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RSVDPACK is a library of functions for computing low rank approximations ofmatrices. The library includes functions for computing standard (partial)factorizations such as the Singular Value Decomposition (SVD), and also socalled "structure preserving" factorizations such as the InterpolativeDecomposition (ID) and the CUR decomposition. The ID and CUR factorizationspick subsets of the rows/columns of a matrix to use as bases for its row/columnspace. Such factorizations preserve properties of the matrix such as sparsityor non-negativity, are helpful in data interpretation, and require in certaincontexts less memory than a partial SVD. The package implements highlyefficient computational algorithms based on randomized sampling, as describedand analyzed in [N. Halko, P.G. Martinsson, J. Tropp, "Finding structure withrandomness: Probabilistic algorithms for constructing approximate matrixdecompositions," SIAM Review, 53(2), 2011], and subsequent papers. Thismanuscript presents some modifications to the basic algorithms that improveperformance and ease of use. The library is written in C and supports bothmulti-core CPU and GPU architectures.

RSVDPACK: An implementation of randomized algorithms for computing the  singular value, interpolative, and CUR decompositions of matrices on  multi-core and GPU architectures