547 research outputs found
A Model for Understanding Numerical Stability
We present a model of roundoff error analysis that combines simplicity with
predictive power. Though not considering all sources of roundoff within an
algorithm, the model is related to a recursive roundoff error analysis and
therefore capable of correctly predicting stability or instability of an
algorithm. By means of nontrivial examples, such as the componentwise backward
stability analysis of Gaussian elimination with a single iterative refinement
step, we demonstrate that the model even yields quantitative backward error
bounds that show all the known problem-dependent terms (with the exception of
dimension-dependent constants, which are the weak spot of any a priori
analysis). The model can serve as a convenient tool for teaching or as a
heuristic device to discover stability results before entering a further,
detailed analysis
On the Scaling Limits of Determinantal Point Processes with Kernels Induced by Sturm-Liouville Operators
By applying an idea of Borodin and Olshanski [J. Algebra 313 (2007), 40-60],
we study various scaling limits of determinantal point processes with trace
class projection kernels given by spectral projections of selfadjoint
Sturm-Liouville operators. Instead of studying the convergence of the kernels
as functions, the method directly addresses the strong convergence of the
induced integral operators. We show that, for this notion of convergence, the
Dyson, Airy, and Bessel kernels are universal in the bulk, soft-edge, and
hard-edge scaling limits. This result allows us to give a short and unified
derivation of the known formulae for the scaling limits of the classical random
matrix ensembles with unitary invariance, that is, the Gaussian unitary
ensemble (GUE), the Wishart or Laguerre unitary ensemble (LUE), and the MANOVA
(multivariate analysis of variance) or Jacobi unitary ensemble (JUE)
On the convergence rates of Gauss and Clenshaw-Curtis quadrature for functions of limited regularity
We study the optimal general rate of convergence of the n-point quadrature
rules of Gauss and Clenshaw-Curtis when applied to functions of limited
regularity: if the Chebyshev coefficients decay at a rate O(n^{-s-1}) for some
s > 0, Clenshaw-Curtis and Gauss quadrature inherit exactly this rate. The
proof (for Gauss, if 0 < s < 2, there is numerical evidence only) is based on
work of Curtis, Johnson, Riess, and Rabinowitz from the early 1970s and on a
refined estimate for Gauss quadrature applied to Chebyshev polynomials due to
Petras (1995). The convergence rate of both quadrature rules is up to one power
of n better than polynomial best approximation; hence, the classical proof
strategy that bounds the error of a quadrature rule with positive weights by
polynomial best approximation is doomed to fail in establishing the optimal
rate.Comment: 7 pages, the figure of the revision has an unsymmetric example, to
appear in SIAM J. Numer. Ana
Sparsing in Real Time Simulation
Modelling of mechatronical systems often leads to large DAEs with stiff components. In real time simulation neither implicit nor explicit methods can cope with such systems in an efficient way: explicit methods have to employ too small steps and implicit methods have to solve too large systems of equations. A solution of this general problem is to use a method that allows manipulations of the Jacobian by computing only those parts that are necessary for the stability of the method. Specifically, manipulation by sparsing aims at zeroing out certain elements of the Jacobian leading t a structure that can be exploited using sparse matrix techniques. The elements to be neglected are chosen by an a priori analysis phase that can be accomplished before the real-time simulaton starts. In this article a sparsing criterion for the linearly implicit Euler method is derived that is based on block diagnonalization and matrix perturbation theory
The Singular Values of the GOE
As a unifying framework for examining several properties that nominally
involve eigenvalues, we present a particular structure of the singular values
of the Gaussian orthogonal ensemble (GOE): the even-location singular values
are distributed as the positive eigenvalues of a Gaussian ensemble with chiral
unitary symmetry (anti-GUE), while the odd-location singular values,
conditioned on the even-location ones, can be algebraically transformed into a
set of independent -distributed random variables. We discuss three
applications of this structure: first, there is a pair of bidiagonal square
matrices, whose singular values are jointly distributed as the even- and
odd-location ones of the GOE; second, the magnitude of the determinant of the
GOE is distributed as a product of simple independent random variables; third,
on symmetric intervals, the gap probabilities of the GOE can be expressed in
terms of the Laguerre unitary ensemble (LUE). We work specifically with
matrices of finite order, but by passing to a large matrix limit, we also
obtain new insight into asymptotic properties such as the central limit theorem
of the determinant or the gap probabilities in the bulk-scaling limit. The
analysis in this paper avoids much of the technical machinery (e.g. Pfaffians,
skew-orthogonal polynomials, martingales, Meijer -function, etc.) that was
previously used to analyze some of the applications.Comment: Introduction extended, typos corrected, reference added. 31 pages, 1
figur
Automatic Deformation of Riemann-Hilbert Problems with Applications to the Painlev\'e II Transcendents
The stability and convergence rate of Olver's collocation method for the
numerical solution of Riemann-Hilbert problems (RHPs) is known to depend very
sensitively on the particular choice of contours used as data of the RHP. By
manually performing contour deformations that proved to be successful in the
asymptotic analysis of RHPs, such as the method of nonlinear steepest descent,
the numerical method can basically be preconditioned, making it asymptotically
stable. In this paper, however, we will show that most of these preconditioning
deformations, including lensing, can be addressed in an automatic, completely
algorithmic fashion that would turn the numerical method into a black-box
solver. To this end, the preconditioning of RHPs is recast as a discrete,
graph-based optimization problem: the deformed contours are obtained as a
system of shortest paths within a planar graph weighted by the relative
strength of the jump matrices. The algorithm is illustrated for the RHP
representing the Painlev\'e II transcendents.Comment: 20 pages, 16 figure
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