SVDPACK comprises four numerical (iterative) methods for computing the singular value decomposition (SVD) of large sparse matrices using double precision ANSI Fortran-77. A compatible ANSI-C version (SVDPACKC) is also available. This software package implements Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left- and right-singular vectors) for large sparse matrices. The package has been ported to a variety of machines ranging from supercomputers to workstations: CRAY Y-MP, CRAY-2S, Alliant FX/80, SPARCstation 10, IBM RS/6000-550, DEC 5000-100, and HP 9000-750. The development of SVDPACK wa motivated by the need to compute large rank approximations to sparse term-document matrices from information retrieval applications. Future updates to SVDPACK(C), will include out-of-core updating strategies, which can be used, for example, to handle extremely large sparse matrices (on the order of a million rows or columns) associated with extremely large databases in query-based information retrieval applications.

References in zbMATH (referenced in 60 articles , 1 standard article )

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  1. Aggarwal, Charu C.: Machine learning for text (2018)
  2. Akbari, Amir; Barton, Paul I.: An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks (2018)
  3. Schulze, Philipp; Unger, Benjamin: Model reduction for linear systems with low-rank switching (2018)
  4. Wu, Lingfei; Romero, Eloy; Stathopoulos, Andreas: PRIMME_SVDS: a high-performance preconditioned SVD solver for accurate large-scale computations (2017)
  5. Wang, Xuansheng; Glineur, François; Lu, Linzhang; Van Dooren, Paul: Extended Lanczos bidiagonalization algorithm for low rank approximation and its applications (2016)
  6. Wu, Lingfei; Stathopoulos, Andreas: A preconditioned hybrid SVD method for accurately computing singular triplets of large matrices (2015)
  7. Zhou, Xun; He, Jing; Huang, Guangyan; Zhang, Yanchun: SVD-based incremental approaches for recommender systems (2015)
  8. Mu, Tingting; Miwa, Makoto; Tsujii, Junichi; Ananiadou, Sophia: Discovering robust embeddings in (dis)similarity space for high-dimensional linguistic features (2014)
  9. Vecharynski, Eugene; Saad, Yousef: Fast updating algorithms for latent semantic indexing (2014)
  10. Pan, Weike; Yang, Qiang: Transfer learning in heterogeneous collaborative filtering domains (2013)
  11. Çivril, A.; Magdon-Ismail, M.: Column subset selection via sparse approximation of SVD (2012)
  12. Gandy, Silvia; Recht, Benjamin; Yamada, Isao: Tensor completion and low-(n)-rank tensor recovery via convex optimization (2011)
  13. Kuo, Yueh-Cheng; Lin, Wen-Wei; Shieh, Shih-Feng; Wang, Weichung: A hyperplane-constrained continuation method for near singularity in coupled nonlinear Schrödinger equations (2010)
  14. Chen, Jie; Fang, Haw-Ren; Saad, Yousef: Fast approximate (k)NN graph construction for high dimensional data via recursive Lanczos bisection (2009)
  15. Boutsidis, C.; Gallopoulos, E.: SVD based initialization: A head start for nonnegative matrix factorization (2008)
  16. Howell, Gary W.; Demmel, James; Fulton, Charles T.; Hammarling, Sven; Marmol, Karen: Cache efficient bidiagonalization using BLAS 2.5 operators. (2008)
  17. Hendrickson, Bruce: Latent semantic analysis and Fiedler retrieval (2007)
  18. Martin, Dian I.; Martin, John C.; Berry, Michael W.; Browne, Murray: Out-of-core SVD performance for document indexing (2007)
  19. Oweiss, Karim G.; Anderson, David J.: Tracking signal subspace invariance for blind separation and classification of nonorthogonal sources in correlated noise (2007)
  20. Aswani Kumar, Cherukuri; Srinivas, Suripeddi: Latent semantic indexing using eigenvalue analysis for efficient information retrieval (2006)

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