Direct methods for sparse linear systems. Computational scientists often encounter problems requiring the solution of sparse systems of linear equations. Attacking these problems efficiently requires an in-depth knowledge of the underlying theory, algorithms, and data structures found in sparse matrix software libraries. Here, Davis presents the fundamentals of sparse matrix algorithms to provide the requisite background. The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages.par With a strong emphasis on MATLAB and the C programming language, Direct Methods for Sparse Linear Systems equips readers with the working knowledge required to use sparse solver packages and write code to interface applications to those packages. The book also explains how MATLAB performs its sparse matrix computations.

References in zbMATH (referenced in 150 articles , 1 standard article )

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  1. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  2. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)
  3. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  4. Gander, Martin J.; Zhang, Hui: A class of iterative solvers for the Helmholtz equation: factorizations, sweeping preconditioners, source transfer, single layer potentials, polarized traces, and optimized Schwarz methods (2019)
  5. Abo, Hirotachi; Vannieuwenhoven, Nick: Most secant varieties of tangential varieties to Veronese varieties are nondefective (2018)
  6. Babb, Tracy; Gillman, Adrianna; Hao, Sijia; Martinsson, Per-Gunnar: An accelerated Poisson solver based on multidomain spectral discretization (2018)
  7. Baumann, Manuel; Benner, Peter; Heiland, Jan: Space-time Galerkin POD with application in optimal control of semilinear partial differential equations (2018)
  8. Breckling, Sean; Neda, Monika; Pahlevani, Fran: A sensitivity study of the Navier-Stokes-(\alpha) model (2018)
  9. Druinsky, Alex; Carlebach, Eyal; Toledo, Sivan: Wilkinson’s inertia-revealing factorization and its application to sparse matrices. (2018)
  10. Eid, Ahmad Hosny: An extended implementation framework for geometric algebra operations on systems of coordinate frames of arbitrary signature (2018)
  11. Fareed, Hiba; Singler, John R.; Zhang, Yangwen; Shen, Jiguang: Incremental proper orthogonal decomposition for PDE simulation data (2018)
  12. Guinness, Joseph; Hammerling, Dorit: Compression and conditional emulation of climate model output (2018)
  13. Hager, William W.; Hungerford, James T.; Safro, Ilya: A multilevel bilinear programming algorithm for the vertex separator problem (2018)
  14. Harrison, A. P.; Joseph, D.: High performance rearrangement and multiplication routines for sparse tensor arithmetic (2018)
  15. Karakashian, O.; Collins, C.: Two-level additive Schwarz methods for discontinuous Galerkin approximations of the biharmonic equation (2018)
  16. Lungten, Sangye; Schilders, Wil H. A.; Maubach, Joseph M. L.: Threshold incomplete factorization constraint preconditioners for saddle-point matrices (2018)
  17. Montanelli, Hadrien; Nakatsukasa, Yuji: Fourth-order time-stepping for stiff PDEs on the sphere (2018)
  18. Moutafis, Byron E.; Filelis-Papadopoulos, Christos K.; Gravvanis, George A.: Parallel Schur complement techniques based on multiprojection methods (2018)
  19. Nhan, Thái Anh; MacLachlan, Scott; Madden, Niall: Boundary layer preconditioners for finite-element discretizations of singularly perturbed reaction-diffusion problems (2018)
  20. Selland Kleppe, Tore: Modified Cholesky Riemann manifold Hamiltonian Monte Carlo: exploiting sparsity for fast sampling of high-dimensional targets (2018)

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