Help with D&K Proposition 2.3.2: Directional & Partial Derivatives

In summary: D_n f_2(a) v_n \\ ... & ... & ... & ... & ... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix}
  • #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 another aspect of the proof of Proposition 2.3.2 ... ...

Duistermaat and Kolk's Proposition 2.3.2 and its proof read as follows:
View attachment 7850
https://www.physicsforums.com/attachments/7851
In the above proof we read the following:

" ... ... The partial differentiability of (ii) is a consequence of (i); the formula follows from \(\displaystyle Df(a) \in \text{ Lin} ( \mathbb{R}^n , \mathbb{R}^p )\) and \(\displaystyle v = \sum_{ 1 \le j \le n } v_j e_j \) ( see 1.11) ... ... "Can someone please demonstrate explicitly and rigorously how it is that the partial differentiability of (ii) is a consequence of (i) and, further, how exactly it is that the formula follows from \(\displaystyle Df(a) \in \text{ Lin} ( \mathbb{R}^n , \mathbb{R}^p )\) and \(\displaystyle v = \sum_{ 1 \le j \le n } v_j e_j\) ... ...
Help will be much appreciated ...

Peter==========================================================================================***NOTE***

It may help readers of the above post to have access to the start of Section "2.3: Directional and Partial Derivatives" ... in order to understand the context and notation of the post ... so I am providing the same ... as follows:
View attachment 7852The above post refers to (1.1) so I am providing text relevant to and including (1.1) ... as follows ...View attachment 7853Hope that the above notes/text help readers of the post understand the context and notation of the post ...

Peter
 
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  • #2
The "partial derivatives" in (ii) are the "directional derivatives" in (i) in the direction of the coordinate axes. The formula follows from the fact that a vector can be written as a sum of its component in each coordinate direction times a unit vector in that direction- that the unit vectors in each coordinate direction form a basis for the vector space.
 
  • #3
Country Boy said:
The "partial derivatives" in (ii) are the "directional derivatives" in (i) in the direction of the coordinate axes. The formula follows from the fact that a vector can be written as a sum of its component in each coordinate direction times a unit vector in that direction- that the unit vectors in each coordinate direction form a basis for the vector space.
Thanks Country Boy ...

... appreciate the help ...

Peter
 
  • #4
Peter said:
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 another aspect of the proof of Proposition 2.3.2 ... ...

Duistermaat and Kolk's Proposition 2.3.2 and its proof read as follows:In the above proof we read the following:

" ... ... The partial differentiability of (ii) is a consequence of (i); the formula follows from \(\displaystyle Df(a) \in \text{ Lin} ( \mathbb{R}^n , \mathbb{R}^p )\) and \(\displaystyle v = \sum_{ 1 \le j \le n } v_j e_j \) ( see 1.11) ... ... "Can someone please demonstrate explicitly and rigorously how it is that the partial differentiability of (ii) is a consequence of (i) and, further, how exactly it is that the formula follows from \(\displaystyle Df(a) \in \text{ Lin} ( \mathbb{R}^n , \mathbb{R}^p )\) and \(\displaystyle v = \sum_{ 1 \le j \le n } v_j e_j\) ... ...
Help will be much appreciated ...

Peter==========================================================================================***NOTE***

It may help readers of the above post to have access to the start of Section "2.3: Directional and Partial Derivatives" ... in order to understand the context and notation of the post ... so I am providing the same ... as follows:
The above post refers to (1.1) so I am providing text relevant to and including (1.1) ... as follows ...Hope that the above notes/text help readers of the post understand the context and notation of the post ...

Peter
Here is an attempt to show \(\displaystyle Df(a) v = \sum_{ 1 \le j \le n } v_j D_j f(a) \ \ \ \ (v \in \mathbb{R}^n ) \)
Now we have ...\(\displaystyle Df(a) v = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} v_1 \\ v_2 \\ ... \\ ... \\ ... \\ v_n \end{pmatrix}\)

\(\displaystyle = \begin{pmatrix} D_1 f_1(a) v_1 + D_2 f_1(a) v_2 + \ ... \ ... \ + D_n f_1(a) v_n \\ D_1 f_2(a) v_1 + D_2 f_2(a) v_2 + \ ... \ ... \ + D_n f_2(a) v_n \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix} \)
Now \(\displaystyle D_j f(a) = D_{e_j} f(a) = D f(a) e_j\) ...

