Multivariable Differentiation - Component Functions ....

In summary, Duistermaat and Kolk provide a proof of Proposition 2.2.9 which states that Lemma 1.1.7 implies the proposition.
  • #1
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I am reading "Multidimensional Real Analysis I: Differentiation" by J. J. Duistermaat and J. A. C. Kolk ...

I am focused on Chapter 2: Differentiation ... ...

I need help with the proof of Proposition 2.2.9 ... ...

Duistermaat and Kolk's Proposition 2.2.9 read as follows:
D&K - 1 - Proposition 2.2.9 ...  .... PART 1 ... .png

In the above text D&K state that Lemma 1.1.7 (iv) implies Proposition 2.2.9 ...

Can someone please indicate how/why ths is the case ...

Peter
===========================================================================================The above post mentions Lemma 1.1.7 ... so I am providing the text of the same ... as follows:

D&K - 1 -  Lemma 1.1.7 ... PART 1 ... .png

D&K - 2 -  Lemma 1.1.7 ... PART 2 ... . .png
 

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  • D&K - 1 -  Lemma 1.1.7 ... PART 1 ... .png
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Differentiability of a function ##g(x)## means: ##g(a+v)-g(a)-((D_a)(g))(v) = \varepsilon_a(v)## with ##\lim_{v \to 0}\dfrac{\varepsilon_a(v)}{||\varepsilon_a(v)||}=0\,.##
Now ##D_a(f_i)## are all linear iff ##D_a(f)## is. With the lemma we get
$$
|f_i(a+v)-f_i(a)-((D_a)(f_i))(v)| \leq \sqrt{n}\cdot ||f(a+v)-f(a)-((D_a)(f))(v)|| = \sqrt{n} \cdot ||\varepsilon_a(v)|| =: || \psi_a(v)||
$$
with ##\psi_a = \sqrt{n}\cdot \varepsilon_a## and ##\lim_{v\to 0}\dfrac{\psi_a(v)}{||\psi_a(v)||} = 0##, i.e. the differentiability of ##f## gives the differentiability of the component functions ##f_i\,.## The other inequality ##||y|| \leq |\sum_{i=1}^n\,y_i|## gives the other estimation ##||f(a+v)-f(a)-((D_a)(f))(v)|| \leq \sum \ldots ## with ##\varepsilon_a(v)= \sum (\varepsilon_i)_a (v_i)\,.##

As an important note here: Proposition 2.2.9 is about the component functions of ##f##, not the partial derivatives, i.e. not about the components of ##x##. The situation with partial derivatives is less strong:
  • Total differentiability implies continuity.
  • Total differentiability implies partial differentiability in all coordinates.
  • Partial differentiability does not imply continuity and thus not total differentiability.
  • Continuous partial differentiability, i.e. functions with continuous partial derivatives are also continuous total differentiable.
 
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  • #3
fresh_42 said:
Differentiability of a function ##g(x)## means: ##g(a+v)-g(a)-((D_a)(g))(v) = \varepsilon_a(v)## with ##\lim_{v \to 0}\dfrac{\varepsilon_a(v)}{||\varepsilon_a(v)||}=0\,.##
Now ##D_a(f_i)## are all linear iff ##D_a(f)## is. With the lemma we get
$$
|f_i(a+v)-f_i(a)-((D_a)(f_i))(v)| \leq \sqrt{n}\cdot ||f(a+v)-f(a)-((D_a)(f))(v)|| = \sqrt{n} \cdot ||\varepsilon_a(v)|| =: || \psi_a(v)||
$$
with ##\psi_a = \sqrt{n}\cdot \varepsilon_a## and ##\lim_{v\to 0}\dfrac{\psi_a(v)}{||\psi_a(v)||} = 0##, i.e. the differentiability of ##f## gives the differentiability of the component functions ##f_i\,.## The other inequality ##||y|| \leq |\sum_{i=1}^n\,y_i|## gives the other estimation ##||f(a+v)-f(a)-((D_a)(f))(v)|| \leq \sum \ldots ## with ##\varepsilon_a(v)= \sum (\varepsilon_i)_a (v_i)\,.##

As an important note here: Proposition 2.2.9 is about the component functions of ##f##, not the partial derivatives, i.e. not about the components of ##x##. The situation with partial derivatives is less strong:
  • Total differentiability implies continuity.
  • Total differentiability implies partial differentiability in all coordinates.
  • Partial differentiability does not imply continuity and thus not total differentiability.
  • Continuous partial differentiability, i.e. functions with continuous partial derivatives are also continuous total differentiable.
Thanks fresh_42 ... for such a clear explanation ...

Just a point of clarification ... ...

You write:

" ... ... Now ##D_a(f_i)## are all linear iff ##D_a(f)## is. ... ... "Can you explain how we know this is true ...

Peter
 
  • #4
If we have a linear map ##D_a(f)## then this is in coordinates a matrix. And each row ##D_a(f_i)## defines a linear map ##v \mapsto D_a(f_i) \cdot v^\tau = \langle D_a(f_i),v\rangle\,.## And this goes back in the other direction the same way: If we have ##p## linear maps ##D_a(f_i) \, : \, \mathbb{R}^n \longrightarrow \mathbb{R}^1## then they can always be written as ##D_a(f_i)(v)=v_i\cdot v^\tau = \langle v_i,v \rangle## and the ##v_i## give us the rows for ##D_a(f)##. (Here vectors ##w## are rows and ##w^\tau## columns.)
 
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  • #5
Thanks fresh_42 ... appreciate the help ...

Reflecting on what you have written ...

Thanks again ...

Peter
 

Related to Multivariable Differentiation - Component Functions ....

1. What is multivariable differentiation?

Multivariable differentiation is a mathematical concept that involves finding the rates of change of a function with respect to multiple variables. It is used to analyze and optimize functions with multiple input variables, such as in economics, physics, and engineering.

2. How is multivariable differentiation different from single variable differentiation?

In single variable differentiation, the rate of change of a function is found with respect to only one variable. In multivariable differentiation, the rate of change is found with respect to multiple variables, making it a more complex and multidimensional process.

3. What are component functions in multivariable differentiation?

Component functions, also known as partial derivatives, are the individual derivatives of a multivariable function with respect to each input variable. They represent the rate of change of the function in a specific direction.

4. What is the chain rule in multivariable differentiation?

The chain rule in multivariable differentiation is a method used to find the derivative of a composite function, where the input variables depend on more than one variable. It involves taking the partial derivatives of each component function and multiplying them together.

5. How is multivariable differentiation applied in real life?

Multivariable differentiation has various real-life applications, such as in economics for analyzing the relationship between multiple variables, in physics for modeling complex systems, and in engineering for optimizing processes and designs. It is also used in machine learning and data analysis to understand and predict patterns in multidimensional data.

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