Multivariable Analysis .... Directional & Partial Derivatives

In summary: This is why sometimes we see ##D_v f(a) = Df(a) v## and sometimes ##D_v f(a) = D f(a) \cdot v##. It just depends on the perspective we are using.
  • #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 an aspect of the proof of Proposition 2.3.2 ... ...

Duistermaat and Kolk's Proposition 2.3.2 and its proof read as follows:
D&K - 1 - Proposition 2.3.2 ...  .... PART 1 ... .png

D&K - 2 - Proposition 2.3.2 ...  .... PART 2 ... .png

In the above proof by D&K we read the following:

" ... ... Assertion (i) follows from Formula (2.11). ... ..."Can someone please demonstrate (formally and rigorously) that this is the case ... that is that assertion (i) follows from Formula (2.11). ... ...Help will be 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:
D&K - Start of Section 2.3 on Directional and Partial Derivatives  ... .png

Hope that the above helps readers of the post understand the context and notation of the post ...

Peter
 

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  • D&K - 2 - Proposition 2.3.2 ...  .... PART 2 ... .png
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  • D&K - Start of Section 2.3 on Directional and Partial Derivatives  ... .png
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  • #2
They are already the same, so I don't know what to show here. The assertion in 2.3.2 is:
  • ##f## has a derivative in ##a##
  • in any direction ##v##
  • ##D_v(f)(a) = \left. \dfrac{d}{dv}\right|_{x=a}f(x) ## is linear
and from equation 2.11 w have:
  • ##Df(a)v = Df(a).v = Df(a)(v) = D_vf(a) = D_{a;v}f = (D_af)(v)## is the derivative in ##x=a## simply written in various different ways, depending on what is emphasized: point of evaluation, direction of change, linearity of ##D##, function in ##x##, etc. The introduction to this article: https://www.physicsforums.com/insights/the-pantheon-of-derivatives-i/ maybe helps a bit, and in this one (§1) I made the fun and gathered a couple of different views: https://www.physicsforums.com/insights/journey-manifold-su2mathbbc-part/ and it isn't even all of them.
  • Since we have no restriction in equation 2.11 on ##v##, it could be any direction, so all of them are valid.
  • Derivatives are linear functions (in the argument ##v##), the rest is only a different way of notation, see previous links.
 
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  • #3
Thanks fresh_42 ...

Just now reflecting on your post ...

But I have say that your post is already very helpful ...

I was wondering why some texts give ##D_v f(a) = Df(a) v## and others give ##D_v f(a) = D f(a) \cdot v## ... but as you point out this is just two ways to express the same thing and ##D_v f(a) = Df(a) v = D f(a) \cdot v ## ... ... (hope that's right ..) ...

Notation seems part of the difficulties in understanding differentiation of multivariable vector-valued functions ...

Peter
 
  • #4
Unfortunately there is no common ground on notation here. It is always a differentiation ##D## in direction ##v## of a function ##f## evaluated at a point ##a##. In the end it is a tangent at a curve at some point. That's why I wrote
$$
\left. \dfrac{d}{dv}\right|_{x=a}f(x)
$$
but this doesn't fit very well in text lines. We have an operator ##D## on a function ##f## at a point ##a## directing towards ##v##. No wonder that different people arrange this differently. Except ##D## which stands for the differentiation process, all others can be variable. And even ##D## can be variable, as it is sometimes a certain derivation among many. A derivation is a linear map for which the Leibniz rule, resp. the Jacobi identity hold, which is the same, that is ##D(f\cdot g) = D(f)\cdot g +f\cdot D(g)##.

The last statement was about the operator ##D## acting on functions: ##f \mapsto Df##.
As differentiation, we have to evaluate it at certain point: ##a \mapsto D_a(f)##.
With more than one direction as in school, we also must say in which direction, which gives us a linear map ##v \mapsto D_a(f)(v)##

The first one is the most abstract and has to do with all the elaborated stuff: Lie algebras, vector bundles and similar.

The second is what is meant if people say, e.g. continuous differentiable. Continuity relates to the dependency of the location ##a##. This dependency is usually neglected as people write e.g. ##f\,'(x)= x^2## and don't distinguish between the function ##f\,'## and the slope ##{f\,'}|_{x=a}\,.##

The third one comes into play, if we don't have ##\mathbb{R}^1## as domain anymore, but ##\mathbb{R}^n## which obviously is more than one possible direction.

Another point we have to deal with is, that the function ##f = (f_1,\ldots, f_p)## itself has components. But this has nothing to do with the other aspects about location, direction and differentiated function considered as variable for ##D##. So all these information has to be grouped around ##D##.
 
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Related to Multivariable Analysis .... Directional & Partial Derivatives

1. What is multivariable analysis?

Multivariable analysis is a statistical method that involves analyzing data with more than one independent variable. It allows scientists to understand and quantify the relationship between multiple variables and a dependent variable.

2. What are directional derivatives?

Directional derivatives are a type of partial derivative that measures the rate of change of a function in a specific direction. It is often used in multivariable analysis to determine how a function changes along a given vector.

3. What are partial derivatives?

Partial derivatives are a type of derivative that measures the rate of change of a function with respect to one of its independent variables, while holding all other variables constant. In multivariable analysis, partial derivatives are used to understand the relationship between a function and its independent variables.

4. What is the difference between directional and partial derivatives?

The main difference between directional and partial derivatives is the direction in which they measure the rate of change. Directional derivatives measure the rate of change in a specific direction, while partial derivatives measure the rate of change with respect to one specific variable, holding all other variables constant.

5. How is multivariable analysis used in real-world applications?

Multivariable analysis is used in a wide range of fields, including economics, psychology, and biology, to name a few. It is used to analyze complex systems and relationships between multiple variables, allowing scientists to make predictions and better understand real-world phenomena.

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