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lagrange interpolation polynomial相关的网络例句

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First, we introduce and discuss the various methods of multivariate polynomial interpolation in the literature. Based on this study, we state multivariate Lagrange interpolation over again from algebraic geometry viewpoint:Given different interpolation nodes A1,A2 .....,An in the affine n-dimensional space Kn, and accordingly function values fi(i = 1,..., m), the question is how to find a polynomial p K[x1, x2,...,xn] satisfying the interpolation conditions:where X=(x1,X2,....,xn). Similarly with univariate problem, we have provedTheorem If the monomial ordering is given, a minimal ordering polynomial satisfying conditions (1) is uniquely exsisted.Such a polynomial can be computed by the Lagrange-Hermite interpolation algorithm introduced in chapter 2. Another statement for Lagrange interpolation problem is:Given monomials 1 ,2 ,.....,m from low degree to high one with respect to the ordering, some arbitrary values fi(i= 1,..., m), find a polynomial p, such thatIf there uniquely exists such an interpolation polynomial p{X, the interpolation problem is called properly posed.

文中首先对现有的多元多项式插值方法作了一个介绍和评述,在此基础上我们从代数几何观点重新讨论了多元Lagrange插值问题:给定n维仿射空间K~n中两两互异的点A_1,A_2,…,A_m,在结点A_i处给定函数值f_i(i=1,…,m),构造多项式p∈K[X_1,X_2,…,X_n],满足Lagrange插值条件:p=f_i,i=1,…,m (1)其中X=(X_1,X_2,…,X_n),与一元情形相似地,本文证明了定理满足插值条件(1)的多项式存在,并且按"序"最低的多项式是唯一的,上述多项式可利用第二章介绍的Lagrange-Hermite插值算法求出,Lagrange插值另一种描述是:按序从低到高给定单项式ω_1,ω_2,…,ω_m,对任意给定的f_1,f_2,…,f_m,构造多项式p,满足插值条件:p=sum from i=1 to m=Ai=f_i,i=1,…,m (2)如果插值多项式p存在且唯一,则称插值问题适定。

In theory of approximations, the classic methods of polynomial approximation for rational expression are various interpolations and operator approximations, such as Lagrange interpolation, Hermite interpolation and Bernstein polynomial approximation.

在逼近论中,用多项式逼近有理式的经典的方法是各种插值与算子逼近方法,如Lagrange插值、Hermite插值和Bernstein多项式逼近等。

How to construct Lagrange interpolation polynomial is quite important.

如何构造多元Lagrange插值多项式是十分关键的。

Clearly, Lagrange interpolation nodes are used the more the number of interpolation polynomial higher.

显然,Lagrange插值中使用的节点越多,插值多项式的次数就越高。

The problem of Lagrange interpolation of polynomial space in space Rs is studied,and the construction of Lagrange interpolation polynomial in space R1 and space R2 is proposed.

研究空间Rs 中多项式空间中的Lagrange插值问题。给出了R1和R2上Lagrange插值多项式的构造,同时,给出了R2上插值问题的几个例子。另外,给出了矩形网点上的Lagrange插值多项式和三角形网点上的Lagrange插值多项式。讨论了Rs空间中的Lagrange插值多项式及其余项

Romberg first use of the method is integral for integration, Then the results obtained by using the interpolation method were obtained Lagrange polynomial interpolation polynomial interpolation and Newton, re-use of least squares fitting of thinking obtained polynomial, the last of these different types of polynomial, identify their respective strengths and weaknesses.

首先运用Romberg积分方法对给出定积分进行积分,然后对得到的结果用插值方法,分别求出Lagrange插值多项式和Newton插值多项式,再运用最小二乘法的思想求出拟合多项式,最后对这些不同类型多项式进行比较,找出它们各自的优劣。

It first uses the integrating method of Romberg , which is an improved trapezoidal integration, to solve the given definite integral,then we create Lagrange's interpolation polynomial and Newton's interpolation polynomial.

