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

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  1. Van Niekerk, J., Bakka, H., Rue, H., Schenk, O. : New Frontiers in Bayesian Modeling Using the INLA Package in R (2021) not zbMATH
  2. Reguly, István Z.; Mudalige, Gihan R.: Productivity, performance, and portability for computational fluid dynamics applications (2020)
  3. Druinsky, Alex; Carlebach, Eyal; Toledo, Sivan: Wilkinson’s inertia-revealing factorization and its application to sparse matrices. (2018)
  4. Nandy, Arup; Jog, C. S.: A monolithic finite-element formulation for magnetohydrodynamics (2018)
  5. Scott, Jennifer; Tůma, Miroslav: A Schur complement approach to preconditioning sparse linear least-squares problems with some dense rows (2018)
  6. 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)
  7. Gould, Nicholas I. M.; Robinson, Daniel P.: A dual gradient-projection method for large-scale strictly convex quadratic problems (2017)
  8. Gould, Nicholas; Scott, Jennifer: The state-of-the-art of preconditioners for sparse linear least-squares problems (2017)
  9. Gupta, Anshul: Enhancing performance and robustness of ILU preconditioners by blocking and selective transposition (2017)
  10. Scott, Jennifer: On using Cholesky-based factorizations and regularization for solving rank-deficient sparse linear least-squares problems (2017)
  11. Wan, Wei; Biegler, Lorenz T.: Structured regularization for barrier NLP solvers (2017)
  12. Akay, H. U.; Oktay, E.; Manguoglu, M.; Sivas, A. A.: Improved parallel preconditioners for multidisciplinary topology optimisations (2016)
  13. Bolukbasi, Ercan Selcuk; Manguoglu, Murat: A multithreaded recursive and nonrecursive parallel sparse direct solver (2016)
  14. Hogg, Jonathan D.; Ovtchinnikov, Evgueni; Scott, Jennifer A.: A sparse symmetric indefinite direct solver for GPU architectures (2016)
  15. Jog, C. S.; Patil, Kunal D.: A hybrid finite element strategy for the simulation of MEMS structures (2016)
  16. Koric, Seid; Gupta, Anshul: Sparse matrix factorization in the implicit finite element method on petascale architecture (2016)
  17. 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)
  18. Badia, Santiago; Martín, Alberto F.; Príncipe, Javier: Enhanced balancing Neumann-Neumann preconditioning in computational fluid and solid mechanics (2013)
  19. Gould, Nicholas I. M.; Orban, Dominique; Robinson, Daniel P.: Trajectory-following methods for large-scale degenerate convex quadratic programming (2013)
  20. Hogg, Jonathan D.; Scott, Jennifer A.: An efficient analyse phase for element problems. (2013)

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