典型文献
KNOT PLACEMENT FOR B-SPLINE CURVE APPROXIMATION VIA l∞,1-NORM AND DIFFERENTIAL EVOLUTION ALGORITHM
文献摘要：
In this paper,we consider the knot placement problem in B-spline curve approximation.A novel two-stage framework is proposed for addressing this problem.In the first step,the l∞,1-norm model is introduced for the sparse selection of candidate knots from an initial knot vector.By this step,the knot number is determined.In the second step,knot positions are formulated into a nonlinear optimization problem and optimized by a global optimization algorithm—the differential evolution algorithm(DE).The candidate knots selected in the first step are served for initial values of the DE algorithm.Since the candidate knots provide a good guess of knot positions,the DE algorithm can quickly converge.One advantage of the proposed algorithm is that the knot number and knot positions are determined automatically.Compared with the current existing algorithms,the proposed algorithm finds approximations with smaller fitting error when the knot number is fixed in advance.Furthermore,the proposed algorithm is robust to noisy data and can handle with few data points.We illustrate with some examples and applications.
文献关键词：
中图分类号：
作者姓名：
Jiaqi Luo;Hongmei Kang;Zhouwang Yang
作者机构：
School of Mathematical Sciences,Soochow University,Suzhou 215008,China;School of Mathematical Sciences,University of Science and Technology of China,Hefei 230026,China
文献出处：
引用格式：
[1]Jiaqi Luo;Hongmei Kang;Zhouwang Yang-.KNOT PLACEMENT FOR B-SPLINE CURVE APPROXIMATION VIA l∞,1-NORM AND DIFFERENTIAL EVOLUTION ALGORITHM)[J].计算数学（英文版）,2022(04):589-606
A类：
KNOT,PLACEMENT,SPLINE,CURVE,APPROXIMATION,DIFFERENTIAL,EVOLUTION,ALGORITHM
B类：
FOR,VIA,NORM,AND,In,this,paper,we,consider,placement,problem,spline,curve,novel,two,stage,framework,proposed,addressing,first,step,norm,model,introduced,sparse,selection,candidate,knots,from,initial,vector,By,number,determined,second,positions,formulated,into,nonlinear,optimization,optimized,by,global,differential,evolution,DE,selected,served,values,Since,provide,good,guess,quickly,converge,One,advantage,that,automatically,Compared,current,existing,algorithms,finds,approximations,smaller,fitting,error,when,fixed,advance,Furthermore,robust,noisy,data,handle,few,points,We,illustrate,some,examples,applications
AB值：
0.483057
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