计算一阶导数的四阶中心差分格式

该文详细介绍了如何利用四阶中心差分公式来计算一阶导数,这种方法基于拉格朗日插值原理,通过周围四个点的数据构造插值曲线,并在目标点求导。最终公式展示了如何从四个相邻点的数据推导出在某点的导数值。

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本文地址:https://goodgoodstudy.blog.youkuaiyun.com/article/details/112008150

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原理:利用指定点 yty_tyt 周围的四个点(yt−2,yt−1,yt+2,yt+2)(y_{t-2}, y_{t-1}, y_{t+2},y_{t+2})(yt2,yt1,yt+2,yt+2)构造 拉格朗日插值曲线
y(x)=yt−2(x−xt−1)(x−xt+1)(x−xt+2)(xt−2−xt−1)(xt−2−xt+1)(xt−2−xt+2)+yt−1(x−xt−2)(x−xt+1)(x−xt+2)(xt−1−xt−2)(xt−1−xt+1)(xt−1−xt+2)+yt+1(x−xt−2)(x−xt−1)(x−xt+2)(xt+1−xt−2)(xt+1−xt−1)(xt+1−xt+2)+yt+2(x−xt−2)(x−xt−1)(x−xt+1)(xt+2−xt−2)(xt+2−xt−1)(xt+2−xt+1) \begin{aligned} y(x) =& y_{t-2}\frac{(x-x_{t-1})(x-x_{t+1})(x-x_{t+2})}{(x_{t-2}-x_{t-1})(x_{t-2}-x_{t+1})(x_{t-2}-x_{t+2})} \\ \\ &+y_{t-1}\frac{(x-x_{t-2})(x-x_{t+1})(x-x_{t+2})}{(x_{t-1}-x_{t-2})(x_{t-1}-x_{t+1})(x_{t-1}-x_{t+2})} \\ \\ &+y_{t+1}\frac{(x-x_{t-2})(x-x_{t-1})(x-x_{t+2})}{(x_{t+1}-x_{t-2})(x_{t+1}-x_{t-1})(x_{t+1}-x_{t+2})} \\ \\ &+y_{t+2}\frac{(x-x_{t-2})(x-x_{t-1})(x-x_{t+1})}{(x_{t+2}-x_{t-2})(x_{t+2}-x_{t-1})(x_{t+2}-x_{t+1})} \end{aligned} y(x)=yt2(xt2xt1)(xt2xt+1)(xt2xt+2)(xxt1)(xxt+1)(xxt+2)+yt1(xt1xt2)(xt1xt+1)(xt1xt+2)(xxt2)(xxt+1)(xxt+2)+yt+1(xt+1xt2)(xt+1xt1)(xt+1xt+2)(xxt2)(xxt1)(xxt+2)+yt+2(xt+2xt2)(xt+2xt1)(xt+2xt+1)(xxt2)(xxt1)(xxt+1)

分母是可以直接写出来的,记 h≡xt−xt−1h \equiv x_t - x_{t-1}hxtxt1 为自变量间隔:
y(x)=yt−2(x−xt−1)(x−xt+1)(x−xt+2)(−h)(−3h)(−4h)+yt−1(x−xt−2)(x−xt+1)(x−xt+2)(h)(−2h)(−3h)+yt+1(x−xt−2)(x−xt−1)(x−xt+2)(3h)(2h)(−h)+yt+2(x−xt−2)(x−xt−1)(x−xt+1)(4h)(3h)(h)=[−yt−2(x−xt−1)(x−xt+1)(x−xt+2)+2yt−1(x−xt−2)(x−xt+1)(x−xt+2)−2yt+1(x−xt−2)(x−xt−1)(x−xt+2)+yt+2(x−xt−2)(x−xt−1)(x−xt+1)]112h3 \begin{aligned} y(x) =& y_{t-2}\frac{(x-x_{t-1})(x-x_{t+1})(x-x_{t+2})}{(-h)(-3h)(-4h)} \\\\ &+y_{t-1}\frac{(x-x_{t-2})(x-x_{t+1})(x-x_{t+2})}{(h)(-2h)(-3h)} \\ \\ &+y_{t+1}\frac{(x-x_{t-2})(x-x_{t-1})(x-x_{t+2})}{(3h)(2h)(-h)} \\ \\ &+y_{t+2}\frac{(x-x_{t-2})(x-x_{t-1})(x-x_{t+1})}{(4h)(3h)(h)} \\\\ =&[ -y_{t-2}(x-x_{t-1})(x-x_{t+1})(x-x_{t+2})\\ & + 2y_{t-1}(x-x_{t-2})(x-x_{t+1})(x-x_{t+2})\\ & -2y_{t+1}(x-x_{t-2})(x-x_{t-1})(x-x_{t+2})\\ & + y_{t+2}(x-x_{t-2})(x-x_{t-1})(x-x_{t+1}) ]\frac{1}{12h^3} \end{aligned} y(x)==yt2(h)(3h)(4h)(xxt1)(xxt+1)(xxt+2)+yt1(h)(2h)(3h)(xxt2)(xxt+1)(xxt+2)+yt+1(3h)(2h)(h)(xxt2)(xxt1)(xxt+2)+yt+2(4h)(3h)(h)(xxt2)(xxt1)(xxt+1)[yt2(xxt1)(xxt+1)(xxt+2)+2yt1(xxt2)(xxt+1)(xxt+2)2yt+1(xxt2)(xxt1)(xxt+2)+yt+2(xxt2)(xxt1)(xxt+1)]12h31

