Hi all,
This is a bit of a generic question, but I was hoping someone could provide some insight on how I could improve the precision of eigenvectors using "projection techniques", like in this post, where a matrix will have 1 or 2 very large eigenvalues, and the remaining eigenvalues are much smaller (by several orders of magnitude). They have code written in R, which I am not too familiar with, but is the idea generically that I should subtract out the overlap of smaller eigenvectors with those of larger eigenvectors to improve their precision? When I say precision, what I mean is that applying eig to a matrix M will generate a set of eigenvectors, but once I check M*v – *v, the resultant array will deviate from 0, i.e. applying M to the "eigenvector" has resulted in a linear combination of multiple other eigenvectors.
Any guidance is appreciated.Hi all,
This is a bit of a generic question, but I was hoping someone could provide some insight on how I could improve the precision of eigenvectors using "projection techniques", like in this post, where a matrix will have 1 or 2 very large eigenvalues, and the remaining eigenvalues are much smaller (by several orders of magnitude). They have code written in R, which I am not too familiar with, but is the idea generically that I should subtract out the overlap of smaller eigenvectors with those of larger eigenvectors to improve their precision? When I say precision, what I mean is that applying eig to a matrix M will generate a set of eigenvectors, but once I check M*v – *v, the resultant array will deviate from 0, i.e. applying M to the "eigenvector" has resulted in a linear combination of multiple other eigenvectors.
Any guidance is appreciated. Hi all,
This is a bit of a generic question, but I was hoping someone could provide some insight on how I could improve the precision of eigenvectors using "projection techniques", like in this post, where a matrix will have 1 or 2 very large eigenvalues, and the remaining eigenvalues are much smaller (by several orders of magnitude). They have code written in R, which I am not too familiar with, but is the idea generically that I should subtract out the overlap of smaller eigenvectors with those of larger eigenvectors to improve their precision? When I say precision, what I mean is that applying eig to a matrix M will generate a set of eigenvectors, but once I check M*v – *v, the resultant array will deviate from 0, i.e. applying M to the "eigenvector" has resulted in a linear combination of multiple other eigenvectors.
Any guidance is appreciated. eigenvector, eigenvalue, precision MATLAB Answers — New Questions

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