Riemann sum of derivative (something like that)

quasar987
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Hi, maybe someone can help. When I think about it, I'm pretty sure that the following is true: Let c be a curve parametrized by t\in [a,b], let \sigma = \{t_0,...,t_N\} be a partition of [a,b] and \delta_{\sigma}=\max_{0\leq k \leq N-1}(t_{k+1}-t_k). Also define \Delta t_k=t_{k+1}-t_k Then,

\lim_{\delta_{\sigma}\rightarrow 0}\sum_{k=0}^{N-1}\frac{|c(t_k+\Delta t_k)-c(t_k)|}{\Delta t_k}\Delta t_k=\int_a^b |\frac{dc}{dt}(t)|dt

Proving this would also amount to proving

\lim_{\delta_{\sigma}\rightarrow 0}\sum_{k=0}^{N-1}\frac{|c(t_k+\Delta t_k)-c(t_k)|}{\Delta t_k}\Delta t_k=\lim_{\delta_{\sigma}\rightarrow 0}\sum_{k=0}^{N-1} |\frac{dc}{dt}(t_k)|\Delta t_k

Is there a way to do this using a finite succession of arguments?
 
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If it can be done directly, it looks like a differential approximation should be the obvious thing to do! Remember that for any differentiable f, there is a remainder r such that:

<br /> f(x + k) = f(x) + k f&#039;(x) + k r(x, k)<br />

and, for each x,

<br /> \lim_{k \rightarrow 0} r(x, k) = 0<br />


Of course, what you want to prove is trivial over any interval where c is monotonic.
 
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Hi Hurkyl,

If I make that substitution in the riemann integral, I get

\lim_{max(k_i)\rightarrow 0} \sum_i^N \frac{f(x_i+k_i)-f(x_i)}{k_1}k_i = \int_a^b f&#039;(x)dx + \lim_{max(k_i)\rightarrow 0} \sum_i^N r(x_i,k_i)k_i

So the problem is essentially the same: In my OP, I knew that the differential ratio was going to the derivative as \delta_{\sigma} \rightarrow 0 so the limit of the riemann sum should be \int_a^b |\frac{dc}{dt}(t)|dt, but did not know how to prove it. Now I know that as max(k)-->0, r(x,k)-->0, so the limit of the riemann sum should be \int_a^b 0dx, but still don't know how to prove it.
 
If you take off the absolute value signs, then it's very easy. Your sum is a telescoping series, and your integral is easily integrated.

If you don't want to use that... do you know about uniform convergence?


Anyways... *bonks self* forget about differential approximation. This is a job for the mean value theorem!
 
Hurray for Hurkyl and the mean value theorem! :D
 

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