arXiv subjects

Man-Chung Yeung

Publications and source records attributed to Man-Chung Yeung.

7 recordsLinked to original sources

arXiv2022

A FEAST variant incorporated with a power iteration

We present a variant of the FEAST matrix eigensolver for solving restricted real and symmetric eigenvalue problems. The method is derived from a combination of a variant of the FEAST method, which employs two contour integrals per iteration, and a power subspace iteration process. Compared with the original FEAST method, our new method does not require that the search subspace dimension must be greater than or equal to the number of eigenvalues inside a search interval, and can deal with narrow search intervals more effectively. Empirically, the FEAST iteration and the power subspace iteration are in a mutually beneficial collaboration to make the new method stable and robust.

math.NA↗

arXiv2016

A Spectral Projection Preconditioner for Solving Ill Conditioned Linear Systems

We present a preconditioner based on spectral projection that is combined with a deflated Krylov subspace method for solving ill conditioned linear systems of equations. Our results show that the proposed algorithm requires many fewer iterations to achieve the convergence criterion for solving an ill conditioned problem than a Krylov subspace solver. In our numerical experiments, the solution obtained by the proposed algorithm is more accurate in terms of the norm of the distance to the exact solution of the linear system of equations.

math.NA↗

arXiv2016

On Solving Ill-Conditioned Linear Systems

This paper presents the first results to combine two theoretically sound methods (spectral projection and multigrid methods) together to attack ill-conditioned linear systems. Our preliminary results show that the proposed algorithm applied to a Krylov subspace method takes much fewer iterations for solving an ill-conditioned problem downloaded from a popular online sparse matrix collection.

math.NA↗

arXiv2015

A FEAST Algorithm with oblique projection for generalized eigenvalue problems

The contour-integral based eigensolvers are the recent efforts for computing the eigenvalues inside a given region in the complex plane. The best-known members are the Sakurai-Sugiura (SS) method, its stable version CIRR, and the FEAST algorithm. An attractive computational advantage of these methods is that they are easily parallelizable. The FEAST algorithm was developed for the generalized Hermitian eigenvalue problems. It is stable and accurate. However, it may fail when applied to non-Hermitian problems. In this paper, we extend the FEAST algorithm to non-Hermitian problems. The approach can be summarized as follows: (i) to construct a particular contour integral to form a subspace containing the desired eigenspace, and (ii) to use the oblique projection technique to extract desired eigenpairs with appropriately chosen test subspace. The related mathematical framework is established. We also address some implementation issues such as how to choose a suitable starting matrix and design good stopping criteria. Numerical experiments are provided to illustrate that our method is stable and efficient.

math.NA↗

arXiv2013

ML(n)BiCGStabt: A ML(n)BiCGStab variant with A-transpose

The 1980 IDR method plays an important role in the history of Krylov subspace methods. It started the research of transpose-free Krylov subspace methods. In this paper, we make a first attempt to bring back A-transpose to the research area by presenting a new ML(n)BiCGStab variant that involves A-transpose in its implementation. Comparisons of this new algorithm with the existing ML(n)BiCGStab algorithms will be presented.

math.NA↗

arXiv2011

An introduction to ML(n)BiCGStab

ML(n)BiCGStab is a Krylov subspace method for the solution of large, sparse and non-symmetric linear systems. In theory, it is a method that lies between the well-known BiCGStab and GMRES/FOM. In fact, when n = 1, ML(1)BiCGStab is BiCGStab and when n = N, ML(N)BiCGStab is GMRES/FOM where N is the size of the linear system. Therefore, ML(n)BiCGStab is a bridge that connects the Lanczos-based BiCGStab and the Arnoldi-based GMRES/FOM. In computation, ML(n)BiCGStab can be much more stable and converge much faster than BiCGStab when a problem with ill-condition is solved. We have tested ML(n)BiCGStab on the standard oil reservoir simulation test data called SPE9 and found that ML(n)BiCGStab reduced the total computational time by more than 60% when compared to BiCGStab. Tests made on the data from Matrix Market also support the superiority of ML(n)BiCGStab over BiCGStab. Because of the O(N^2) storage requirement in the full GMRES, one has to adopt a restart strategy to get the storage under control when GMRES is implemented. In comparison, ML(n)BiCGStab is a method with only O(nN) storage requirement and therefore it does not need a restart strategy. In this paper, we introduce ML(n)BiCGStab (in particular, a new algorithm involving A-transpose), its relations to some existing methods and its implementations.

math.NA↗

arXiv2010

ML(n)BiCGStab: Reformulation, Analysis and Implementation

With the aid of index functions, we re-derive the ML(n)BiCGStab algorithm in a paper by Yeung and Chan in 1999 in a more systematic way. It turns out that there are n ways to define the ML(n)BiCGStab residual vector. Each definition will lead to a different ML(n)BiCGStab algorithm. We demonstrate this by presenting a second algorithm which requires less storage. In theory, this second algorithm serves as a bridge that connects the Lanczos-based BiCGStab and the Arnoldi-based FOM while ML(n)BiCG a bridge connecting BiCG and FOM. We also analyze the breakdown situations from the probabilistic point of view and summarize some useful properties of ML(n)BiCGStab. Implementation issues are also addressed.

math.NA↗