《具体数学》2 Sum Study Note (2)

Repertoire method

If we want to calculate : ∑i=1ni2\sum_{i=1}^n i^2, we can construct a recursion :

{R0=αRn=Rn−1+β+γn+δn2\begin{cases} R_0 = \alpha \\ R_n = R_{n-1} + \beta + \gamma n + \delta n^2 \end{cases}

And it must satisfy :

Rn=A(n)α+B(n)β+C(n)γ+D(n)δR_n = A(n)\alpha + B(n)\beta + C(n)\gamma + D(n)\delta

Consider that we have already known A(n),B(n),C(n)A(n),B(n),C(n), now I will demonstrate to you how to calculate D(n)D(n) :

Let Rn=n3R_n = n^3 , so α=0\alpha = 0, and :

n3−(n−1)3=3n2−3n+1β=1,  γ=−3,  δ=3\begin{align*} n^3 - (n-1)^3 &= 3n^2 - 3n + 1\\ \\ \beta = 1 ,\; \gamma &= -3 ,\; \delta = 3 \end{align*}

Therefore :

Rn=n3=A(n)⋅α+B(n)β+C(n)γ+D(n)δR_n = n^3 = A(n) \cdot \alpha + B(n)\beta + C(n)\gamma + D(n)\delta

Since only D(n)D(n) remains unknown, we may solve for it directly !

Expansion method

For calculating ∑k=1nk2\sum_{k=1}^n k^2, this is a one-fold sum, yet we may rewrite it as a double sum :

∑k=1nk2=∑k=1n[k∑j=1k1]=∑k=1n∑j=1kk=∑j=1n∑k=jnk=∑j=1n(j+n)(n−j+1)2\begin{align*} \sum_{k=1}^{n} k^2 &= \sum_{k=1}^{n} \left[ k \sum_{j=1}^{k} 1 \right] \\ &= \sum_{k=1}^{n} \sum_{j=1}^{k} k \\ &= \sum_{j=1}^{n} \sum_{k=j}^{n} k \\ &= \sum_{j=1}^{n} \frac{(j + n)(n - j + 1)}{2} \end{align*}

That is easy.

Difference

Definition :

g(x)=Δf(x)⟺∑g(x) dx=f(x)+Cg(x) = \Delta f(x) \quad \Longleftrightarrow \quad \sum g(x) \, dx = f(x) + C

For example :

∑0≤k<nkm‾=km+1‾m+1∣0n=nm+1‾m+1−0m+1‾m+1=nm+1‾m+1\sum_{0 \le k < n} k^{\underline{m}} = \left. \frac{k^{\underline{m+1}}}{m+1} \right|_{0}^{n} = \frac{n^{\underline{m+1}}}{m+1}-\frac{0^{\underline{m+1}}}{m+1}=\frac{n^{\underline{m+1}}}{m+1}

Proof :

As :

Δ(xm‾)=(x+1)m‾−xm‾=(x+1)⋅x⋅(x−1)⋯(x−m+2)−x⋅(x−1)⋯(x−m+1)=m⋅xm−1‾\begin{align*} \Delta(x^{\underline{m}}) &= (x+1)^{\underline{m}} - x^{\underline{m}}\\ &= (x+1) \cdot x \cdot (x-1) \cdots (x - m + 2) - x \cdot (x-1) \cdots (x - m + 1)\\ &= m \cdot x^{\underline{m-1}} \end{align*}

So :

xm‾=Δ(xm+1‾)m+1x^{\underline{m}} = \frac{\Delta\left(x^{\underline{m+1}}\right)}{m+1}

Below are more common differences :

image-20260719145810421

  • xm‾x^{\underline {m}} is like xmx^m in finite
  • 2x2^x is like exe^x in finite
  • HxH_x is like ln⁡x\ln x in finite

So the whole calculation of xm‾x^{\underline m} is :

∑abxm‾δx={xm+1‾m+1∣ab,m≠−1,Hx∣ab,m=−1.\sum_{a}^{b} x^{\underline{m}} \delta x= \begin{cases} \left.\displaystyle \frac{x^{\underline{m+1}}}{m+1}\right|_{a}^{b}, & m \neq -1,\\[6pt] \left.H_x\right|_{a}^{b}, & m = -1. \end{cases}


Δ(u(x)v(x))=u(x+1)v(x+1)−u(x)v(x)=u(x+1)v(x+1)−u(x)v(x+1)+u(x)v(x+1)−u(x)v(x)=u(x)Δv(x)+v(x+1)Δu(x)\begin{align*} \Delta\big(u(x)v(x)\big) &= u(x+1)v(x+1) - u(x)v(x) \\ &= u(x+1)v(x+1) - u(x)v(x+1) \\ &\quad + u(x)v(x+1) - u(x)v(x) \\ &= u(x)\Delta v(x) + v(x+1)\Delta u(x) \end{align*}

Here, Ef(x)=f(x+1)Ef(x)=f(x+1). We refer to E as the shift operator.


Give you an example :

*English improve

  1. And it must have to be -> And it must satisfy
  2. Only $D(n)$ is unknown, so we can get it easily ! -> Since only $D(n)$ remains unknown, we may solve for it directly !
  3. but we can change it -> yet we may rewrite it as
  4. Here's -> Below are
  5. we called it -> we refer to E as