WSMP

WSMP: A high-performance serial and parallel sparse linear solver. Watson Sparse Matrix Package (WSMP) is a collection of algorithms for efficiently solving large systems of linear equations whose coefficient matrices are sparse. This high-performance, robust, and easy-to-use software can be used as a serial package, or in a shared-memory multiprocessor environment, or as a scalable parallel solver in a message-passing environment, where each node can either be a uniprocessor or a shared-memory multiprocessor


References in zbMATH (referenced in 45 articles )

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  1. Druinsky, Alex; Carlebach, Eyal; Toledo, Sivan: Wilkinson’s inertia-revealing factorization and its application to sparse matrices. (2018)
  2. Nandy, Arup; Jog, C. S.: A monolithic finite-element formulation for magnetohydrodynamics (2018)
  3. Scott, Jennifer; Tůma, Miroslav: A Schur complement approach to preconditioning sparse linear least-squares problems with some dense rows (2018)
  4. Boutsidis, Christos; Drineas, Petros; Kambadur, Prabhanjan; Kontopoulou, Eugenia-Maria; Zouzias, Anastasios: A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix (2017)
  5. Gould, Nicholas I. M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  6. Gould, Nicholas; Scott, Jennifer: The state-of-the-art of preconditioners for sparse linear least-squares problems (2017)
  7. Gupta, Anshul: Enhancing performance and robustness of ILU preconditioners by blocking and selective transposition (2017)
  8. Scott, Jennifer: On using Cholesky-based factorizations and regularization for solving rank-deficient sparse linear least-squares problems (2017)
  9. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  10. Bolukbasi, Ercan Selcuk; Manguoglu, Murat: A multithreaded recursive and nonrecursive parallel sparse direct solver (2016)
  11. Hogg, Jonathan D.; Ovtchinnikov, Evgueni; Scott, Jennifer A.: A sparse symmetric indefinite direct solver for GPU architectures (2016)
  12. Jog, C. S.; Patil, Kunal D.: A hybrid finite element strategy for the simulation of MEMS structures (2016)
  13. Amestoy, Patrick; Ashcraft, Cleve; Boiteau, Olivier; Buttari, Alfredo; L’Excellent, Jean-Yves; Weisbecker, Clément: Improving multifrontal methods by means of block low-rank representations (2015)
  14. Badia, Santiago; Martín, Alberto F.; Príncipe, Javier: Enhanced balancing Neumann-Neumann preconditioning in computational fluid and solid mechanics (2013)
  15. Gould, Nicholas I. M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)
  16. Hogg, Jonathan D.; Scott, Jennifer A.: An efficient analyse phase for element problems. (2013)
  17. Hogg, Jonathan D.; Scott, Jennifer A.: Pivoting strategies for tough sparse indefinite systems (2013)
  18. Hogg, Jonathan; Scott, Jennifer: New parallel sparse direct solvers for multicore architectures (2013)
  19. Janna, Carlo; Ferronato, Massimilano; Gambolati, Giuseppe: Enhanced block FSAI preconditioning using domain decomposition techniques (2013)
  20. Ho, Kenneth L.; Greengard, Leslie: A fast direct solver for structured linear systems by recursive skeletonization (2012)

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