Why Does the Multidimensional Chain Rule Lead to Euler's Theorem Proof?

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SUMMARY

The discussion focuses on applying the multidimensional chain rule to derive Euler's theorem, specifically the transformation \(\sum\limits_{i=1}^{n} \frac{\partial f}{\partial x_i}(x) \cdot x_i= \frac{d f(tx)}{dt} \big|_{t=1}\). The participant clarifies that the left-hand side represents a sum of partial derivatives, while the right-hand side is a derivative evaluated at \(t=1\). The key insight is recognizing that both expressions must be equal when evaluated correctly, highlighting the relationship between the chain rule in multiple dimensions and the properties of homogeneous functions.

PREREQUISITES
  • Understanding of the multidimensional chain rule in calculus
  • Familiarity with partial derivatives and their notation
  • Knowledge of homogeneous functions and Euler's theorem
  • Basic differentiation techniques in multivariable calculus
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  • Study the application of the multidimensional chain rule in various contexts
  • Explore the properties of homogeneous functions and their implications
  • Learn about the derivation and applications of Euler's theorem
  • Practice problems involving partial derivatives and their sums
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Students and educators in mathematics, particularly those studying multivariable calculus, as well as researchers interested in the applications of Euler's theorem and the multidimensional chain rule.

Lambda96
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Homework Statement
Proof ##\sum\limits_{i=1}^{n} x_i \frac{\partial f}{\partial x_i}(x)=n f(x)##
Relevant Equations
Chain rule
Hi,

I am having problems with the following task:

Bildschirmfoto 2024-06-13 um 14.38.20.png

My lecturer gave me the tip that I should apply the multidimensional chain rule to obtain the following transformation ##\sum\limits_{i=1}^{n} \frac{\partial f}{\partial x_i}(x) \cdot x_i= \frac{d f(tx)}{dt} \big|_{t=1}##

Unfortunately, I don't know why the two terms should be equal because of the chain rule. There is a sum on the left and a derivative on the right side.

One more thing, in the 1D case the chain rule is ##[f(g(x))]'=f'(g(x)) \cdot g'(x)## but the term ##g'(x)## for the multidimensional case is missing on the right-hand side.
 
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You have two ways of calculating \frac{d}{dt}f(tx). One is to apply the chain rule, as you would for any function: <br /> \frac{d}{dt}f(tx) = \sum_i \frac{\partial f}{\partial x_i} \frac{d}{dt}(tx_i). The other is to use the particular feature of f that f(tx) = t^nf(x): <br /> \frac{d}{dt}f(tx) = \left(\frac{d}{dt} t^n\right)f(x). These two expressions must be equal to each other. What then happens if you evaluate the derivatives and set t = 1?
 
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Thank you pasmith for your help 👍


I think I'm beginning to understand the derivation now. I was also wondering all the time how the ##t## gets into the equation, but since you evaluate the derivative for ##t=1##, you can claim that the two derivatives are equal.
 

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