Our lab need to do some simulation on online social networks. We need to handle a 5000*5000 adjacency matrix, namely, to get its largest eigenvalue and corresponding eigenvector. Matlab can be used but it is time-consuming. Is Spark effective in linear algebra calculations and transformations? Later we would have 5000000*5000000 matrix processed. It seems emergent that we should find some distributed computation platform.
I see SVD has been implemented and I can get eigenvalues of a matrix through this API. But when I want to get both eigenvalues and eigenvectors or at least the biggest eigenvalue and the corresponding eigenvector, it seems that current Spark doesn't have such API. Is it possible that I write eigenvalue decomposition from scratch? What should I do? Thanks a lot! Miles Yao -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/How-can-I-implement-eigenvalue-decomposition-in-Spark-tp11646.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
