Iterative methods for solving linear systems
Iterative methods for solving linear systems
A comparative study of sparse approximate inverse preconditioners
IMACS'97 Proceedings on the on Iterative methods and preconditioners
Incomplete Cholesky Factorizations with Limited Memory
SIAM Journal on Scientific Computing
Solving Sparse Symmetric Sets of Linear Equations by Preconditioned Conjugate Gradients
ACM Transactions on Mathematical Software (TOMS)
Efficient strategies for adaptive 3-D mesh generation over complex orography
Neural, Parallel & Scientific Computations
Robust Approximate Inverse Preconditioning for the Conjugate Gradient Method
SIAM Journal on Scientific Computing
Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
Numerical Approximation of Partial Differential Equations
Numerical Approximation of Partial Differential Equations
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Many environmental processes can be modelled as transient convection-diffusion-reaction problems. This is the case, for instance, of the operation of activated-carbon filters. For industrial applications there is a growing demand for 3D simulations, so efficient linear solvers are a major concern. We have compared the numerical performance of two families of incomplete Cholesky factorizations as preconditioners of conjugate gradient iterations: drop-tolerance and prescribed-memory strategies. Numerical examples show that the former are computationally more efficient, but the latter may be preferable due to their predictable memory requirements.