Developing Intuition for Eigenvalues and Eigenvectors in Matrices

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In summary, when it comes to finding eigenvalues and eigenvectors for matrices, there is no easy way or intuition. However, there are some patterns that can be observed, such as the fact that triangular matrices have eigenvalues on the main diagonal. Ultimately, the eigenpairs contain all the information about the matrix and solving for them is the most challenging question. Some useful knowledge to keep in mind is that real symmetric and hermitian matrices have real eigenvalues and the sum and product of the eigenvalues have specific relationships to the matrix's trace and determinant, respectively.
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I do not have a specific problem to show, but was wondering if someone could give tips on how to see or develop intuition on those eigenvectors for (2x2 and 3x3) matricies, i.e. which are the cases where they are obvious and how to see it, other than diagonal matrices (i.e. only diagonal is non-zero from upper left to lower right).

Thanks.
 
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Well, a triangular matrix (one that has only zeroes below the main diagonal [or only zeroes above the main diagonal) also has its eigenvalues on the main diagonal. Other than that, solve the eigenvalue equation!
 
  • #3
For a general matrix, there is no "intuition" about finding eigenvalues and eigenvectors.

In one sense, finding all the eigenpairs is the hardest question that can be asked about a general matrix. If you know all the eigenpairs, then you can easily transform the matrix into diagonal form, and any other question you can ask about it becomes trivial. In other words, the eigenpairs contain "all the information you can possibly know" about the matrix in an easy-to-use form - but there's no such thing as a free mathematical lunch!
 
  • #4
That's a very nice way of putting it AlephZero. Essentially, matrices are linear maps, and they're only ever determined up to conjugacy, and over C at least, this is completely determined by its Jordan Canonical form, which is precisely the information of its eigenvalues and dimensions of eigenspaces.

Of course, some intuition, or knowledge, is useful - a real symmetric matrix, or a hermitian matrix, has real eigenvalues. The sum of the eigenvalues is the trace, the product is the determinant (modulo some warnings about multiplicities).
 

Related to Developing Intuition for Eigenvalues and Eigenvectors in Matrices

What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts that are used to analyze linear systems. Eigenvalues refer to the scalar values that represent how a transformation changes a vector, while eigenvectors refer to the vectors that are unchanged by the transformation.

Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important because they provide insight into the behavior of linear systems. They can be used to understand the stability of a system, to find the direction of maximum change, and to simplify complex calculations.

How do you calculate eigenvalues and eigenvectors?

To calculate eigenvalues and eigenvectors, you need to first find the characteristic polynomial of the matrix. Then, you can use this polynomial to find the eigenvalues. Once you have the eigenvalues, you can find the corresponding eigenvectors by solving a system of equations.

What is the significance of the eigenvalues and eigenvectors?

The eigenvalues and eigenvectors of a matrix provide important information about the behavior of a linear system. They can tell us about the stability, growth, and oscillatory behavior of the system, and they can be used to simplify complex calculations.

How can eigenvalues and eigenvectors be applied in real-world situations?

Eigenvalues and eigenvectors have many practical applications in fields such as physics, engineering, and economics. They can be used to analyze the behavior of complex systems, to find the optimal direction for a process, and to simplify complex calculations in data analysis and machine learning.

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