An ANSI C code for sparse LU factorization is presented that combines a column pre-ordering strategy with a right-looking unsymmetric-pattern multifrontal numerical factorization. The pre-ordering and symbolic analysis phase computes an upper bound on fill-in, work, and memory usage during the subsequent numerical factorization. User-callable routines are provided for ordering and analyzing a sparse matrix, computing the numerical factorization, solving a system with the LU factors, transposing and permuting a sparse matrix, and converting between sparse matrix representations.\parThe simple user interface shields the user from the details of the complex sparse factorization data structures by returning simple handles to opaque objects. Additional user-callable routines are provided for printing and extracting the contents of these opaque objects. An even simpler way to use the package is through its MATLAB interface. UMFPACK is incorporated as a built-in operator in MATLAB 6.5 as $x= A^{-1} {\bold b}$ when $A$ is sparse and unsymmetric. (Source:

References in zbMATH (referenced in 302 articles , 1 standard article )

Showing results 1 to 20 of 302.
Sorted by year (citations)

1 2 3 ... 14 15 16 next

  1. Abdulla, Ugur G.; Bukshtynov, Vladislav; Hagverdiyev, Ali: Gradient method in Hilbert-Besov spaces for the optimal control of parabolic free boundary problems (2019)
  2. Arbogast, Todd; Tao, Zhen: A direct mixed-enriched Galerkin method on quadrilaterals for two-phase Darcy flow (2019)
  3. Bonito, Andrea; Lei, Wenyu; Pasciak, Joseph E.: Numerical approximation of the integral fractional Laplacian (2019)
  4. Cimrman, Robert; Lukeš, Vladimír; Rohan, Eduard: Multiscale finite element calculations in python using sfepy (2019)
  5. 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)
  6. Detommaso, Gianluca; Dodwell, Tim; Scheichl, Rob: Continuous level Monte Carlo and sample-adaptive model hierarchies (2019)
  7. Dodwell, T. J.; Ketelsen, C.; Scheichl, R.; Teckentrup, A. L.: Multilevel Markov Chain Monte Carlo (2019)
  8. Dörfler, Willy; Nürnberg, Robert: Discrete gradient flows for General curvature energies (2019)
  9. 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)
  10. Giannetti, F.; Camarri, S.; Citro, V.: Sensitivity analysis and passive control of the secondary instability in the wake of a cylinder (2019)
  11. Gläser, Dennis; Flemisch, Bernd; Helmig, Rainer; Class, Holger: A hybrid-dimensional discrete fracture model for non-isothermal two-phase flow in fractured porous media (2019)
  12. Hook, James; Pestana, Jennifer; Tisseur, Françoise; Hogg, Jonathan: Max-balanced Hungarian scalings (2019)
  13. Howse, Alexander J.; de Sterck, Hans; Falgout, Robert D.; MacLachlan, Scott; Schroder, Jacob: Parallel-in-time multigrid with adaptive spatial coarsening for the linear advection and inviscid Burgers equations (2019)
  14. Kou, Jisheng; Sun, Shuyu; Wu, Yuanqing: A semi-analytic porosity evolution scheme for simulating wormhole propagation with the Darcy-Brinkman-Forchheimer model (2019)
  15. Li, Ruipeng; Xi, Yuanzhe; Erlandson, Lucas; Saad, Yousef: The eigenvalues slicing library (EVSL): algorithms, implementation, and software (2019)
  16. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  17. Oyarzúa, Ricardo; Solano, Manuel; Zúñiga, Paulo: A high order mixed-FEM for diffusion problems on curved domains (2019)
  18. Prohl, Andreas; Schellnegger, Christian: Adaptive concepts for stochastic partial differential equations (2019)
  19. Rabault, Jean; Kuchta, Miroslav; Jensen, Atle; Réglade, Ulysse; Cerardi, Nicolas: Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control (2019)
  20. Solano, Manuel; Vargas, Felipe: A high order HDG method for Stokes flow in curved domains (2019)

1 2 3 ... 14 15 16 next