Calculating portions of eigenvalues and eigenvectors of matrices or matrix pencils has many applications. An approach to this calculation for Hermitian problems based on a density matrix has been proposed in 2009 and a software package called FEAST has been developed. The density-matrix approach allows FEAST's implementation to exploit a key strength of modern computer architectures, namely, multiple levels of parallelism. Consequently, the software package has been well received and subsequently commercialized. A detailed theoretical analysis of Hermitian FEAST has also been established very recently. This paper generalizes the FEAST algorithm and theory, for the first time, to tackle non-Hermitian problems. Fundamentally, the new algorithm is basic subspace iteration or Bauer bi-iteration, except applied with a novel accelerator based on Cauchy integrals. The resulting algorithm retains the multi-level parallelism of Hermitian FEAST, making it a valuable new tool for large-scale computational science and engineering problems on leading-edge computing platforms.
In this paper, a Parallel Direct Eigensolver for Sequences of Hermitian Eigenvalue Problems with no tridiagonalization is proposed, denoted by \texttt{PDESHEP}, and it combines direct methods with iterative methods. \texttt{PDESHEP} first reduces a Hermitian matrix to its banded form, then applies a spectrum slicing algorithm to the banded matrix, and finally computes the eigenvectors of the original matrix via backtransform. Therefore, compared with conventional direct eigensolvers, \texttt{PDESHEP} avoids tridiagonalization, which consists of many memory-bounded operations. In this work, the…
This paper proposes an efficient method for computing selected generalized eigenpairs of a sparse Hermitian definite matrix pencil $(A,B)$. Based on Zolotarev's best rational function approximations of the signum function and conformal mapping techniques, we construct the best rational function approximation of a rectangular function supported on an arbitrary interval via function compositions with partial fraction representations. This new best rational function approximation can be applied to construct spectrum filters of $(A,B)$ with a smaller number of poles than a direct construction with…
This paper describes a set of rational filtering algorithms to compute a few eigenvalues (and associated eigenvectors) of non-Hermitian matrix pencils. Our interest lies in computing eigenvalues located inside a given disk, and the proposed algorithms approximate these eigenvalues and associated eigenvectors by harmonic Rayleigh-Ritz projections on subspaces built by computing range spaces of rational matrix functions through randomized range finders. These rational matrix functions are designed so that directions associated with non-sought eigenvalues are dampened to (approximately) zero. Var…
Improved Accuracy and Parallelism for MRRR-based Eigensolvers -- A Mixed Precision Approach
Aachen Institute for advanced study in Computational Engineering Science, RWTH Aachen University（亚琛高等计算工程科学研究院，亚琛工业大学）；Depto. de Ingeniería y Ciencia de Computadores, Universidad Jaume I（计算机科学与工程学院，海梅一世大学）
Recently a novel family of eigensolvers, called spectral indicator methods (SIMs), was proposed. Given a region on the complex plane, SIMs first compute an indicator by the spectral projection. The indicator is used to test if the region contains eigenvalue(s). Then the region containing eigenvalues(s) is subdivided and tested. The procedure is repeated until the eigenvalues are identified within a specified precision. In this paper, using Cayley transformation and Krylov subspaces, a memory efficient multilevel eigensolver is proposed. The method uses less memory compared with the early versi…
Convergence rates of block iterations for solving eigenvalue problems typically measure errors of Ritz values approximating eigenvalues. The errors of the Ritz values are commonly bounded in terms of principal angles between the initial or iterative subspace and the invariant subspace associated with the target eigenvalues. Such bounds thus cannot be applied repeatedly as needed for restarted block eigensolvers, since the left- and right-hand sides of the bounds use different terms. They must be combined with additional bounds which could cause an overestimation. Alternative repeatable bounds …
We propose an eigensolver and the corresponding package, GCGE, for solving large scale eigenvalue problems. This method is the combination of damping idea, subspace projection method and inverse power method with dynamic shifts. To reduce the dimensions of projection subspaces, a moving mechanism is developed when the number of desired eigenpairs is large. The numerical methods, implementing techniques and the structure of the package are presented. Plenty of numerical results are provided to demonstrate the efficiency, stability and scalability of the concerned eigensolver and the package GCG…
Convergence analysis of block iterative solvers for Hermitian eigenvalue problems and the closely related research on properties of matrix-based signal filters are challenging, and attract increasing attention due to their recent applications in spectral data clustering and graph-based signal processing. We combine majorization-based techniques pioneered for investigating the Rayleigh-Ritz method in [SIAM J. Matrix Anal. Appl., 31 (2010), pp. 1521-1537] with tools of classical analysis of the block power method by Rutishauser [Numer. Math., 13 (1969), pp. 4-13] to derive convergence rate bound…
Low-rank Riemannian eigensolver for high-dimensional Hamiltonians
ETH Zurich（苏黎世联邦理工学院）；Seminar for Applied Mathematics, ETH Zurich（苏黎世联邦理工学院应用数学研讨会）；Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences（俄罗斯科学院马丘克数值数学研究所）；National Research University Higher School of Economics（国立高等经济大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
Three-Level Parallel J-Jacobi Algorithms for Hermitian Matrices
Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb（萨格勒布大学机械与造船工程学院）；Department of Mathematics, University of Zagreb（萨格勒布大学数学系）；Ruđer Bošković Institute（鲁德·博什科维奇研究所）；Multicom d.o.o.（Multicom有限公司）
Randomized Block Davidson Eigensolvers for Plane-Wave Density-Functional Theory
Paul Scherrer Institute（保罗谢勒研究所）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Swiss National Supercomputing Centre (CSCS)（瑞士国家超级计算中心）；ETH Zurich（苏黎世联邦理工学院）
Non-Linear Least-Squares Optimization of Rational Filters for the Solution of Interior Eigenvalue Problems
RWTH Aachen University（亚琛工业大学）；Jülich Supercomputing Centre, Institute for Advanced Simulation, Forschungszentrum Jülich GmbH（于利希超级计算中心，先进模拟研究所，于利希研究中心有限公司）
In this work we study the convergence properties of the one-level parallel Schwarz method with Robin transmission conditions applied to the one-dimensional and two-dimensional Helmholtz and Maxwell's equations. One-level methods are not scalable in general. However, it has recently been proven that when impedance transmission conditions are used in the case of the algorithm applied to the equations with absorption, under certain assumptions, scalability can be achieved and no coarse space is required. We show here that this result is also true for the iterative version of the method at the con…
symveig: Verified eigenvalue enclosures for symmetry-decomposed Hermitian matrices
National Forensic Sciences University（国家法证科学大学）；Department of Applied Science and Humanities, School of Engineering & Technology, Vivekananda Institute of Professional Studies - Technical Campus（维韦卡南达专业研究所技术校区工程与技术学院应用科学与人文系）
