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 241 articles , 1 standard article )

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  1. Bespalov, Alex; Rocchi, Leonardo; Silvester, David: T-IFISS: a toolbox for adaptive FEM computation (2021)
  2. Khan, Arbaz; Bespalov, Alex; Powell, Catherine E.; Silvester, David J.: Robust a posteriori error estimation for parameter-dependent linear elasticity equations (2021)
  3. Liang, Zhao-Zheng; Dou, Yan: Efficient preconditioning techniques for velocity tracking of Stokes control problem (2021)
  4. Palitta, Davide: Matrix equation techniques for certain evolutionary partial differential equations (2021)
  5. Ramage, Alison; Ruiz, Daniel; Sartenaer, Annick; Tannier, Charlotte: Using partial spectral information for block diagonal preconditioning of saddle-point systems (2021)
  6. Steel, Thijs; Camps, Daan; Meerbergen, Karl; Vandebril, Raf: A multishift, multipole rational QZ method with aggressive early deflation (2021)
  7. Badahmane, A.; Bentbib, A. H.; Sadok, H.: Preconditioned Krylov subspace and GMRHSS iteration methods for solving the nonsymmetric saddle point problems (2020)
  8. Cao, Yang: A block positive-semidefinite splitting preconditioner for generalized saddle point linear systems (2020)
  9. Cao, Yang; Shi, Zhen-Quan; Shi, Quan: Regularized DPSS preconditioners for generalized saddle point linear systems (2020)
  10. Chen, Chen; Liao, Qifeng: ANOVA Gaussian process modeling for high-dimensional stochastic computational models (2020)
  11. Cheng, Guo; Li, Ji-Cheng: A relaxed upper and lower triangular splitting preconditioner for the linearized Navier-Stokes equation (2020)
  12. Elman, Howard C.; Su, Tengfei: A low-rank solver for the stochastic unsteady Navier-Stokes problem (2020)
  13. Fan, Hongtao; Zheng, Bing: Modified SIMPLE preconditioners for saddle point problems from steady incompressible Navier-Stokes equations (2020)
  14. Gao, Wen-Li; Li, Xi-An; Lu, Xin-Ming: On quasi shift-splitting iteration method for a class of saddle point problems (2020)
  15. Huang, Na: Variable parameter Uzawa method for solving a class of block three-by-three saddle point problems (2020)
  16. Ju, Lili; Leng, Wei; Wang, Zhu; Yuan, Shuai: Numerical investigation of ensemble methods with block iterative solvers for evolution problems (2020)
  17. Liang, Zhao-Zheng; Axelsson, Owe; Zhang, Guo-Feng: Efficient iterative solvers for a complex valued two-by-two block linear system with application to parabolic optimal control problems (2020)
  18. Li, Jun; Miao, Shu-Xin: A generalized SHSS preconditioner for generalized saddle point problem (2020)
  19. Li, Rui-Xia; Zhang, Guo-Feng: Preconditioned iterative methods for the convective FitzHugh-Nagumo equations (2020)
  20. Li, Rui-Xia; Zhang, Guo-Feng; Liang, Zhao-Zheng: Fast solver of optimal control problems constrained by Ohta-Kawasaki equations (2020)

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