On a near optimal sampling strategy for least squares polynomial regression

Cited 9 time in webofscience Cited 0 time in scopus
  • Hit : 47
  • Download : 0
We present a sampling strategy of least squares polynomial regression. The strategy combines two recently developed methods for least squares method: Christoffel least squares algorithm and quasi-optimal sampling. More specifically, our new strategy first choose samples from the pluripotential equilibrium measure and then re-order the samples by the quasi-optimal algorithm. A weighted least squares problem is solved on a (much) smaller sample set to obtain the regression result. It is then demonstrated that the new strategy results in a polynomial least squares method with high accuracy and robust stability at almost minimal number of samples. (C) 2016 Elsevier Inc. All rights reserved.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2016-12
Language
English
Article Type
Article
Citation

JOURNAL OF COMPUTATIONAL PHYSICS, v.326, pp.931 - 946

ISSN
0021-9991
DOI
10.1016/j.jcp.2016.09.032
URI
http://hdl.handle.net/10203/297256
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 9 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0