427 research outputs found
An O(N squared) method for computing the eigensystem of N by N symmetric tridiagonal matrices by the divide and conquer approach
An efficient method is proposed to solve the eigenproblem of N by N Symmetric Tridiagonal (ST) matrices. Unlike the standard eigensolvers which necessitate O(N cubed) operations to compute the eigenvectors of such ST matrices, the proposed method computes both the eigenvalues and eigenvectors with only O(N squared) operations. The method is based on serial implementation of the recently introduced Divide and Conquer (DC) algorithm. It exploits the fact that by O(N squared) of DC operations, one can compute the eigenvalues of N by N ST matrix and a finite number of pairs of successive rows of its eigenvector matrix. The rest of the eigenvectors--all of them or one at a time--are computed by linear three-term recurrence relations. Numerical examples are presented which demonstrate the superiority of the proposed method by saving an order of magnitude in execution time at the expense of sacrificing a few orders of accuracy
High-Performance Solvers for Dense Hermitian Eigenproblems
We introduce a new collection of solvers - subsequently called EleMRRR - for
large-scale dense Hermitian eigenproblems. EleMRRR solves various types of
problems: generalized, standard, and tridiagonal eigenproblems. Among these,
the last is of particular importance as it is a solver on its own right, as
well as the computational kernel for the first two; we present a fast and
scalable tridiagonal solver based on the Algorithm of Multiple Relatively
Robust Representations - referred to as PMRRR. Like the other EleMRRR solvers,
PMRRR is part of the freely available Elemental library, and is designed to
fully support both message-passing (MPI) and multithreading parallelism (SMP).
As a result, the solvers can equally be used in pure MPI or in hybrid MPI-SMP
fashion. We conducted a thorough performance study of EleMRRR and ScaLAPACK's
solvers on two supercomputers. Such a study, performed with up to 8,192 cores,
provides precise guidelines to assemble the fastest solver within the ScaLAPACK
framework; it also indicates that EleMRRR outperforms even the fastest solvers
built from ScaLAPACK's components
Efficient numerical diagonalization of hermitian 3x3 matrices
A very common problem in science is the numerical diagonalization of
symmetric or hermitian 3x3 matrices. Since standard "black box" packages may be
too inefficient if the number of matrices is large, we study several
alternatives. We consider optimized implementations of the Jacobi, QL, and
Cuppen algorithms and compare them with an analytical method relying on
Cardano's formula for the eigenvalues and on vector cross products for the
eigenvectors. Jacobi is the most accurate, but also the slowest method, while
QL and Cuppen are good general purpose algorithms. The analytical algorithm
outperforms the others by more than a factor of 2, but becomes inaccurate or
may even fail completely if the matrix entries differ greatly in magnitude.
