Abstract
Original language  Undefined 

Supervisors/Advisors 

Award date  20 Jun 2002 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9036517710 
Publication status  Published  20 Jun 2002 
Keywords
 IR38630
 METIS206125
Cite this
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Handling Wavelet Expansions in numerical Methods. / Metselaar, Arend Aalberthus Roeland.
Enschede : Twente University Press (TUP), 2002. 119 p.Research output: Thesis › PhD Thesis  Research UT, graduation UT
TY  THES
T1  Handling Wavelet Expansions in numerical Methods
AU  Metselaar, Arend Aalberthus Roeland
PY  2002/6/20
Y1  2002/6/20
N2  Wavelet expansions have drawn a lot of attention in recent decades. Wavelets originate from signal analysis, and one of the purposes is data compression. The ability to compress data can also be used to reduce the amount of computation work in a numerical simulation.A family of wavelets forms a basis for a function space. Most adaptive wavelet methods are based on expansions of functions in a freely selectable subset of such a basis. This avoids the often expensive, grid generation step needed in most other adaptive methods, but leads to more complicated bookkeeping.A large part of this thesis is devoted to the problem of performing this bookkeeping efficiently. Some methods divide the wavelet expansion into parts of decreasing importance. Using an approximate division is often more efficient than using the best division, which requires all terms in the expansion to be sorted.
AB  Wavelet expansions have drawn a lot of attention in recent decades. Wavelets originate from signal analysis, and one of the purposes is data compression. The ability to compress data can also be used to reduce the amount of computation work in a numerical simulation.A family of wavelets forms a basis for a function space. Most adaptive wavelet methods are based on expansions of functions in a freely selectable subset of such a basis. This avoids the often expensive, grid generation step needed in most other adaptive methods, but leads to more complicated bookkeeping.A large part of this thesis is devoted to the problem of performing this bookkeeping efficiently. Some methods divide the wavelet expansion into parts of decreasing importance. Using an approximate division is often more efficient than using the best division, which requires all terms in the expansion to be sorted.
KW  IR38630
KW  METIS206125
M3  PhD Thesis  Research UT, graduation UT
SN  9036517710
PB  Twente University Press (TUP)
CY  Enschede
ER 