Eig(A'*A) - out of memory. Any shortcuts?

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SUMMARY

The discussion focuses on optimizing memory usage when calculating the eigenvalues of a large sparse matrix A using MATLAB. Key strategies include utilizing MATLAB's sparse matrix functionality, setting a variable to A'*A before clearing A, and executing MATLAB scripts with the "-nojvm" option to reduce memory overhead. Additionally, participants recommend ensuring the use of a 64-bit operating system and MATLAB version to handle larger datasets effectively.

PREREQUISITES
  • Understanding of MATLAB sparse matrices
  • Familiarity with eigenvalue algorithms
  • Knowledge of memory management in computational tasks
  • Experience with 64-bit operating systems
NEXT STEPS
  • Explore MATLAB's sparse matrix documentation
  • Learn about memory optimization techniques in MATLAB
  • Investigate the "-nojvm" command line option for MATLAB
  • Research best practices for handling large datasets in MATLAB
USEFUL FOR

Data scientists, mathematicians, and engineers working with large sparse matrices in MATLAB, particularly those looking to optimize memory usage during eigenvalue calculations.

mikeph
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A is pretty large (and sparse). I'd really like to be able to calculate this, using less RAM.
 
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I wouldn't call myself an expert on eigenvalue algorithms, but I had some ideas.

First, you've been using a MatLab http://www.mathworks.com/help/techdoc/ref/sparse.html" matrix for A, right?

Second, you could try setting a variable to A'*A, then clearing A, and then having a call to eig on that variable.

If you're on unix and you don't need the development environment, an option is to run a MatLab script with the "-nojvm" command line option.

Other than that, increase the swap size. If that's not enough, get the following if you aren't already using them for this task:

a 64 bit OS (and computer if needed)

64-bit MatLab
 
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Thanks for the suggestions
 

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