Approximate GCD by bernstein basis, and its applications

Kosaku Nagasaka
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引用次数: 1

Abstract

For the given pair of univariate polynomials generated by empirical data hence with a priori error on their coefficients, computing their greatest common divisor can be done by several known approximate GCD algorithms that are usually for polynomials represented by the power polynomial basis (power form). Recently, there are studies on approximate GCD of polynomials represented by not the power polynomial basis, and especially the Bernstein polynomial basis (Bernstein form) is one of them. we are interested in computing approximate GCD of polynomials in the power form but their perturbation is measured by the Euclidean norm of perturbation in the Bernstein form, and we introduce its applications for computing a reduced rational function, the rational function approximation and Padé approximation to get a better approximation in L2-norm on [0, 1].
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用bernstein基近似GCD及其应用
对于由经验数据生成的单变量多项式,其系数存在先验误差,计算其最大公约数可以通过几种已知的近似GCD算法来完成,这些算法通常用于幂多项式基(幂形式)表示的多项式。近年来,人们对非幂多项式基表示的多项式的近似GCD进行了研究,特别是Bernstein多项式基(Bernstein形式)就是其中之一。我们感兴趣的是计算幂形式多项式的近似GCD,但它们的摄动是通过伯恩斯坦形式的欧几里得摄动范数来测量的,我们介绍了它在计算约有理函数、有理函数近似和pad近似中的应用,以得到在l2范数上[0,1]的更好的近似。
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