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

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 optimization of a beam structure for eigenfrequency maximization is solved. The results show an applicability and effectiveness of the proposed method.