IFISS

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 253 articles , 1 standard article )

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  1. Danieli, Federico; Southworth, Ben S.; Wathen, Andrew J.: Space-time block preconditioning for incompressible flow (2022)
  2. Feng, Xiaobing; Luo, Yan; Vo, Liet; Wang, Zhu: An efficient iterative method for solving parameter-dependent and random convection-diffusion problems (2022)
  3. Wang, Na-Na; Li, Ji-Cheng: On parameterized block symmetric positive definite preconditioners for a class of block three-by-three saddle point problems (2022)
  4. Bespalov, Alex; Rocchi, Leonardo; Silvester, David: T-IFISS: a toolbox for adaptive FEM computation (2021)
  5. Botchev, Mike A.; Knizhnerman, Leonid; Tyrtyshnikov, Eugene E.: Residual and restarting in Krylov subspace evaluation of the (\varphi) function (2021)
  6. Khan, Arbaz; Bespalov, Alex; Powell, Catherine E.; Silvester, David J.: Robust a posteriori error estimation for parameter-dependent linear elasticity equations (2021)
  7. Liang, Zhao-Zheng; Dou, Yan: Efficient preconditioning techniques for velocity tracking of Stokes control problem (2021)
  8. Liang, Zhao-Zheng; Zhang, Guo-Feng: Robust preconditioning techniques for multiharmonic finite element method with application to time-periodic parabolic optimal control problems (2021)
  9. Liao, Li-Dan; Zhang, Guo-Feng; Wang, Xiang: Preconditioned iterative method for nonsymmetric saddle point linear systems (2021)
  10. Lin, Xue-lei; Ng, Michael: An all-At-once preconditioner for evolutionary partial differential equations (2021)
  11. Miao, Shu-Xin; Zhang, Jing; Meng, Lingsheng: A general Uzawa-type method for a class of (2\times2) block structure linear system (2021)
  12. Montoison, Alexis; Orban, Dominique: TriCG and TriMR: two iterative methods for symmetric quasi-definite systems (2021)
  13. Palitta, Davide: Matrix equation techniques for certain evolutionary partial differential equations (2021)
  14. Ramage, Alison; Ruiz, Daniel; Sartenaer, Annick; Tannier, Charlotte: Using partial spectral information for block diagonal preconditioning of saddle-point systems (2021)
  15. Rana, Md. Masud; Howle, Victoria E.; Long, Katharine; Meek, Ashley; Milestone, William: A new block preconditioner for implicit Runge-Kutta methods for parabolic PDE problems (2021)
  16. Steel, Thijs; Camps, Daan; Meerbergen, Karl; Vandebril, Raf: A multishift, multipole rational QZ method with aggressive early deflation (2021)
  17. Zeng, Min-Li: The RSS-like iteration method for block two-by-two linear systems from time-periodic parabolic optimal control problems (2021)
  18. Zhang, Jing; Miao, Shu-Xin: A general fast shift-splitting iteration method for nonsymmetric saddle point problems (2021)
  19. Badahmane, A.; Bentbib, A. H.; Sadok, H.: Preconditioned Krylov subspace and GMRHSS iteration methods for solving the nonsymmetric saddle point problems (2020)
  20. Cao, Yang: A block positive-semidefinite splitting preconditioner for generalized saddle point linear systems (2020)

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