An efficient algorithm for Kriging approximation and optimization with large-scale sampling data

@inproceedings{Sakata2004AnEA,
  title={An efficient algorithm for Kriging approximation and optimization with large-scale sampling data},
  author={S. Sakata and F. Ashida and M. Zako},
  year={2004}
}
This paper describes an algorithm to improve a computational cost for estimation using the Kriging method with a large number of sampling data. An improved formula to compute the weighting coefficient for Kriging estimation is proposed. The Sherman–Morrison–Woodbury formula is applied to solving an approximated simultaneous equation to determine a weighting coefficient. A profile of the matrix is reduced by sorting of given data. Applying the proposal formula to several examples indicates its… CONTINUE READING
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