Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
The Korean Journal of Computational & Applied Mathematics
A new data-mapping scheme for latency-tolerant distributed sparse triangular solution
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Parallel Computing - Special issue: Parallel computing in numerical optimization
GALAHAD, a library of thread-safe Fortran 90 packages for large-scale nonlinear optimization
ACM Transactions on Mathematical Software (TOMS)
Numerical performance of incomplete factorizations for 3D transient convection-diffusion problems
Advances in Engineering Software
A comparison of projective and direct solvers for finite elements in elastostatics
Advances in Engineering Software
On centroidal voronoi tessellation—energy smoothness and fast computation
ACM Transactions on Graphics (TOG)
Computational Optimization and Applications
Efficient solution for Galerkin-based polynomial chaos expansion systems
Advances in Engineering Software
A Subspace Minimization Method for the Trust-Region Step
SIAM Journal on Optimization
A Block FSAI-ILU Parallel Preconditioner for Symmetric Positive Definite Linear Systems
SIAM Journal on Scientific Computing
Efficient Preconditioner Updates for Shifted Linear Systems
SIAM Journal on Scientific Computing
A generalized Block FSAI preconditioner for nonsymmetric linear systems
Journal of Computational and Applied Mathematics
An active set truncated Newton method for large-scale bound constrained optimization
Computers & Mathematics with Applications
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We propose an incomplete Cholesky factorization for the solution of large-scale trust region subproblems and positive definite systems of linear equations. This factorization depends on a parameter p that specifies the amount of additional memory (in multiples of n, the dimension of the problem) that is available; there is no need to specify a drop tolerance. Our numerical results show that the number of conjugate gradient iterations and the computing time are reduced dramatically for small values of p. We also show that in contrast with drop tolerance strategies, the new approach is more stable in terms of number of iterations and memory requirements.