# Directional Derivatives ....Notation .... D&K ....

• MHB
• Math Amateur
In summary, the derivative of a function at a point is a linear operator that approximates the function near that point. This operator has the same dimension as the domain and range copies of $\mathbb{R}$ as the original function. In single-variable calculus, the derivative is represented by a $1\times 1$ matrix, but can be thought of as a linear operator in matrix notation.
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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 D&K's notation for directional derivatives ... ...

D&K's definition of directional and partial derivatives reads as follows:
View attachment 7856
I am assuming that under D&K's definitions and notation one can write:$$\displaystyle D_v f(a) = \begin{pmatrix} D_v f_1 (a) \\ D_v f_2 (a) \\ D_v f_3 (a) \\ ... \\ ... \\ ... \\ D_v f_p (a) \end{pmatrix}$$$$\displaystyle = Df(a)v$$$$\displaystyle = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} v_1 \\ v_2 \\ ... \\ ... \\ ... \\ v_n \end{pmatrix}$$

$$\displaystyle = \begin{pmatrix} D_1 f_1(a) v_1 + D_2 f_1(a) v_2 + \ ... \ ... \ + D_n f_1(a) v_n \\ D_1 f_2(a) v_1 + D_2 f_2(a) v_2 + \ ... \ ... \ + D_n f_2(a) v_n \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix}$$Is the above a correct use of notation according to D&K's schema of notation ...

Peter
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Proposition 2.3.2 may well be relevant to the above post ... so I am providing the same ... as follows:View attachment 7857
View attachment 7858

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 an aspect of D&K's notation for directional derivatives ... ...

D&K's definition of directional and partial derivatives reads as follows:

I am assuming that under D&K's definitions and notation one can write:$$\displaystyle D_v f(a) = \begin{pmatrix} D_v f_1 (a) \\ D_v f_2 (a) \\ D_v f_3 (a) \\ ... \\ ... \\ ... \\ D_v f_p (a) \end{pmatrix}$$$$\displaystyle = Df(a)v$$$$\displaystyle = \begin{pmatrix} D_1 f_1(a) & D_2 f_1(a) & ... & ... & D_n f_1(a) \\ D_1 f_2(a) & D_2 f_2(a) & ... & ... & D_n f_2(a) \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ ... & ... & ... & ... &... \\ D_1 f_p(a) & D_2 f_p(a) & ... & ... & D_n f_p(a) \end{pmatrix} \begin{pmatrix} v_1 \\ v_2 \\ ... \\ ... \\ ... \\ v_n \end{pmatrix}$$

$$\displaystyle = \begin{pmatrix} D_1 f_1(a) v_1 + D_2 f_1(a) v_2 + \ ... \ ... \ + D_n f_1(a) v_n \\ D_1 f_2(a) v_1 + D_2 f_2(a) v_2 + \ ... \ ... \ + D_n f_2(a) v_n \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ ... \ ... \ ... \ ... \ ... \\ D_1 f_p(a) v_1 + D_2 f_p(a) v_2 + \ ... \ ... \ + D_n f_p(a) v_n \end{pmatrix}$$Is the above a correct use of notation according to D&K's schema of notation ...

Peter
=========================================================================================

Proposition 2.3.2 may well be relevant to the above post ... so I am providing the same ... as follows:

I just thought I would share with MHB members Duistermaat and Kolk "Remark on notation". This remark occurs after the definition of directional and partial derivatives and reads as follows:https://www.physicsforums.com/attachments/7859
View attachment 7860
I have to say in passing that learning about ... or further ... getting a good understanding of ... the differentiation of functions/mappings from $$\displaystyle \mathbb{R}^n$$ to $$\displaystyle \mathbb{R}^p$$ ... is harder than I thought it would be ... ... :( ... ...Peter

Hi, Peter.

Peter said:
I have to say in passing that learning about ... or further ... getting a good understanding of ... the differentiation of functions/mappings from $$\displaystyle \mathbb{R}^n$$ to $$\displaystyle \mathbb{R}^p$$ ... is harder than I thought it would be ... ... :( ... ...

Though it may take some time to see, the case of mappings from $\mathbb{R}^{n}$ to $\mathbb{R}^{p}$ is the natural generalization of mappings from $\mathbb{R}^{n}$ to $\mathbb{R}$ (i.e., real-valued functions of several variables), which is itself the generalization of mappings from $\mathbb{R}\rightarrow\mathbb{R}$. I will do my best to outline this flow below.

The Derivative as a Linear Operator
The derivative a function (whether it be $f:\mathbb{R}\rightarrow\mathbb{R}$, $f:\mathbb{R}^{n}\rightarrow\mathbb{R},$ or $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$) at a point $a$ is the linear mapping that most closely resembles $f$ at $a$ (Note: This is why the definition of the derivative (as you've posted previously) involves the term
$$\|f(x)-f(a)-Df(a)(x-a)\|,$$
because this term is the error the linear mapping has with respect to the true function values near $a$, and we want this error to be zero in the limit that $x\rightarrow a$).

