HSL_MI28

This article focuses on the design and development of a new robust and efficient general-purpose incomplete Cholesky factorization package HSL_MI28, which is available within the HSL mathematical software library. It implements a limited memory approach that exploits ideas from the positive semidefinite Tismenetsky-Kaporin modification scheme and, through the incorporation of intermediate memory, is a generalization of the widely used ICFS algorithm of Lin and Moré. Both the density of the incomplete factor and the amount of memory used in its computation are under the user's control. The performance of HSL_MI28 is demonstrated using extensive numerical experiments involving a large set of test problems arising from a wide range of real-world applications. The numerical experiments are used to isolate the effects of scaling, ordering, and dropping strategies so as to assess their usefulness in the development of robust algebraic incomplete factorization preconditioners and to select default settings for HSL_MI28. They also illustrate the significant advantage of employing a modest amount of intermediate memory. Furthermore, the results demonstrate that, with limited memory, high-quality yet sparse general-purpose preconditioners are obtained. Comparisons are made with ICFS, with a level-based incomplete factorization code and, finally, with a state-of-the-art direct solver.

[1]  M. Benzi Preconditioning techniques for large linear systems: a survey , 2002 .

[2]  Daniil Svyatskiy,et al.  Interpolation-free monotone finite volume method for diffusion equations on polygonal meshes , 2009, J. Comput. Phys..

[3]  Yousef Saad,et al.  ILUT: A dual threshold incomplete LU factorization , 1994, Numer. Linear Algebra Appl..

[4]  Jennifer A. Scott,et al.  HSL_MA97 : a bit-compatible multifrontal code for sparse symmetric systems , 2011 .

[5]  Jennifer A. Scott,et al.  Pivoting strategies for tough sparse indefinite systems , 2013, TOMS.

[6]  Vivek Sarin,et al.  An Empirical Analysis of the Performance of Preconditioners for SPD Systems , 2012, TOMS.

[7]  Igor E. Kaporin,et al.  High quality preconditioning of a general symmetric positive definite matrix based on its UTU + UTR + RTU-decomposition , 1998, Numer. Linear Algebra Appl..

[8]  S. Sloan An algorithm for profile and wavefront reduction of sparse matrices , 1986 .

[9]  Timothy A. Davis,et al.  The university of Florida sparse matrix collection , 2011, TOMS.

[10]  Kincho H. Law,et al.  A robust incomplete factorization based on value and space constraints , 1995 .

[11]  A. Jennings,et al.  The solution of sparse linear equations by the conjugate gradient method , 1978 .

[12]  Timothy A. Davis,et al.  Algorithm 837: AMD, an approximate minimum degree ordering algorithm , 2004, TOMS.

[13]  Anshul Gupta,et al.  Adaptive Techniques for Improving the Performance of Incomplete Factorization Preconditioning , 2010, SIAM J. Sci. Comput..

[14]  T. Manteuffel An incomplete factorization technique for positive definite linear systems , 1980 .

[15]  Scott W. Sloan,et al.  A FORTRAN program for profile and wavefront reduction , 1989 .

[16]  Jennifer A. Scott,et al.  On Positive Semidefinite Modification Schemes for Incomplete Cholesky Factorization , 2014, SIAM J. Sci. Comput..

[17]  Miroslav Tůma,et al.  The importance of structure in incomplete factorization preconditioners , 2011 .

[18]  D. Ruiz A Scaling Algorithm to Equilibrate Both Rows and Columns Norms in Matrices 1 , 2001 .

[19]  Nicholas I. M. Gould,et al.  A numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations , 2007, TOMS.

[20]  Iain S. Duff,et al.  Strategies for Scaling and Pivoting for Sparse Symmetric Indefinite Problems , 2005, SIAM J. Matrix Anal. Appl..

[21]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[22]  Roland W. Freund,et al.  An Implementation of the Look-Ahead Lanczos Algorithm for Non-Hermitian Matrices , 1993, SIAM J. Sci. Comput..

[23]  Jorge J. Moré,et al.  Digital Object Identifier (DOI) 10.1007/s101070100263 , 2001 .

[24]  Mark T. Jones,et al.  An improved incomplete Cholesky factorization , 1995, TOMS.

[25]  Michele Benzi,et al.  Preconditioning Highly Indefinite and Nonsymmetric Matrices , 2000, SIAM J. Sci. Comput..

[26]  Bora Uçar,et al.  A Symmetry Preserving Algorithm for Matrix Scaling , 2014, SIAM J. Matrix Anal. Appl..

[27]  E. Cuthill,et al.  Reducing the bandwidth of sparse symmetric matrices , 1969, ACM '69.

[28]  John Reid,et al.  Ordering symmetric sparse matrices for small profile and wavefront , 1999 .

[29]  Daniil Svyatskiy,et al.  Monotone finite volume schemes for diffusion equations on unstructured triangular and shape-regular polygonal meshes , 2007, J. Comput. Phys..

[30]  Nicholas I. M. Gould,et al.  A numerical evaluation of HSL packages for the direct solution of large sparse, symmetric linear systems of equations , 2004, TOMS.

[31]  M. Tismenetsky,et al.  A new preconditioning technique for solving large sparse linear systems , 1991 .

[32]  Chih-Jen Lin,et al.  Incomplete Cholesky Factorizations with Limited Memory , 1999, SIAM J. Sci. Comput..

[33]  Wenbin,et al.  A High-Quality Preconditioning Technique for Multi-Length-Scale Symmetric Positive Definite Linear Systems , 2009 .

[34]  Iain S. Duff,et al.  On Algorithms For Permuting Large Entries to the Diagonal of a Sparse Matrix , 2000, SIAM J. Matrix Anal. Appl..

[35]  Mark T. Jones,et al.  Algorithm 740: Fortran subroutines to compute improved incomplete Cholesky factorizations , 1995, TOMS.

[36]  Alex Pothen,et al.  A Scalable Parallel Algorithm for Incomplete Factor Preconditioning , 2000, SIAM J. Sci. Comput..

[37]  Igor E. Kaporin,et al.  High quality preconditioning of a general symmetric positive definite matrix based on its U , 1998 .

[38]  J. Scott,et al.  A study of pivoting strategies for tough sparse indefinite systems , 2012 .

[39]  Jennifer A. Scott,et al.  The effects of scalings on the performance of a sparse symmetric indefinite solver , 2008 .

[40]  Gianmarco Manzini,et al.  Mimetic finite difference method for the Stokes problem on polygonal meshes , 2009, J. Comput. Phys..