接下来就是求该三次函数在 xtx_txt 处的导数 y′(xt)y'(x_{t})y(xt)
y′(x)={−yt−2[(x−xt+1)(x−xt+2)+(x−xt−1)(x−xt+2)+(x−xt−1)(x−xt+1)]+2yt−1[(x−xt+1)(x−xt+2)+(x−xt−2)(x−xt+2)+(x−xt−2)(x−xt+1)]−2yt+1[(x−xt−1)(x−xt+2)+(x−xt−2)(x−xt+2)+(x−xt−2)(x−xt−1)]+yt+2[(x−xt−1)(x−xt+1)+(x−xt−2)(x−xt+1)+(x−xt−2)(x−xt−1)]}112h3 \begin{aligned} y'(x) =& \{ -y_{t-2}[(x-x_{t+1})(x-x_{t+2})+(x-x_{t-1})(x-x_{t+2})+(x-x_{t-1})(x-x_{t+1})]\\ \\ & + 2y_{t-1}[(x-x_{t+1})(x-x_{t+2})+(x-x_{t-2})(x-x_{t+2})+(x-x_{t-2})(x-x_{t+1})]\\ \\ & -2y_{t+1}[(x-x_{t-1})(x-x_{t+2})+(x-x_{t-2})(x-x_{t+2})+(x-x_{t-2})(x-x_{t-1})]\\ \\ & + y_{t+2}[(x-x_{t-1})(x-x_{t+1})+(x-x_{t-2})(x-x_{t+1})+(x-x_{t-2})(x-x_{t-1})] \}\frac{1}{12h^3} \end{aligned} y(x)={yt2[(xxt+1)(xxt+2)+(xxt1)(xxt+2)+(xxt1)(xxt+1)]+2yt1[(xxt+1)(xxt+2)+(xxt2)(xxt+2)+(xxt2)(xxt+1)]2yt+1[(xxt1)(xxt+2)+(xxt2)(xxt+2)+(xxt2)(xxt1)]+yt+2[(xxt1)(xxt+1)+(xxt2)(xxt+1)+(xxt2)(xxt1)]}12h31
所以
y′(xt)={−yt−2[(xt−xt+1)(xt−xt+2)+(xt−xt−1)(xt−xt+2)+(xt−xt−1)(xt−xt+1)]+2yt−1[(xt−xt+1)(xt−xt+2)+(xt−xt−2)(xt−xt+2)+(xt−xt−2)(xt−xt+1)]−2yt+1[(xt−xt−1)(xt−xt+2)+(xt−xt−2)(xt−xt+2)+(xt−xt−2)(xt−xt−1)]+yt+2[(xt−xt−1)(xt−xt+1)+(xt−xt−2)(xt−xt+1)+(xt−xt−2)(xt−xt−1)]}112h3={−yt−2[(−h)(−2h)+(h)(−2h)+(h)(−h)]+2yt−1[(−h)(−2h)+(2h)(−2h)+(2h)(−h)]−2yt+1[(h)(−2h)+(2h)(−2h)+(2h)(h)]+yt+2[(h)(−h)+(2h)(−h)+(2h)(h)]}112h3=(yt−2−8yt−1+8yt+1−yt+2)112h \begin{aligned} y'(x_t) =& \{ -y_{t-2}[(x_t-x_{t+1})(x_t-x_{t+2})+(x_t-x_{t-1})(x_t-x_{t+2})+(x_t-x_{t-1})(x_t-x_{t+1})]\\ \\ & + 2y_{t-1}[(x_t-x_{t+1})(x_t-x_{t+2})+(x_t-x_{t-2})(x_t-x_{t+2})+(x_t-x_{t-2})(x_t-x_{t+1})]\\ \\ & -2y_{t+1}[(x_t-x_{t-1})(x_t-x_{t+2})+(x_t-x_{t-2})(x_t-x_{t+2})+(x_t-x_{t-2})(x_t-x_{t-1})]\\ \\ & + y_{t+2}[(x_t-x_{t-1})(x_t-x_{t+1})+(x_t-x_{t-2})(x_t-x_{t+1})+(x_t-x_{t-2})(x_t-x_{t-1})] \}\frac{1}{12h^3} \\ \\ =& \{ -y_{t-2}[(-h)(-2h)+(h)(-2h)+(h)(-h)]\\ \\ & + 2y_{t-1}[(-h)(-2h)+(2h)(-2h)+(2h)(-h)]\\ \\ & -2y_{t+1}[(h)(-2h)+(2h)(-2h)+(2h)(h)]\\ \\ & + y_{t+2}[(h)(-h)+(2h)(-h)+(2h)(h)] \}\frac{1}{12h^3} \\ \\ =& ( y_{t-2} -8y_{t-1} +8y_{t+1}- y_{t+2} )\frac{1}{12h} \\ \end{aligned} y(xt)==={yt2[(xtxt+1)(xtxt+2)+(xtxt1)(xtxt+2)+(xtxt1)(xtxt+1)]+2yt1[(xtxt+1)(xtxt+2)+(xtxt2)(xtxt+2)+(xtxt2)(xtxt+1)]2yt+1[(xtxt1)(xtxt+2)+(xtxt2)(xtxt+2)+(xtxt2)(xtxt1)]+yt+2[(xtxt1)(xtxt+1)+(xtxt2)(xtxt+1)+(xtxt2)(xtxt1)]}12h31{yt2[(h)(2h)+(h)(2h)+(h)(h)]+2yt1[(h)(2h)+(2h)(2h)+(2h)(h)]2yt+1[(h)(2h)+(2h)(2h)+(2h)(h)]+yt+2[(h)(h)+(2h)(h)+(2h)(h)]}12h31(yt28yt1+8yt+1yt+2)12h1

