Small Eigenvalue Problems

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In summary, the individual is seeking advice on the best method for finding eigenvalues and eigenvectors of a small Hermitian matrix (7x7 or smaller) with bounded program memory. The expert suggests using root estimation algorithms such as Newton's method instead of attempting to solve the characteristic polynomial, as it would be time-consuming and less accurate. They also provide two resources to get started and mention that the package Eispack is a good option.
  • #1
nworm
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Dear experts!

I have a small Hermitian matrix (7*7 or smaller). I need to find all eigenvalues and eigenvectors of this matrix. The program memory is bounded.
What method is optimal in this case?
Can you give any e-links?

Thanks In Advance.
 
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  • #2
I remember learning some method for estimating eigenvalues then useing some kind of "binary search" to get closer to the true values, but I've forgotten what it was called.

There are a number of methods to obtain eigenvalues, or at least to accurately estimate them. Here's two pages I managed to scrouge up to get you started.

http://www.phys.au.dk/subatom/nucltheo/numeric/current/note6.htm
http://www.nasc.snu.ac.kr/sheen/nla/html/node20.html

You should not attempt to solve the characteristic polynomial. It will take too long and your answer won't be as accurate as other methods. This is because for a 7x7 matrix, the characteristic polynomial will be of order 7, and by Abel's theorem, there's no formula to get an exact answer. As such, you have to use root estimation algorithims like Newtons method. But don't. They will take far too long to converge.

I wish I could recall some other methods, but I don't often deal with matrices larger than 4x4.
 
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  • #3
Thank you very mach. Eispack is a very good package.
 

What is a small eigenvalue problem?

A small eigenvalue problem is a mathematical problem that involves finding the eigenvalues and corresponding eigenvectors of a small-sized matrix. The eigenvalues are scalars that represent the magnitude of the corresponding eigenvectors. In other words, a small eigenvalue problem is a way to understand the behavior of a matrix and its vectors in a simplified manner.

Why are small eigenvalue problems important?

Small eigenvalue problems are important because they can help us understand the behavior and characteristics of a matrix, which can have applications in various fields such as physics, engineering, and computer science. Additionally, solving small eigenvalue problems can also lead to solutions for larger, more complex problems.

How are small eigenvalue problems solved?

Small eigenvalue problems can be solved using various methods such as the power method, inverse iteration, and Jacobi's method. These methods involve finding the eigenvalues and eigenvectors through iterative processes and can be performed using computer algorithms.

What are the applications of small eigenvalue problems?

Small eigenvalue problems have various applications in different fields. In physics, they can help in understanding the behavior of quantum systems. In engineering, they can be used in structural analysis and control system design. In computer science, small eigenvalue problems can be applied in data analysis, image processing, and machine learning.

Are there any limitations to solving small eigenvalue problems?

Yes, there are some limitations to solving small eigenvalue problems. One of the main limitations is that they can only be applied to square matrices. Additionally, the accuracy of the solutions can be affected by rounding errors and the choice of algorithm used to solve the problem.

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