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

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 characteristics. As a numerical example, layout optimisation of a beam structure for eigenfrequency maximization is solved. The results show an applicability and effectiveness of the proposed method. (C) 2003 Elsevier B.V. All rights reserved.