Algorithm 866: IFISS, a Matlab toolbox for modelling incompressible flow. IFISS is a graphical Matlab package for the interactive numerical study of incompressible flow problems. It includes algorithms for discretization by mixed finite element methods and a posteriori error estimation of the computed solutions. The package can also be used as a computational laboratory for experimenting with state-of-the-art preconditioned iterative solvers for the discrete linear equation systems that arise in incompressible flow modelling. A unique feature of the package is its comprehensive nature; for each problem addressed, it enables the study of both discretization and iterative solution algorithms as well as the interaction between the two and the resulting effect on overall efficiency. (Source: http://dl.acm.org/)

References in zbMATH (referenced in 161 articles , 1 standard article )

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  1. Benner, Peter; Bujanović, Zvonimir; Kürschner, Patrick; Saak, Jens: RADI: a low-rank ADI-type algorithm for large scale algebraic Riccati equations (2018)
  2. Benner, Peter; Qiu, Yue; Stoll, Martin: Low-rank eigenvector compression of posterior covariance matrices for linear Gaussian inverse problems (2018)
  3. Bertaccini, Daniele; Durastante, Fabio: Iterative methods and preconditioning for large and sparse linear systems with applications (2018)
  4. Elman, Howard C.; Silvester, David J.: Collocation methods for exploring perturbations in linear stability analysis (2018)
  5. Elman, Howard C.; Su, Tengfei: A low-rank multigrid method for the stochastic steady-state diffusion problem (2018)
  6. Huang, Na; Ma, Chang-Feng: On the eigenvalues of the saddle point matrices discretized from Navier-Stokes equations (2018)
  7. Huang, Na; Ma, Chang-Feng; Zou, Jun: Spectral analysis, properties and nonsingular preconditioners for singular saddle point problems (2018)
  8. Huang, Yunying; Chao, Zhen; Chen, Guoliang: Spectral properties of the matrix splitting preconditioners for generalized saddle point problems (2018)
  9. Huang, Zhuo-Hong; Huang, Ting-Zhu: Semi-convergence analysis of the GSS iteration methods for singular saddle point problems (2018)
  10. Huang, Zhuo-Hong; Huang, Ting-Zhu: A modified generalized shift-splitting method for nonsymmetric saddle point problems (2018)
  11. Ibeid, Huda; Yokota, Rio; Pestana, Jennifer; Keyes, David: Fast multipole preconditioners for sparse matrices arising from elliptic equations (2018)
  12. Ke, Yi-Fen; Ma, Chang-Feng: A new relaxed splitting preconditioner for the generalized saddle point problems from the incompressible Navier-Stokes equations (2018)
  13. Kooij, Gijs L.; Botchev, Mike A.; Geurts, Bernard J.: An exponential time integrator for the incompressible Navier-Stokes equation (2018)
  14. Lee, Kookjin; Carlberg, Kevin; Elman, Howard C.: Stochastic least-squares Petrov-Galerkin method for parameterized linear systems (2018)
  15. Liang, Zhao-Zheng; Axelsson, Owe; Neytcheva, Maya: A robust structured preconditioner for time-harmonic parabolic optimal control problems (2018)
  16. Liang, Zhao-Zheng; Zhang, Guo-Feng: On nonlinear inexact Uzawa algorithms for stabilized saddle point problems (2018)
  17. Liang, Zhao-Zheng; Zhang, Guo-Feng: Parameterized approximate block LU preconditioners for generalized saddle point problems (2018)
  18. Lungten, Sangye; Schilders, Wil H. A.; Maubach, Joseph M. L.: Threshold incomplete factorization constraint preconditioners for saddle-point matrices (2018)
  19. McDonald, Eleanor; Pestana, Jennifer; Wathen, Andy: Preconditioning and iterative solution of all-at-once systems for evolutionary partial differential equations (2018)
  20. Pearson, John W.; Pestana, Jennifer; Silvester, David J.: Refined saddle-point preconditioners for discretized Stokes problems (2018)

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Further publications can be found at: http://www.ma.man.ac.uk/~djs/ifiss/publist.html