zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Adaptive quasi-Monte-Carlo integration based on MISER and VEGAS. (English) Zbl 1042.65009
Niederreiter, Harald (ed.), Monte Carlo and quasi-Monte Carlo methods 2002. Proceedings of a conference, National University of Singapore, Republic of Singapore, November 25--28, 2002. Berlin: Springer (ISBN 3-540-20466-0/pbk). 393-406 (2004).
Summary: Quasi-Monte Carlo (QMC) routines are one of the most common techniques for solving integration problems in high dimensions. However, their efficiency degrades if the variation of the integrand is concentrated in small areas of the integration domain. Adaptive algorithms cope with this situation by adjusting the flow of computation based on previous integrand evaluations. We explore ways to modify the Monte Carlo based adaptive algorithms MISER and VEGAS such that low-discrepancy point sets are used instead of random samples. Experimental results show that the proposed algorithms outperform plain QMC as well as the original adaptive integration routine for certain classes of test cases. For the entire collection see [Zbl 1029.00038].

65C05Monte Carlo methods