这就是计算一阶导数的四阶中心差分格式!

### 关于一阶导数的七点六阶中心差分格式 在一维空间中,对于函数 \( f(x) \),其一阶导数可以通过数值方法近似表示。为了达到更高的精度,通常会采用高阶差分格式来逼近导数项。其中,七点六阶中心差分格式是一种常见的高阶差分方法。 #### 差分公式的推导 假设已知网格上的节点值 \( f_{i-3}, f_{i-2}, f_{i-1}, f_i, f_{i+1}, f_{i+2}, f_{i+3} \),则通过泰勒展开并组合这些节点值得到如下公式: \[ f'(x_i) = \frac{-c_3 f_{i-3} + c_2 f_{i-2} - c_1 f_{i-1} + c_0 f_i + c_1 f_{i+1} - c_2 f_{i+2} + c_3 f_{i+3}}{h} \] 系数 \( c_0, c_1, c_2, c_3 \) 的具体表达形式取决于所期望的截断误差阶次。经过理论分析得出,当目标是一阶导数且希望达到六阶精度时,对应的系数为[^5]: \[ c_0 = 0,\quad c_1 = \frac{9}{8},\quad c_2 = -\frac{7}{6},\quad c_3 = \frac{1}{24}. \] 因此,最终的七点六阶中心差分公式可以写成: \[ f'(x_i) = \frac{1}{24h}\left(-f_{i-3} + 9f_{i-2} - 45f_{i-1} + 45f_{i+1} - 9f_{i+2} + f_{i+3}\right). \] 此公式具有较高的计算复杂度,但在实际应用中能够显著提高数值解的准确性[^5]。 #### Python 实现示例 以下是基于上述公式的简单Python实现代码片段: ```python def seven_point_derivative(f_values, h): """ 计算给定数据点的一阶导数,使用七点六阶中心差分格式 参数: f_values (list): 函数值列表,长度至少为7. h (float): 步长. 返回: list: 对应位置的一阶导数值. """ n = len(f_values) derivatives = [] for i in range(3, n - 3): derivative = ( (-f_values[i-3] + 9*f_values[i-2] - 45*f_values[i-1] + 45*f_values[i+1] - 9*f_values[i+2] + f_values[i+3]) / (24 * h)) derivatives.append(derivative) return derivatives ``` 该算法适用于均匀网格情况下的数值求导问题,并能有效减少因低阶差分带来的舍入误差影响[^5]。
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