Eigen decomposition of a large spasre matrix

In summary, the conversation discusses the issue of finding eigenvalues and eigenvectors for a large sparse matrix in Matlab. The speaker has tried using the eigs function but encountered an out of memory error. They have 4GB of RAM and online help suggested using ARPACK or ARPACK++ with C/C++. The speaker asks for alternative means or suggestions for solving this issue. Another person mentions that typically sparse matrix methods are not possible if a sizable fraction of the eigenvalues is desired and suggests using a method that only returns the first few. They mention that Mathematica may have such methods implemented but they are not familiar with other software. The speaker states that they need at least the first 7 eigenvalues and corresponding eigenvectors, but
  • #1
Mecas
2
0
I have 12000 x 12000 R(data class single) sparse matrix, and I want to find its eigen values and vectors, but I can't do it in matlab, it gives out of memory error by doing
[V D] = eigs(R)
and I am using 4GB RAM , online help suggested using ARPACK or ARPACK++ with C/C++. please any alternative means or suggestion on how to solve this will be greatly appreciated .
 
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  • #2
How many eigenvalues do you want? Typically sparse matrix methods are not possible if you want a sizable fraction of the eigenvalues. You need to use a method that only returns the first few and is designed for sparse matrices.

I believe that Mathematica has such methods implemented but I have never used it myself. I don't know about other software.
 
  • #3
At least first 7, but I also need the eigenvectors as well, I have inquired about mathematica, I discovered I have to change the matrix R ( which is generated in matlab) into its format manually (quite impossible for a 12000 x 12000 matrix) which means there is not command that can easily do that in mathimatica. from research, its suggested to se numerical methods(iteration) for such large and sparse matrix but I don't how to go about it and that's where the arpack with fotran77 or lapack with C come in but I am not familiar with them.
 

FAQ: Eigen decomposition of a large spasre matrix

1. What is the purpose of eigen decomposition of a large sparse matrix?

Eigen decomposition, also known as eigendecomposition, is a mathematical process that breaks down a matrix into its constituent parts, known as eigenvalues and eigenvectors. This process is used to simplify calculations and analysis of large and complex matrices.

2. How does eigen decomposition help with analyzing large sparse matrices?

Eigen decomposition allows for the transformation of a large sparse matrix into a smaller but equivalent matrix. This smaller matrix is easier to analyze and can provide insights into the underlying structure and relationships within the original matrix.

3. What are the benefits of using eigen decomposition for large sparse matrices?

Eigen decomposition can help with dimensionality reduction, allowing for a more efficient representation of the original matrix. It also allows for the identification of important features and patterns within the data, making it useful for data analysis and machine learning tasks.

4. Are there any limitations to eigen decomposition of large sparse matrices?

One limitation is that eigen decomposition can only be applied to square matrices. Additionally, it may not be suitable for matrices with a large number of dimensions or with a high degree of sparsity, as the resulting smaller matrix may not accurately represent the original data.

5. How is eigen decomposition different from other matrix decomposition methods?

Eigen decomposition differs from other methods, such as singular value decomposition, in that it decomposes a matrix into its eigenvalues and eigenvectors, rather than singular values and unitary matrices. Additionally, eigen decomposition can only be applied to square matrices, while other methods may be applicable to non-square matrices.

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