16:30
16:30
14:30
Generating good meshes and inverting good matrices
Abstract
An essential first step in many problems of numerical analysis and
computer graphics is to cover a region with a reasonably regular mesh.
We describe a short MATLAB code that begins with a "distance function"
to describe the region: $d(x)$ is the distance to the boundary
(with d
17:00
Fast and high quality display of large relational information with an introduction to recent advances in mathematica
Abstract
The talk will start with an introduction to recent development in Mathematica, with emphasis on numerical computing. This will be followed by a discussion of graph drawing algorithms for the display of relational information, in particular force directed algorithms. The talk will show that by employing multilevel approach and octree data structure, it is possible to achieve fast display of very large relational information, without compromising the quality.
17:00
15:30
Function Space Representations of Semilattice Tensor Products: Some Conjectures of Quackenbush from 1985 and a Conjecture of E.T
14:15
Completing Stochastic Volatility Models with Variance Swaps
Abstract
Complete stochastic volatility models provide prices and
hedges. There are a number of complete models which jointly model an
underlying and one or more vanilla options written on it (for example
see Lyons, Schonbucher, Babbar and Davis). However, any consistent
model describing the volatility of options requires a complex
dependence of the volatility of the option on its strike. To date we
do not have a clear approach to selecting a model for the volatility
of these options
14:15
15:15
Geometry and singularities at infinity of real (plane) polynomial functions
16:30
14:30
Practical implementation of an inexact GMRES method
Abstract
We consider the solution of a linear system of equations using the GMRES iterative method. In some applications, performing inexact matrix-vector products in this method may be interesting, provided that a reasonable convergence of GMRES is achieved. A GMRES algorithm where the matrix vector product is performed inexactly is termed ”inexact GMRES algorithm”. An application of this idea occurs in computational electromagnetics, where the fast multipole method provides approximations of the matrix-vector product within a user-defined precision, and where these inaccurate matrix-vector products are all the more cheaper (in terms of CPU time) as the user-defined precision is low. The key point is then to design a control strategy of the accuracy of the matrix-vector product so that the GMRES converges better in the sense that 1) the inexact method achieves a satisfactory limiting accuracy, 2) within a reasonable number of steps.
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In [1], a relaxation strategy is proposed for general systems and validated on a large set of numerical experiments. This work is based on heuristic considerations and proposes a strategy that enables a convergence of the GMRES iterates $x_{k}$ within a relative normwise backward error $\frac{\|b−Ax_{k}\|}{\|A\| \|x_{k}\| + \|b\|}$ less than a prescribed quantity $\eta$ > 0, on a significant number of numerical experiments. Similar strategies have been applied to the solution of device simulation problems using domain decomposition [2] and to the preconditioning of a radiation diffusion problem in [5].
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A step toward a theoretical explanation of the observed behaviour of the inexact GMRES is proposed in [3, 4]. In this talk, we show that in spite of this considerable theoretical study, the experimental work of [1] is not fully understood yet. We give an overview of the questions that still remains open both in exact arithmetic and in floating-point arithmetic, and we provide some insights into the solution of some of them. Possible applications of this work for the preconditioned GMRES method, when the matrix-vector product is accurate but the preconditioning operation is approximate, are also investigated, based on [3].
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References
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[1] A. Bouras and V. Frayss´e. Inexact matrix-vector products in Krylov methods for solving linear systems: a relaxation strategy. SIAM Journal on Matrix Analysis and Applications, 2004. To appear.
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[2] A. Bouras, V. Frayss´e, and L. Giraud. A relaxation strategy for inner-outer linear solvers in domain decomposition methods. Technical Report TR/PA/00/17, CERFACS, Toulouse, France, 2000.
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[3] V. Simoncini and D. B. Szyld. Theory of inexact Krylov subspace methods and applications to scientific computing. SIAM Journal Scientific Computing, 25:454–477, 2003.
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[4] J. van den Eshof and G. L. G. Sleijpen. Inexact Krylov subspace methods for linear systems. SIAM Journal on Matrix Analysis and Applications, February 2004. To appear.
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[5] J. S. Warsa, M. Benzi, T. A. Warein, and J. E. Morel. Preconditioning a mixed discontinuous finite element method for radiation diffusion. Numerical Linear Algebra with Applications, 2004. To appear.
17:00
Convexity on Grassmannians and calculus of variations
Abstract
The talk will discuss the variationnal problem on finite
dimensional normed spaces and Finsler manifolds.
We first review different notions of ellipticity (convexity) for
parametric integrands (densities) on normed spaces and compare them with
different minimality properties of affine subspaces. Special attention will
be given to Busemann and Holmes-Thompson k-area. If time permits, we will
then present the first variation formula on Finsler manifolds and exhibit a
class of minimal submanifolds.
17:00
Topological representatives of free group automorphisms and automorphism growth
15:45
Rough Paths revisited
Abstract
A version of Lyons theory of rough path calculus which applies to a
subclass of rough paths for which more geometric interpretations are
valid will be presented. Application will be made to the Brownian and
to the (fractional) support theorem.
15:30