347 research outputs found
A Householder-based algorithm for Hessenberg-triangular reduction
The QZ algorithm for computing eigenvalues and eigenvectors of a matrix
pencil requires that the matrices first be reduced to
Hessenberg-triangular (HT) form. The current method of choice for HT reduction
relies entirely on Givens rotations regrouped and accumulated into small dense
matrices which are subsequently applied using matrix multiplication routines. A
non-vanishing fraction of the total flop count must nevertheless still be
performed as sequences of overlapping Givens rotations alternately applied from
the left and from the right. The many data dependencies associated with this
computational pattern leads to inefficient use of the processor and poor
scalability.
In this paper, we therefore introduce a fundamentally different approach that
relies entirely on (large) Householder reflectors partially accumulated into
block reflectors, by using (compact) WY representations. Even though the new
algorithm requires more floating point operations than the state of the art
algorithm, extensive experiments on both real and synthetic data indicate that
it is still competitive, even in a sequential setting. The new algorithm is
conjectured to have better parallel scalability, an idea which is partially
supported by early small-scale experiments using multi-threaded BLAS. The
design and evaluation of a parallel formulation is future work
A rational QZ method
We propose a rational QZ method for the solution of the dense, unsymmetric
generalized eigenvalue problem. This generalization of the classical QZ method
operates implicitly on a Hessenberg, Hessenberg pencil instead of on a
Hessenberg, triangular pencil. Whereas the QZ method performs nested subspace
iteration driven by a polynomial, the rational QZ method allows for nested
subspace iteration driven by a rational function, this creates the additional
freedom of selecting poles. In this article we study Hessenberg, Hessenberg
pencils, link them to rational Krylov subspaces, propose a direct reduction
method to such a pencil, and introduce the implicit rational QZ step. The link
with rational Krylov subspaces allows us to prove essential uniqueness
(implicit Q theorem) of the rational QZ iterates as well as convergence of the
proposed method. In the proofs, we operate directly on the pencil instead of
rephrasing it all in terms of a single matrix. Numerical experiments are
included to illustrate competitiveness in terms of speed and accuracy with the
classical approach. Two other types of experiments exemplify new possibilities.
First we illustrate that good pole selection can be used to deflate the
original problem during the reduction phase, and second we use the rational QZ
method to implicitly filter a rational Krylov subspace in an iterative method
A multishift, multipole rational QZ method with aggressive early deflation
The rational QZ method generalizes the QZ method by implicitly supporting
rational subspace iteration. In this paper we extend the rational QZ method by
introducing shifts and poles of higher multiplicity in the Hessenberg pencil,
which is a pencil consisting of two Hessenberg matrices. The result is a
multishift, multipole iteration on block Hessenberg pencils which allows one to
stick to real arithmetic for a real input pencil. In combination with optimally
packed shifts and aggressive early deflation as an advanced deflation technique
we obtain an efficient method for the dense generalized eigenvalue problem. In
the numerical experiments we compare the results with state-of-the-art routines
for the generalized eigenvalue problem and show that we are competitive in
terms of speed and accuracy
The geometry and combinatorics of Springer fibers
This survey paper describes Springer fibers, which are used in one of the
earliest examples of a geometric representation. We will compare and contrast
them with Schubert varieties, another family of subvarieties of the flag
variety that play an important role in representation theory and combinatorics,
but whose geometry is in many respects simpler. The end of the paper describes
a way that Springer fibers and Schubert varieties are related, as well as open
questions.Comment: 18 page
Blocked algorithms for the reduction to Hessenberg-triangular form revisited
We present two variants of Moler and Stewart's algorithm for reducing a matrix pair to Hessenberg-triangular (HT) form with increased data locality in the access to the matrices. In one of these variants, a careful reorganization and accumulation of Givens rotations enables the use of efficient level 3 BLAS. Experimental results on four different architectures, representative of current high performance processors, compare the performances of the new variants with those of the implementation of Moler and Stewart's algorithm in subroutine DGGHRD from LAPACK, Dackland and Kågström's two-stage algorithm for the HT form, and a modified version of the latter which requires considerably less flop
Eigenvalue Methods for Interpolation Bases
This thesis investigates eigenvalue techniques for the location of roots of polynomials expressed in the Lagrange basis. Polynomial approximations to functions arise in almost all areas of computational mathematics, since polynomial expressions can be manipulated in ways that the original function cannot. Polynomials are most often expressed in the monomial basis; however, in many applications polynomials are constructed by interpolating data at a series ofpoints. The roots of such polynomial interpolants can be found by computing the eigenvalues of a generalized companion matrix pair constructed directly from the values of the interpolant. This affords the opportunity to work with polynomials expressed directly in the interpolation basis in which they were posed, avoiding the often ill-conditioned transformation between bases.
Working within this framework, this thesis demonstrates that computing the roots of polynomials via these companion matrices is numerically stable, and the matrices involved can be reduced in such a way as to significantly lower the number of operations required to obtain the roots.
Through examination of these various techniques, this thesis offers insight into the speed, stability, and accuracy of rootfinding algorithms for polynomials expressed in alternative bases
Minimizing Communication in Linear Algebra
In 1981 Hong and Kung proved a lower bound on the amount of communication
needed to perform dense, matrix-multiplication using the conventional
algorithm, where the input matrices were too large to fit in the small, fast
memory. In 2004 Irony, Toledo and Tiskin gave a new proof of this result and
extended it to the parallel case. In both cases the lower bound may be
expressed as (#arithmetic operations / ), where M is the size
of the fast memory (or local memory in the parallel case). Here we generalize
these results to a much wider variety of algorithms, including LU
factorization, Cholesky factorization, factorization, QR factorization,
algorithms for eigenvalues and singular values, i.e., essentially all direct
methods of linear algebra. The proof works for dense or sparse matrices, and
for sequential or parallel algorithms. In addition to lower bounds on the
amount of data moved (bandwidth) we get lower bounds on the number of messages
required to move it (latency). We illustrate how to extend our lower bound
technique to compositions of linear algebra operations (like computing powers
of a matrix), to decide whether it is enough to call a sequence of simpler
optimal algorithms (like matrix multiplication) to minimize communication, or
if we can do better. We give examples of both. We also show how to extend our
lower bounds to certain graph theoretic problems.
We point out recently designed algorithms for dense LU, Cholesky, QR,
eigenvalue and the SVD problems that attain these lower bounds; implementations
of LU and QR show large speedups over conventional linear algebra algorithms in
standard libraries like LAPACK and ScaLAPACK. Many open problems remain.Comment: 27 pages, 2 table
On computing the eigenvalues of a symplectic pencil
AbstractThis paper presents an algorithm for computing the eigenvalues of a symplectic pencil that arises in one of the commonly used approaches for solving the discrete-time algebraic Riccati equation. The algorithm is numerically efficient and reliable in that it employs only orthogonal transformations and makes use of the structure of the symplectic pencil. It requires about one-fourth the number of floating-point operations that the QZ algorithm uses to compute the eigenvalues of the pencil directly. The proposed method can be regarded as being analogous for the case of symplectic pencils to the method developed by Van Loan for computing the eigenvalues of Hamiltonian matrices
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