This is often done by calculating the difference between the two matrices and determining if it falls within a certain threshold.

2. How do you determine if two matrices are similar?

To determine if two matrices are similar, you can use a variety of methods. One common method is to calculate the difference between the two matrices and see if it falls within a certain threshold. Another method is to compare the eigenvalues and eigenvectors of the two matrices.

3. What is the importance of Matrix Similarity in science?

Matrix Similarity is important in science because it allows for the comparison of data sets, which can help identify patterns and relationships. This can be useful in fields such as statistics, data analysis, and machine learning.

4. Can two matrices be similar but not equal?

Yes, two matrices can be similar but not equal. This means that they may have the same overall structure and properties, but some of the individual elements may be different. This is why it is important to use a threshold when determining matrix similarity.

5. How can Matrix Similarity be used in real-world applications?

Matrix Similarity has many real-world applications, such as in image and signal processing, pattern recognition, and data compression. It is also commonly used in fields such as biology, chemistry, and physics to compare and analyze large sets of data.

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