Proving the Diagonalizability of a Real 2x2 Matrix Using Invertible Matrices

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SUMMARY

The discussion focuses on proving the diagonalizability of a 2x2 real matrix A that cannot be diagonalized by any matrix P. It establishes that there exists an invertible real 2x2 matrix P such that P-1AP results in a Jordan form matrix of the structure <code>[[λ, 1], [0, λ]]</code>, indicating that A has a single eigenvalue and one eigenvector. Participants emphasize the importance of selecting a basis that includes the eigenvector and an orthogonal vector to facilitate the transformation.

PREREQUISITES
  • Understanding of eigenvalues and eigenvectors in linear algebra
  • Knowledge of Jordan canonical form and its significance
  • Familiarity with invertible matrices and their properties
  • Basic concepts of linear transformations and basis change
NEXT STEPS
  • Study the properties of Jordan forms in linear algebra
  • Learn about the process of changing bases in vector spaces
  • Explore the implications of having a single eigenvalue in 2x2 matrices
  • Investigate the role of orthogonal vectors in linear transformations
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Students and educators in linear algebra, mathematicians focusing on matrix theory, and anyone interested in advanced concepts of matrix diagonalization and transformations.

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Homework Statement


Let A be a 2x2 real matrix which cannot be diagonalized by any matrix P (real or complex). Prove there is an invertible real 2x2 matrix P such that

<br /> P^{-1}AP = \left( \begin{array}{cc} \lambda &amp; 1 \\ 0 &amp; \lambda \end{array} \right)<br />

I know how to diagonalize a matrix by using eigenvectors but I don't think that really helps here. I tried proving it by letting A be {a, b, c, d} and P be {e, f, g, h} but it gets really messy and I don't think that's the right way to do it. Any help appreciated!
 
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Well, it does help a little. It means you know that if a 2 by 2 matrix has two independent eigenvectors, then it can be diagonalized. And, of course, if the matrix has two distinct eigenvalues, then their eigenvectors are independent. Here, your matrix must have only one eigenvalue (which may be complex) and only one eigenvector. You might try this: choose your basis so that one of the basis vectors is that eigenvector and the other is orthogonal to it.
 
Thanks. So do you mean: view the transformation associated with matrix A in a basis of {eigenvector, orthogonal to eigenvector} and find the matrix for the transformation in this basis?
I'm not sure what the significance of the orthogonal vector is here.
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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