This can mostly be circumvented by using a hybrid method, which falls back to
QL if conditions are such that the analytical calculation might become too
inaccurate. For all algorithms, we give an overview of the underlying
mathematical ideas, and present detailed benchmark results. C and Fortran
implementations of our code are available for download from
http://www.mpi-hd.mpg.de/~globes/3x3/ .Comment: 13 pages, no figures, new hybrid algorithm added, matches published
version, typo in Eq. (39) corrected; software library available at
http://www.mpi-hd.mpg.de/~globes/3x3
Minimizing Communication for Eigenproblems and the Singular Value Decomposition
Algorithms have two costs: arithmetic and communication. The latter
represents the cost of moving data, either between levels of a memory
hierarchy, or between processors over a network. Communication often dominates
arithmetic and represents a rapidly increasing proportion of the total cost, so
we seek algorithms that minimize communication. In \cite{BDHS10} lower bounds
were presented on the amount of communication required for essentially all
-like algorithms for linear algebra, including eigenvalue problems and
the SVD. Conventional algorithms, including those currently implemented in
(Sca)LAPACK, perform asymptotically more communication than these lower bounds
require. In this paper we present parallel and sequential eigenvalue algorithms
(for pencils, nonsymmetric matrices, and symmetric matrices) and SVD algorithms
that do attain these lower bounds, and analyze their convergence and
communication costs.Comment: 43 pages, 11 figure
Improved Accuracy and Parallelism for MRRR-based Eigensolvers -- A Mixed Precision Approach
The real symmetric tridiagonal eigenproblem is of outstanding importance in
numerical computations; it arises frequently as part of eigensolvers for
standard and generalized dense Hermitian eigenproblems that are based on a
reduction to tridiagonal form. For its solution, the algorithm of Multiple
Relatively Robust Representations (MRRR) is among the fastest methods. Although
fast, the solvers based on MRRR do not deliver the same accuracy as competing
methods like Divide & Conquer or the QR algorithm. In this paper, we
demonstrate that the use of mixed precisions leads to improved accuracy of
MRRR-based eigensolvers with limited or no performance penalty. As a result, we
obtain eigensolvers that are not only equally or more accurate than the best
available methods, but also -in most circumstances- faster and more scalable
than the competition
Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization
We present a randomized, inverse-free algorithm for producing an approximate
diagonalization of any matrix pencil . The bulk of the
algorithm rests on a randomized divide-and-conquer eigensolver for the
generalized eigenvalue problem originally proposed by Ballard, Demmel, and
Dumitriu [Technical Report 2010]. We demonstrate that this divide-and-conquer
approach can be formulated to succeed with high probability as long as the
input pencil is sufficiently well-behaved, which is accomplished by
generalizing the recent pseudospectral shattering work of Banks, Garza-Vargas,
Kulkarni, and Srivastava [Foundations of Computational Mathematics 2022]. In
particular, we show that perturbing and scaling regularizes its
pseudospectra, allowing divide-and-conquer to run over a simple random grid and
in turn producing an accurate diagonalization of in the backward error
sense. The main result of the paper states the existence of a randomized
algorithm that with high probability (and in exact arithmetic) produces
invertible and diagonal such that and in at most
operations, where is the asymptotic complexity of matrix
multiplication. This not only provides a new set of guarantees for highly
parallel generalized eigenvalue solvers but also establishes nearly matrix
multiplication time as an upper bound on the complexity of exact arithmetic
matrix pencil diagonalization.Comment: 58 pages, 8 figures, 2 table
MRRR-based Eigensolvers for Multi-core Processors and Supercomputers
The real symmetric tridiagonal eigenproblem is of outstanding importance in
numerical computations; it arises frequently as part of eigensolvers for
standard and generalized dense Hermitian eigenproblems that are based on a
reduction to tridiagonal form. For its solution, the algorithm of Multiple
Relatively Robust Representations (MRRR or MR3 in short) - introduced in the
late 1990s - is among the fastest methods. To compute k eigenpairs of a real
n-by-n tridiagonal T, MRRR only requires O(kn) arithmetic operations; in
contrast, all the other practical methods require O(k^2 n) or O(n^3) operations
in the worst case. This thesis centers around the performance and accuracy of
MRRR.Comment: PhD thesi
Divide-and-conquer sequential matrix diagonalisation for parahermitian matrices
A number of algorithms capable of iteratively calculating a polynomial matrix eigenvalue decomposition (PEVD) have been introduced. The PEVD is a generalisation of the ordinary EVD and will diagonalise a parahermitian matrix via paraunitary operations. Inspired by the existence of low complexity divide-and-conquer solutions to eigenproblems, this paper addresses a divide-and-conquer approach to the PEVD utilising the sequential matrix diagonalisation (SMD) algorithm. We demonstrate that with the proposed techniques, encapsulated in a novel algorithm titled divide-and-conquer sequential matrix diagonalisation (DC-SMD), algorithm complexity can be significantly reduced. This reduction impacts on a number of broadband multichannel problems, including those involving large arrays
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