RSpectra: Solvers for Large-Scale Eigenvalue and SVD Problems

R interface to the 'Spectra' library <https://spectralib.org/> for large-scale eigenvalue and SVD problems. It is typically used to compute a few eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues, which is usually more efficient than eigen() if k << n. This package provides the 'eigs()' function that does the similar job as in 'Matlab', 'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function to calculate the largest k singular values and corresponding singular vectors of a real matrix. The matrix to be computed on can be dense, sparse, or in the form of an operator defined by the user.

Package details

AuthorYixuan Qiu [aut, cre], Jiali Mei [aut] (Function interface of matrix operation), Gael Guennebaud [ctb] (Eigenvalue solvers from the 'Eigen' library), Jitse Niesen [ctb] (Eigenvalue solvers from the 'Eigen' library)
MaintainerYixuan Qiu <[email protected]>
LicenseMPL (>= 2)
Version0.13-1
URL https://github.com/yixuan/RSpectra
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("RSpectra")

Try the RSpectra package in your browser

Any scripts or data that you put into this service are public.

RSpectra documentation built on May 23, 2018, 1:05 a.m.