3,232 research outputs found
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl
On the computation of Gaussian quadrature rules for Chebyshev sets of linearly independent functions
We consider the computation of quadrature rules that are exact for a
Chebyshev set of linearly independent functions on an interval . A
general theory of Chebyshev sets guarantees the existence of rules with a
Gaussian property, in the sense that basis functions can be integrated
exactly with just points and weights. Moreover, all weights are positive
and the points lie inside the interval . However, the points are not the
roots of an orthogonal polynomial or any other known special function as in the
case of regular Gaussian quadrature. The rules are characterized by a nonlinear
system of equations, and earlier numerical methods have mostly focused on
finding suitable starting values for a Newton iteration to solve this system.
In this paper we describe an alternative scheme that is robust and generally
applicable for so-called complete Chebyshev sets. These are ordered Chebyshev
sets where the first elements also form a Chebyshev set for each . The
points of the quadrature rule are computed one by one, increasing exactness of
the rule in each step. Each step reduces to finding the unique root of a
univariate and monotonic function. As such, the scheme of this paper is
guaranteed to succeed. The quadrature rules are of interest for integrals with
non-smooth integrands that are not well approximated by polynomials
Fast matrix computations for pair-wise and column-wise commute times and Katz scores
We first explore methods for approximating the commute time and Katz score
between a pair of nodes. These methods are based on the approach of matrices,
moments, and quadrature developed in the numerical linear algebra community.
They rely on the Lanczos process and provide upper and lower bounds on an
estimate of the pair-wise scores. We also explore methods to approximate the
commute times and Katz scores from a node to all other nodes in the graph.
Here, our approach for the commute times is based on a variation of the
conjugate gradient algorithm, and it provides an estimate of all the diagonals
of the inverse of a matrix. Our technique for the Katz scores is based on
exploiting an empirical localization property of the Katz matrix. We adopt
algorithms used for personalized PageRank computing to these Katz scores and
theoretically show that this approach is convergent. We evaluate these methods
on 17 real world graphs ranging in size from 1000 to 1,000,000 nodes. Our
results show that our pair-wise commute time method and column-wise Katz
algorithm both have attractive theoretical properties and empirical
performance.Comment: 35 pages, journal version of
http://dx.doi.org/10.1007/978-3-642-18009-5_13 which has been submitted for
publication. Please see
http://www.cs.purdue.edu/homes/dgleich/publications/2011/codes/fast-katz/ for
supplemental code
On the convergence of spectral deferred correction methods
In this work we analyze the convergence properties of the Spectral Deferred
Correction (SDC) method originally proposed by Dutt et al. [BIT, 40 (2000), pp.
241--266]. The framework for this high-order ordinary differential equation
(ODE) solver is typically described wherein a low-order approximation (such as
forward or backward Euler) is lifted to higher order accuracy by applying the
same low-order method to an error equation and then adding in the resulting
defect to correct the solution. Our focus is not on solving the error equation
to increase the order of accuracy, but on rewriting the solver as an iterative
Picard integral equation solver. In doing so, our chief finding is that it is
not the low-order solver that picks up the order of accuracy with each
correction, but it is the underlying quadrature rule of the right hand side
function that is solely responsible for picking up additional orders of
accuracy. Our proofs point to a total of three sources of errors that SDC
methods carry: the error at the current time point, the error from the previous
iterate, and the numerical integration error that comes from the total number
of quadrature nodes used for integration. The second of these two sources of
errors is what separates SDC methods from Picard integral equation methods; our
findings indicate that as long as difference between the current and previous
iterate always gets multiplied by at least a constant multiple of the time step
size, then high-order accuracy can be found even if the underlying "solver" is
inconsistent the underlying ODE. From this vantage, we solidify the prospects
of extending spectral deferred correction methods to a larger class of solvers
to which we present some examples.Comment: 29 page
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