CSparse

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 187 articles , 1 standard article )

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  1. Bollhöfer, Matthias; Schenk, Olaf; Janalik, Radim; Hamm, Steve; Gullapalli, Kiran: State-of-the-art sparse direct solvers (2020)
  2. da Silva, André Renato Villela; Ochi, Luiz Satoru; da Silva Barros, Bruno José; Pinheiro, Rian Gabriel S.: Efficient approaches for the flooding problem on graphs (2020)
  3. Dellar, O. J.; Jones, B. Ll.: Efficient frequency response computation for low-order modelling of spatially distributed systems (2020)
  4. Groß, Michael; Dietzsch, Julian; Röbiger, Chris: Non-isothermal energy-momentum time integrations with drilling degrees of freedom of composites with viscoelastic fiber bundles and curvature-twist stiffness (2020)
  5. Li, Zheyuan; Wood, Simon N.: Faster model matrix crossproducts for large generalized linear models with discretized covariates (2020)
  6. Qin, Zhipeng; Riaz, Amir; Balaras, Elias: A locally second order symmetric method for discontinuous solution of Poisson’s equation on uniform Cartesian grids (2020)
  7. Vavasis, Stephen A.; Papoulia, Katerina D.; Hirmand, M. Reza: Second-order cone interior-point method for quasistatic and moderate dynamic cohesive fracture (2020)
  8. Wood, Simon N.: Inference and computation with generalized additive models and their extensions (2020)
  9. Zheng, Yang; Fantuzzi, Giovanni; Papachristodoulou, Antonis; Goulart, Paul; Wynn, Andrew: Chordal decomposition in operator-splitting methods for sparse semidefinite programs (2020)
  10. Acer, Seher; Kayaaslan, Enver; Aykanat, Cevdet: A hypergraph partitioning model for profile minimization (2019)
  11. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  12. Baharev, Ali; Neumaier, Arnold; Schichl, Hermann: A manifold-based approach to sparse global constraint satisfaction problems (2019)
  13. Bollhöfer, Matthias; Eftekhari, Aryan; Scheidegger, Simon; Schenk, Olaf: Large-scale sparse inverse covariance matrix estimation (2019)
  14. Cockayne, Jon; Oates, Chris J.; Ipsen, Ilse C. F.; Girolami, Mark: A Bayesian conjugate gradient method (with discussion) (2019)
  15. Cui, Yiran; Morikuni, Keiichi; Tsuchiya, Takashi; Hayami, Ken: Implementation of interior-point methods for LP based on Krylov subspace iterative solvers with inner-iteration preconditioning (2019)
  16. Curbelo, Jezabel; Duarte, Lucia; Alboussière, Thierry; Dubuffet, Fabien; Labrosse, Stéphane; Ricard, Yanick: Numerical solutions of compressible convection with an infinite Prandtl number: comparison of the anelastic and anelastic liquid models with the exact equations (2019)
  17. Devarakonda, Aditya; Fountoulakis, Kimon; Demmel, James; Mahoney, Michael W.: Avoiding communication in primal and dual block coordinate descent methods (2019)
  18. Dijoux, Loic; Fontaine, Vincent; Mara, Thierry Alex: A projective hybridizable discontinuous Galerkin mixed method for second-order diffusion problems (2019)
  19. Eberly, Wayne: Automating algorithm selection: checking for matrix properties that can simplify computations (2019)
  20. 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)

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