But ... taking (as an example) \(\displaystyle j = 1\) ... ... i.e. \(\displaystyle e_j = e_1\) ... we have

\(\displaystyle D_1 f(a) = D_{ e_1} f(a) = D f(a) e_1 = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} 1 \\ 0 \\ 0 \\ ... \\ ... \\ ... \\ 0 \end{pmatrix}
\)
\(\displaystyle = \begin{pmatrix} D_1 f_1 (a) \\ D_1 f_2 (a) \\ D_1 f_3 (a) \\ ... \\ ... \\ ... \\ D_1 f_p (a) \end{pmatrix}
\)
\(\displaystyle = D_{ e_1} f(a) = D_1 f(a) \)
... and similar expressions can be derived for \(\displaystyle D_2 f(a), D_3 f(a) , \ ... \ ...\ , \ D_n f(a)\) ...

From the above, it is clear that \(\displaystyle \sum_{ 1 \le j \le n} v_j D_j f(a) = Df(a) v\)

Can someone please confirm that the above is basically correct ...?
Peter
 
Last edited:
  • #5
Peter said:
Here is an attempt to show \(\displaystyle Df(a) v = \sum_{ 1 \le j \le n } v_j D_j f(a) \ \ \ \ (v \in \mathbb{R}^n ) \)
Now we have ...\(\displaystyle Df(a) v = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} v_1 \\ v_2 \\ ... \\ ... \\ ... \\ v_n \end{pmatrix}\)

\(\displaystyle = \begin{pmatrix} D_1 f_1(a) v_1 + D_2 f_1(a) v_2 + \ ... \ ... \ + D_n f_1(a) v_n \\ D_1 f_2(a) v_1 + D_2 f_2(a) v_2 + \ ... \ ... \ + D_n f_2(a) v_n \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix} \)
Now \(\displaystyle D_j f(a) = D_{e_j} f(a) = D f(a) e_j\) ...

But ... taking (as an example) \(\displaystyle j = 1\) ... ... i.e. \(\displaystyle e_j = e_1\) ... we have

\(\displaystyle D_1 f(a) = D_{ e_1} f(a) = D f(a) e_1 = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} 1 \\ 0 \\ 0 \\ ... \\ ... \\ ... \\ 0 \end{pmatrix}
\)
\(\displaystyle = \begin{pmatrix} D_1 f_1 (a) \\ D_1 f_2 (a) \\ D_1 f_3 (a) \\ ... \\ ... \\ ... \\ D_1 f_p (a) \end{pmatrix}
\)
\(\displaystyle = D_{ e_1} f(a) = D_1 f(a) \)
... and similar expressions can be derived for \(\displaystyle D_2 f(a), D_3 f(a) , \ ... \ ...\ , \ D_n f(a)\) ...

From the above, it is clear that \(\displaystyle \sum_{ 1 \le j \le n} v_j D_j f(a) = Df(a) v\)

Can someone please confirm that the above is basically correct ...?
Peter
l will now attempt to, explicitly, complete the demonstration that \(\displaystyle Df(a) v = \sum_{ 1 \le j \le n } v_j D_j f(a) \ \ \ \ (v \in \mathbb{R}^n ) \)In the previous post we have demonstrated that

\(\displaystyle D_1 f(a) = D_{ e_1} f(a) = D f(a) e_1 = \begin{pmatrix} D_1 f_1 (a) \\ D_1 f_2 (a) \\ D_1 f_3 (a) \\ ... \\ ... \\ ... \\ D_1 f_p (a) \end{pmatrix}\)

