MHB How Does Rigor Balance with Intuition in the Summation of Exponential Series?

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The discussion explores the balance between rigorous mathematical treatment and intuitive reasoning in the context of summing exponential series. It highlights the challenge of justifying simplifications, such as approximating terms like $\frac{k(k-1)(k-2)}{k^3}$ to 1 for large k, as seen in the Taylor series representation of the exponential function. The author references Dunham's non-rigorous argument and Rudin's more thorough approach, seeking a middle ground. The conversation also delves into the limit of a specific sum involving binomial coefficients and powers, demonstrating that it converges to the exponential series through careful error analysis. Ultimately, the discussion emphasizes the importance of balancing rigor with intuition in mathematical derivations.
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When I was first introduced to a derivation of the taylor series representation of the exponential function here (pg 25): http://paginas.fisica.uson.mx/horacio.munguia/Personal/Documentos/Libros/Euler The_Master of Us.pdf
I noted the author, Dunham mentioning that the argument was non rigorous. I suspected since that it had to do something with the step involving simplifying the expressions like $\frac{k(k-1)(k-2)}{k^3}$ to $1$ for infinitely large $k$. Later Baby Rudin clarified with the section on $e$ in chapter 3. However I am still faced with some urge to simply argue when faced with sums like $\sum\limits_{n=0}^M {n \choose k}(\frac{x}{n})^{k}$ that terms like $\frac{n(n-1)(n-2)}{n^3}$ go to 1 for sufficiently large n and then just consider the sum $\sum\limits_{k=0}^\infty \frac{x^k}{k!}$. Is there some kind of balance between Rudin's thorough treatment and Euler's intuitive but perhaps loose argument?

The best I could think of is as follows:
Letting $f_n(x)=\sum\limits_{k=0}^n {n \choose k}(\frac{x}{n})^k$
I don't think it is hard to show $|{{f_n(x)}-\sum\limits_{k=0}^n \frac{x^k}{k!}}|<o(1)$
By considering the terms $\frac{n(n-1)}{2}x^2$ like so: $(\frac{1}{2}-\frac{1}{2n})x^2$ and then summing up all the error terms like $-\frac{1}{2n}x^2$.

This leads me to ask about this sum:

$ 1+M(\frac{f}{M})^{\alpha}+\frac{M(M-1)(\frac{f}{M})^{2\alpha}}{2!}+\frac{M(M-1)(M-2)(\frac{f}{M})^{3\alpha}}{3!}+...=\sum\limits_{k=0}^M {M \choose k}(\frac{f}{M})^{k\alpha}$

How would you find the limit of this sum as $M->\infty$?
 
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The limit of this sum can be found using the same approach as before. Letting $g_M(f)=\sum\limits_{k=0}^M {M \choose k}(\frac{f}{M})^{k\alpha}$, we can show that $|{{g_M(f)}-\sum\limits_{k=0}^M \frac{f^{k\alpha}}{k!}}|<o(1)$. By considering the terms $\frac{M(M-1)(M-2)}{3!}f^{3\alpha}$ like so: $(\frac{1}{6}-\frac{1}{6M^2})f^{3\alpha}$ and then summing up all the error terms like $-\frac{1}{6M^2}f^{3\alpha}$. The limit of the sum is therefore $\sum\limits_{k=0}^\infty \frac{f^{k\alpha}}{k!}$.
 
We all know the definition of n-dimensional topological manifold uses open sets and homeomorphisms onto the image as open set in ##\mathbb R^n##. It should be possible to reformulate the definition of n-dimensional topological manifold using closed sets on the manifold's topology and on ##\mathbb R^n## ? I'm positive for this. Perhaps the definition of smooth manifold would be problematic, though.

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