Error Analysis of the Scaled Moving Least Squares Approximation

WANG Qing-qing; LI Xiao-lin

doi:10.21656/1000-0887.370260

Volume 38 Issue 11

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WANG Qing-qing, LI Xiao-lin. Error Analysis of the Scaled Moving Least Squares Approximation[J]. Applied Mathematics and Mechanics, 2017, 38(11): 1289-1299. doi: 10.21656/1000-0887.370260

Citation:

WANG Qing-qing, LI Xiao-lin. Error Analysis of the Scaled Moving Least Squares Approximation[J]. Applied Mathematics and Mechanics, 2017, 38(11): 1289-1299. doi: 10.21656/1000-0887.370260

Citation:

WANG Qing-qing, LI Xiao-lin. Error Analysis of the Scaled Moving Least Squares Approximation[J]. Applied Mathematics and Mechanics, 2017, 38(11): 1289-1299. doi: 10.21656/1000-0887.370260

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Error Analysis of the Scaled Moving Least Squares Approximation

doi: 10.21656/1000-0887.370260

School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, P.R.China

Funds: The National Natural Science Foundation of China（General Program）(11471063)

Received Date: 2016-08-26
Rev Recd Date: 2017-09-28
Publish Date: 2017-11-15

Abstract

Abstract

Compared with the moving least squares (MLS) approximation, the scaled moving least squares (SMLS) approximation can avoid the issue of ill-conditioned matrices involved in the MLS approximation. Error estimates of the SMLS approximation were conducted for the approximation function and its arbitrary-order derivatives. Finally, some numerical examples were given. The numerical results indicate that the SMLS approximation provides monotonic convergence and higher accuracy with higher computational stability in comparison with the MLS approximation.
- meshless method,
- scaled moving least squares approximation,
- stability,
- error estimate

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References(18)

References

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