首先运用Romberg积分方法对给出定积分进行积分,然后对得到的结果用插值方法,分别求出Lagrange插值多项式和Newton插值多项式,再运用最小二乘法的思想求出拟合多项式,最后对这些不同类型多项式进行比较,找出它们各自的优劣。

Here pis 1 or 2.In chapter 8. we extend the Runge-Kutta methods to variable delay differentialalgebraic system. It is proved that if the Runge-Kutta method which is algebraicallystable and diagonally stable is consistent with order p , the extended Runge-Kuttamethod with Lagrange interpolation procedure is D_A-convergence with order M. HereM=min{p, u + q + 1 }, and u + q is the degree of Lagrange interpolation polynomial.

第八章将求解常微分方程的Runge-Kutta方法改造后用于求解变延迟微分代数系统,并且证明如果代数稳定且对角稳定的Runge-Kutta方法对于常微分方程初值问题在经典意义下是p阶相容的,那么具有Lagrange插值过程的该方法是M阶D_A-收敛的,M=min{p,u+q+1},u+q为Lagrange插值多项式的次数。

From our results we know that the average error of the Lagrange interpolation sequence and the Hermite interpolation sequence based on the Chebyshev nodes in the 1-fold integrated Wiener space equal weakly to the average error of their corresponding optimal approximation polynomial in the 1-fold integrated Wiener space,and as a kind of information-based operation,they have simple form and their recover functions are polynomials,in the 1-fold integrated wiener space,their average error equal weakly to the corresponding minimal information radius whose permissible information operators class is function values.

通过我们的结果可以知道,基于第一类Chebyshev多项式零点的Lagrange插值算子列和Hermite插值算子列在1-重积分Wiener空间下的平均误差弱等价于相应的最佳逼近多项式在1-重积分Wiener空间下的平均误差,并且作为形式简单且恢复函数为多项式的一种信息基算法,其在1-重积分Wiener空间下的平均误差弱等价于相应的以函数值计算为可允许信息算子的最小平均信息半径。

First, we introduce and discuss the various methods of multivariate polynomial interpolation in the literature. Based on this study, we state multivariate Lagrange interpolation over again from algebraic geometry viewpoint:Given different interpolation nodes A1,A2 .....,An in the affine n-dimensional space Kn, and accordingly function values fi(i = 1,..., m), the question is how to find a polynomial p K[x1, x2,...,xn] satisfying the interpolation conditions:where X=(x1,X2,....,xn). Similarly with univariate problem, we have provedTheorem If the monomial ordering is given, a minimal ordering polynomial satisfying conditions (1) is uniquely exsisted.Such a polynomial can be computed by the Lagrange-Hermite interpolation algorithm introduced in chapter 2. Another statement for Lagrange interpolation problem is:Given monomials 1 ,2 ,.....,m from low degree to high one with respect to the ordering, some arbitrary values fi(i= 1,..., m), find a polynomial p, such thatIf there uniquely exists such an interpolation polynomial p{X, the interpolation problem is called properly posed.

文中首先对现有的多元多项式插值方法作了一个介绍和评述,在此基础上我们从代数几何观点重新讨论了多元Lagrange插值问题:给定n维仿射空间K~n中两两互异的点A_1,A_2,…,A_m,在结点A_i处给定函数值f_i(i=1,…,m),构造多项式p∈K[X_1,X_2,…,X_n],满足Lagrange插值条件:p=f_i,i=1,…,m (1)其中X=(X_1,X_2,…,X_n),与一元情形相似地,本文证明了定理满足插值条件(1)的多项式存在,并且按&序&最低的多项式是唯一的,上述多项式可利用第二章介绍的Lagrange-Hermite插值算法求出,Lagrange插值另一种描述是:按序从低到高给定单项式ω_1,ω_2,…,ω_m,对任意给定的f_1,f_2,…,f_m,构造多项式p,满足插值条件:p=sum from i=1 to m=Ai=f_i,i=1,…,m (2)如果插值多项式p存在且唯一,则称插值问题适定。

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