Harwell-Boeing sparse matrix collection

Sparse matrix test problems. We describe the Harwell-Boeing sparse matrix collection, a set of standard test matrices for sparse matrix problems. Our test set comprises problems in linear systems, least squares, and eigenvalue calculations from a wide variety of scientific and engineering disciplines. The problems range from small matrices, used as counter-examples to hypotheses in sparse matrix research, to large test cases arising in large-scale computation. We offer the collection to other researchers as a standard benchmark for comparative studies of algorithms. The procedures for obtaining and using the test collection are discussed. We also describe the guidelines for contributing further test problems to the collection.

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  1. Al-Baali, Mehiddin; Caliciotti, Andrea; Fasano, Giovanni; Roma, Massimo: A class of approximate inverse preconditioners based on Krylov-subspace methods for large-scale nonconvex optimization (2020)
  2. Pandur, Marija Miloloža: Preconditioned gradient iterations for the eigenproblem of definite matrix pairs (2019)
  3. Wang, Rui-Rui; Niu, Qiang; Tang, Xiao-Bin; Wang, Xiang: Solving shifted linear systems with restarted GMRES augmented with error approximations (2019)
  4. Wu, Tao; Gleich, David F.: Multiway Monte Carlo method for linear systems (2019)
  5. Arzani, F.; Peyghami, M. Reza: An approach based on dwindling filter method for positive definite generalized eigenvalue problem (2018)
  6. Gonzaga de Oliveira, Sanderson L.; Bernardes, Júnior A. B.; Chagas, Guilherme O.: An evaluation of low-cost heuristics for matrix bandwidth and profile reductions (2018)
  7. Mallach, Sven: Linear ordering based MIP formulations for the vertex separation or pathwidth problem (2018)
  8. Schlüter, Federico; Strappa, Yanela; Milone, Diego H.; Bromberg, Facundo: Blankets joint posterior score for learning Markov network structures (2018)
  9. Jacquelin, Mathias; Lin, Lin; Yang, Chao: \textttPselinv-- a distributed memory parallel algorithm for selected inversion, the symmetric case (2017)
  10. Ji, Hao; Li, Yaohang: A breakdown-free block conjugate gradient method (2017)
  11. Stachurski, Andrzej: On a conjugate directions method for solving strictly convex QP problem (2017)
  12. Wei, Wei; Dai, Hua: Implicitly restarted refined partially orthogonal projection method with deflation (2017)
  13. Zhu, Yao; Gleich, David F.; Grama, Ananth: Erasure coding for fault-oblivious linear system solvers (2017)
  14. Gu, Xian-Ming; Huang, Ting-Zhu; Carpentieri, Bruno: BiCGCR2: A new extension of conjugate residual method for solving non-Hermitian linear systems (2016)
  15. Hasan, Mahmudul; Hossain, Shahadat; Khan, Ahamad Imtiaz; Mithila, Nasrin Hakim; Suny, Ashraful Huq: DSJM: a software toolkit for direct determination of sparse Jacobian matrices (2016)
  16. Drummond, L. A.; Duff, Iain S.; Guivarch, Ronan; Ruiz, Daniel; Zenadi, Mohamed: Partitioning strategies for the block Cimmino algorithm (2015)
  17. Lecomte, Christophe: TRAX: an approach for the time rational analysis of complex dynamic systems (2015)
  18. Lubin, Miles; Dunning, Iain: Computing in operations research using Julia (2015)
  19. Zhong, Hong-xiu; Wu, Gang; Chen, Guo-liang: A flexible and adaptive simpler block GMRES with deflated restarting for linear systems with multiple right-hand sides (2015)
  20. Agullo, E.; Giraud, L.; Jing, Y.-F.: Block GMRES method with inexact breakdowns and deflated restarting (2014)

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