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泰勒定理与导数的局限

带拉格朗日余项的泰勒定理、余项控制着什么,以及对导数能与不能告诉你的诚实清算——包括达布的意外。

带余项的泰勒定理

泰勒定理是带更多导数的中值定理。中值定理是它的一阶情形:f(b) = f(a) + f′(c)(b − a)。当有 n 阶导数可用时,我们用一个多项式在 a 处匹配前 n 阶,而余项度量这个差距。拉格朗日形式把这个差距放在一个隐藏点上——正是中值定理的精神,升高一阶。

Taylor's theorem (Lagrange remainder). Suppose f has n+1 derivatives on an
open interval containing a and x. Then there is a point c strictly between a and x
with
    f(x) = P_n(x) + R_n(x),
where the degree-n Taylor polynomial at a is
    P_n(x) = f(a) + f'(a)(x-a) + f''(a)/2! (x-a)^2 + ... + f^(n)(a)/n! (x-a)^n,
and the remainder is
    R_n(x) = f^(n+1)(c) / (n+1)! · (x - a)^(n+1).

Worked estimate: f(x) = e^x at a = 0, n = 2, on [0, 1].
    P_2(x) = 1 + x + x^2/2,   R_2(x) = e^c/6 · x^3  for some c in (0, x).
For x in [0,1], c < 1 so e^c < e < 3, giving the honest bound
    |e^x - (1 + x + x^2/2)| = |R_2(x)| <= 3/6 · x^3 = x^3 / 2.
At x = 0.1:  error <= (0.1)^3 / 2 = 0.0005.  (True error ≈ 0.00017 — within bound.)
余项是高一阶的导数,取自未知点 c,除以 (n+1)!——界住那个导数,就界住了误差。

导数告诉你什么,又不告诉你什么

导数强大,却非全知。两个诚实的告诫为本轨道收尾。第一,光滑不等于解析:一个函数可以在某点拥有所有高阶导数,却仍不等于它在那里的泰勒级数。第二,导数在一个特定方面比连续函数更古怪,这由达布定理刻画。

The classic smooth-but-not-analytic example:
    f(x) = exp(-1/x^2)  for x ≠ 0,    f(0) = 0.

One can show (by induction, using exp decay beating every polynomial) that
    f^(n)(0) = 0   for EVERY n = 0, 1, 2, ...
So every Taylor coefficient at 0 is 0, and the Taylor polynomial P_n is the
zero polynomial for all n. Its Taylor 'series' is therefore 0.

But f(x) > 0 for every x ≠ 0. So the Taylor series (= 0) does NOT equal f
near 0. Here the remainder R_n(x) = f(x) - 0 = f(x) does NOT go to 0.

Moral: infinitely differentiable (smooth) is strictly weaker than real-analytic.
所有导数在 0 处都为零,函数却不为零——泰勒级数收敛到了错误的对象。

最后,达布定理说每个导数都具有介值性质:若 f 在 [a, b] 上可微,则 f′ 取遍 f′(a) 与 f′(b) 之间的每个值——即便 f′ 不必连续。所以导数不能有跳跃间断。这正是“导数并不告诉你一切”的确切边界:它可以不连续,但绝不会以跳跃的方式不连续。