text: The increasing number of applications requiring the solution of large scale singular value problems have rekindled interest in iterative methods for the SVD. Some promising recent ad- vances in large scale iterative methods are still plagued by slow convergence and accuracy limitations for computing smallest singular triplets. Furthermore, their current implementations in MATLAB cannot address the required large problems. Recently, we presented a preconditioned, two-stage method to effectively and accurately compute a small number of extreme singular triplets. In this research, we present a high-performance software, PRIMME SVDS, that implements our hybrid method based on the state-of-the-art eigensolver package PRIMME for both largest and smallest singular values. PRIMME SVDS fills a gap in production level software for computing the partial SVD, especially with preconditioning. The numerical experiments demonstrate its superior performance compared to other state-of-the-art software and its good parallel performance under strong and weak scaling.
  @cite_13: List of figures List of tables Preface 1. Introduction to ARPACK. Important features Getting started Reverse communication interface Availability Installation Documentation Dependence on LAPACK and BLAS Expected performance P_ARPACK Contributed additions Trouble shooting and problems 2. Getting started with ARPACK. Directory structure and contents Getting started An example for a symmetric Eigenvalue problem 3. General use of ARPACK. Naming conventions, Precisions, and types Shift and invert spectral transformation mode Reverse communication structure for shift-Invert Using the computational modes Computational modes for real symmetric problems Postprocessing for Eigenvectors using dseupd Computational modes for real nonsymmetric problems Postprocessing for Eigenvectors Using dneupd Computational modes for complex problems Postprocessing for Eigenvectors Using zneupd 4. The implicitly restarted Arnoldi method: structure of the Eigenvalue problem Krylov subspaces and projection methods The Arnoldi factorization Restarting the Arnoldi method The generalized Eigenvalue problem Stopping Criterion 5. Computational routines. ARPACK subroutines LAPACK routines used by ARPACK BLAS routines used by ARPACK Appendix A. Templates and driver routines Symmetric drivers Real Nonsymmetric drivers Complex drivers Band drivers The singular value decomposition Appendix B. Tracking the progress of ARPACK. Obtaining trace output Check-pointing ARPACK Appendix C. The XYaupd ARPACK Routines. DSAUPD DNAUPD ZNAUPD Bibliography Index.
summary: Despite accuracy limitations, eigenvalue iterative methods based on @math are widely used for computing the largest eigenpairs where the loss of accuracy is limited (see )) and even for low accuracy computations of the smallest singular values. For example, two popular packages in machine learning, scikit-learn http: scikit-learn.org stable modules generated sklearn.decomposition.TruncatedSVD.html and Spark's library MLib https: spark.apache.org docs 1.2.1 mllib-dimensionality-reduction.html , use a wrapper for the popular package ARPACK (implicit restarting Arnoldi method) @cite_13 . Other solvers for standard Hermitian eigenvalue problems can also be used. Table lists the most widely used eigensolver libraries with high-performance computing implementations.