Orthonormalization using Matlab

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In summary, the conversation discusses the use of matlab's [V,D]=eig(A,B) function to find eigenvectors and eigenvalues, and the issue of the eigenvectors not being orthonormalized. The solution is to use external functions, such as the Gram-Schmidt orthonormalization function available on Matlab File Exchange.
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madeinmsia
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I'm using matlab's [V,D]=eig(A,B) function to find the eigenvectors and eigenvalues given two full matrices of A and B.

I know the eigenvectors that I get are not orthonormalized, so how do I do this?

Let's say I'm solving a simple Sturm-Liouville problem like [tex]\phi''(x)}+\lambda\sigma(x)\phi(x) = 0[/tex] where [tex]\sigma(x) = 1 - x^{2}[/tex].

The general solution that I have by formulae is

[tex]\phi_{n}(x)\cong\frac{1}{\sigma^{1/4}}sin[\lambda_{n}^{1/2}\int\sigma(s)^{1/2}ds], \lambda_{n}\cong\frac{(n\pi)^{2}}{(\int\sigma(s)^{1/2}ds)^{2})}[/tex]

When I compare the graph of the eigenfunction from my formula to the numerical eigenfunction I got, they are quite similar except it looks like it is missing some weighting function.
 
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What is orthonormalization?

Orthonormalization is a mathematical process used to transform a set of vectors into a new set that is both orthogonal and normalized. This means that all vectors in the new set are at right angles to each other and have a magnitude of 1.

Why is orthonormalization important?

Orthonormalization is important because it simplifies calculations and makes it easier to analyze data. It also helps to reduce the likelihood of errors in calculations.

How is orthonormalization performed using Matlab?

In Matlab, orthonormalization can be performed using the qr function. This function takes in a matrix as input and returns an orthonormal matrix and an upper triangular matrix. The orthonormal matrix can then be used as the basis for the new set of vectors.

Can orthonormalization be applied to any type of matrix?

Yes, orthonormalization can be applied to any type of matrix, whether it is square, rectangular, or even complex-valued. However, the resulting orthonormal matrix may not be unique for non-square matrices.

Are there any limitations to orthonormalization using Matlab?

One limitation of orthonormalization in Matlab is that it can be computationally expensive for large matrices. Additionally, for non-square matrices, the resulting orthonormal matrix may not be unique and may depend on the order of the columns in the original matrix.

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