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• W3087853493 abstract "In this paper, we present a progressive and iterative approximation method with memory for least square fitting (MLSPIA). It adjusts the control points and the weighted sums iteratively to construct a series of fitting curves (surfaces) with three weights. For any normalized totally positive basis, even when the collocation matrix is of deficient column rank, we obtain a condition to guarantee that these curves (surfaces) converge to the least square fitting curve (surface) to the given data points. It is proved that the theoretical convergence rate of the method is faster than the one of the progressive and iterative approximation method for least square fitting (LSPIA) in Deng and Lin (2014) under the same assumption. Examples verify this phenomenon." @default.
• W3087853493 created "2020-10-01" @default.
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• W3087853493 date "2020-10-01" @default.
• W3087853493 modified "2023-10-09" @default.
• W3087853493 title "On a progressive and iterative approximation method with memory for least square fitting" @default.
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• W3087853493 doi "https://doi.org/10.1016/j.cagd.2020.101931" @default.
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