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I need to perform these three steps: - Find the inverse of a square symmetric matrix - Multiply the result with another square symmetric matrix - Finally, find the eigenvectors and eigenvalues of the resultant matrix

The python code:

S_i = np.linalg.inv(S1)
S_m = S_i*S2
evector, evalue = np.linalg.eig(S_m)

Is there an existing implementation of these steps in the Hadoop Map-Reduce framework?

I know about the SVD implementation in Mahout, and the inverse of a matrix can be computed using SVD provided there's an efficient multiplication implementation in Hadoop. Further, I could not find any eigenvector computation implementation for Hadoop.

Any suggestion/pointers will be helpful.

Tathagato Rai Dastidar
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1 Answers1

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To fit Eigen vector computation into MapReduce you might want to take a look at this: http://en.wikipedia.org/wiki/Divide-and-conquer_eigenvalue_algorithm

Also this answer here how to implement eigenvalue calculation with MapReduce/Hadoop? details how

PageRank solves the dominant eigenvector problem by iteratively finding the steady-state discrete flow condition of the network.

Community
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Abhiroop Sarkar
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