... ... and in general

\(\displaystyle D_j f(a) = D_{ e_j} f(a) = D f(a) e_j =\begin{pmatrix} D_j f_1 (a) \\ D_j f_2 (a) \\ D_j f_3 (a) \\ ... \\ ... \\ ... \\ D_j f_p (a) \end{pmatrix}\)
So ...\(\displaystyle v_1 D_1 f(a) = v_1 \begin{pmatrix} D_1 f_1 (a) \\ D_1 f_2 (a) \\ D_1 f_3 (a) \\ ... \\ ... \\ ... \\ D_1 f_p (a) \end{pmatrix} = \begin{pmatrix} v_1 D_1 f_1 (a) \\ v_1 D_1 f_2 (a) \\ v_1 D_1 f_3 (a) \\ ... \\ ... \\ ... \\ v_1 D_1 f_p (a) \end{pmatrix} = \begin{pmatrix} D_1 f_1 (a) v_1 \\ D_1 f_2 (a) v_1 \\ D_1 f_3 (a) v_1 \\ ... \\ ... \\ ... \\ D_1 f_p (a) v_1 \end{pmatrix}
\)
and in general ...\(\displaystyle v_j D_j f(a) = \begin{pmatrix} D_j f_1 (a) v_j \\ D_j f_2 (a) v_j \\ D_j f_3 (a) v_j \\ ... \\ ... \\ ... \\ D_1 f_p (a) v_j \end{pmatrix}\)
So ... ...\(\displaystyle \sum_{ 1 \le j \le n } v_j D_j f(a)\) \(\displaystyle = \begin{pmatrix} D_1 f_1 (a) v_1 \\ D_1 f_2 (a) v_1 \\ D_1 f_3 (a) v_1 \\ ... \\ ... \\ ... \\ D_1 f_p (a) v_1 \end{pmatrix} + \begin{pmatrix} D_2 f_1 (a) v_2 \\ D_2 f_2 (a) v_2 \\ D_2 f_3 (a) v_2 \\ ... \\ ... \\ ... \\ D_2 f_p (a) v_2 \end{pmatrix} + \ ... \ ... \ \begin{pmatrix} D_j f_1 (a) v_j \\ D_j f_2 (a) v_j \\ D_j f_3 (a) v_j \\ ... \\ ... \\ ... \\ D_j f_p (a) v_j \end{pmatrix} + \ ... \ ... \ + \begin{pmatrix} D_n f_1 (a) v_n \\ D_n f_2 (a) v_n \\ D_n f_3 (a) v_n \\ ... \\ ... \\ ... \\ D_n f_p (a) v_n \end{pmatrix}
\)

= \(\displaystyle = \begin{pmatrix} D_1 f_1(a) v_1 + D_2 f_1(a) v_2 + \ ... \ ... \ + D_n f_1(a) v_n \\ D_1 f_2(a) v_1 + D_2 f_2(a) v_2 + \ ... \ ... \ + D_n f_2(a) v_n \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix} \)\(\displaystyle = Df(a) v\)
Can someone please either confirm the above demonstration is correct or point out the errors and shortcomings ...

Peter
 
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Related to Help with D&K Proposition 2.3.2: Directional & Partial Derivatives

What is D&K Proposition 2.3.2?

D&K Proposition 2.3.2 is a mathematical concept that describes the relationship between directional and partial derivatives in multivariate calculus.

Why is D&K Proposition 2.3.2 important?

This proposition is important because it allows us to calculate the directional derivative of a function in any direction, as well as the partial derivatives with respect to each variable.

How do directional and partial derivatives differ?

Directional derivatives measure the rate of change of a function in a specific direction, while partial derivatives measure the rate of change in a specific variable while holding all other variables constant.

What are some real-world applications of D&K Proposition 2.3.2?

D&K Proposition 2.3.2 is used in various fields such as physics, economics, engineering, and finance to model and analyze the behavior of complex systems.

Are there any limitations or assumptions to D&K Proposition 2.3.2?

Yes, D&K Proposition 2.3.2 assumes that the function is continuous and differentiable. It also assumes that the direction of differentiation is constant throughout the domain of the function.

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