2016_ZHAO_Y.Z._Ph.D.pdf (2.65 MB)
Multilevel sparse grid kernels collocation with radial basis functions for elliptic and parabolic problems
thesis
posted on 2017-01-16, 11:28 authored by Yangzhang ZhaoRadial basis functions (RBFs) are well-known for the ease implementation as
they are the mesh-free method [31, 37, 71, 72]. In this thesis, we modify the
multilevel sparse grid kernel interpolation (MuSIK) algorithm proposed in [48]
for use in Kansa’s collocation method (referred to as MuSIK-C) to solve elliptic
and parabolic problems. The curse of dimensionality is a significant challenge
in high dimension approximation. A full grid collocation method requires O(Nd)
nodal points to construct an approximation; here N is the number of nodes in
one direction and d means the dimension. However, the sparse grid collocation
method in this thesis only demand O(N logd1(N)) nodes. We save much more
memory cost using sparse grids and obtain a good performance as using full grids.
Moreover, the combination technique [20, 54] allows the sparse grid collocation
method to be parallelised. When solving parabolic problems, we follow Myers
et al.’s suggestion in [90] to use the space-time method, considering time as
one spatial dimension. If we apply sparse grids in the spatial dimensions and
use time-stepping, we still need O(N2 logd1(N)) nodes. However, if we use the
space-time method, the total number of nodes is O(N logd(N)).
In this thesis, we always compare the performance of multiquadric (MQ) basis
function and the Gaussian basis function. In all experiments, we observe that
the collocation method using the Gaussian with scaling shape parameters does
not converge. Meanwhile, in Chapter 3, there is an experiment to show that the
space-time method with MQ has a similar convergence rate as a time-stepping
method using MQ in option pricing. From the numerical experiments in Chapter
4, MuSIK-C using MQ and the Gaussian always give more rapid convergence
and high accuracy especially in four dimensions (T R3) for PDEs with smooth
conditions. Compared to some recently proposed mesh-based methods, MuSIK-C
shows similar performance in low dimension situation and better approximation
in high dimension. In Chapter 5, we combine the Method of Lines (MOL) and our
MuSIK-C to obtain good convergence in pricing one asset European option and
the Margrabe option, that have non-smooth initial conditions.
History
Supervisor(s)
Levesley, Jeremy; Georgoulis, EmmanuilDate of award
2017-01-03Author affiliation
Department of MathematicsAwarding institution
University of LeicesterQualification level
- Doctoral
Qualification name
- PhD
Language
enAdministrator link
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