Whatever the case may be ($f:\mathbb{R}\rightarrow\mathbb{R}$, $f:\mathbb{R}^{n}\rightarrow\mathbb{R},$ or $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$), since the derivative linear operator must approximate $f$ it must be a mapping whose domain and range copies of $\mathbb{R}$ are of the same dimension as the domain and range copies of $\mathbb{R}$ for $f$. Hence

\begin{align*} f:\mathbb{R}\rightarrow\mathbb{R}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}\rightarrow\mathbb{R}\\ f:\mathbb{R}^{n}\rightarrow\mathbb{R}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}^{n}\rightarrow\mathbb{R}\\ f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p} \end{align*}

Computing Derivatives: Real-Valued Function of a Single Variable
Thinking in terms of matrices and linear operators in single-variable calculus is not typically emphasized because the "matrices" representing the derivative linear operator are all $1\times 1$ (i.e., numbers), see correspondence table above.

However, let's emphasize the matrix notation via an example. Take $f:\mathbb{R}\rightarrow\mathbb{R}$ to be $f(x)=x^{2}.$ The domain and range spaces are collections of $1\times 1$ column vectors. The derivative of $f$ at a point $a\in\mathbb{R}$ will be a linear mapping from $\mathbb{R}\rightarrow\mathbb{R}$, and so can be expressed expressed as a $1\times 1$ matrix:
$$Df(a)=[2a].$$
This matrix acts on a $1\times 1$ column vector $v\in\mathbb{R}$ by matrix multiplication to produce a column vector in the range copy of $\mathbb{R}$:
$$Df(a)v=[2a][v_{1}]=[2av_{1}].$$

Computing Derivatives: Real-Valued Function of Several Variables
Now we are thinking of functions $f:\mathbb{R}^{n}\rightarrow\mathbb{R}$. From the table above, we know that the derivative will be a linear mapping from $\mathbb{R}^{n}$ to $\mathbb{R}$, which means that it can be represented by a $1\times n$ matrix.

For example, take $f(x):\mathbb{R}^{3}\rightarrow\mathbb{R}$ to be defined as $f(x)=f(x_{1},x_{2},x_{3})=x_{1}^{2}+2x_{2}x_{3}+x_{3}^{2}$ and note $v\in\mathbb{R}^{3}$ is the column vector
$$v= \begin{bmatrix} v_{1}\\ v_{2}\\ v_{3} \end{bmatrix}.$$

This next step is critical: The components of the $1\times 3$ derivative matrix are obtained by doing single-variable calculus. In other words, the first component is obtained by doing single-variable calculus on $f$ with respect to $x_{1}$ (i.e., differentiating with respect to $x_{1}$ only and thinking of $x_{2}$ and $x_{3}$ as constants), the second component is obtained by doing single-variable calculus on $f$ with respect to $x_{2}$ (i.e., differentiating with respect to $x_{2}$ only and thinking of $x_{1}$ and $x_{3}$ as constants), and the third component is obtained by doing single-variable calculus on $f$ with respect to $x_{3}$ (i.e., differentiating with respect to $x_{3}$ only and thinking of $x_{1}$ and $x_{2}$ as constants). Hence,
$$Df(x) = \begin{bmatrix} 2x_{1}& 2x_{3}& 2x_{2}+2x_{3} \end{bmatrix},$$
or, using $x\mapsto a$,
$$Df(a) = \begin{bmatrix} 2a_{1}& 2a_{3}& 2a_{2}+2a_{3} \end{bmatrix}.$$
Now, this derivative operator acts on $v$ through matrix multiplication to produce a $1\times 1$ column vector in $\mathbb{R}$:
$$Df(a)v= \begin{bmatrix} 2a_{1}& 2a_{3}& 2a_{2}+2a_{3} \end{bmatrix} \begin{bmatrix} v_{1}\\ v_{2}\\ v_{3} \end{bmatrix} =[2a_{1}v_{1}+2a_{3}v_{2}+(2a_{2}+2a_{3})v_{3}].$$

Again, the key here is that each column of the above $1\times n$ derivative matrix is obtained by doing single-variable calculus.

Computing Derivatives: Vector-Valued Functions of Several Variables
Now we are considering $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}.$ From our table above, we know that we must eventually construct a linear mapping from $\mathbb{R}^{n}$ to $\mathbb{R}^{p}$; i.e., a matrix of dimension $p\times n$.

This case is actually no different from the one before. All that is happening is that we now have $p$ different functions of $n$ variables that we collect in a column vector of height $p$:
$$f(x)=f(x_{1},x_{2},\ldots, x_{n})= \begin{bmatrix} f_{1}(x)\\ f_{2}(x)\\ \vdots\\ f_{p}(x) \end{bmatrix} = \begin{bmatrix} f_{1}(x_{1},x_{2},\ldots, x_{n})\\ f_{2}(x_{1},x_{2},\ldots, x_{n})\\ \vdots\\ f_{p}(x_{1},x_{2},\ldots, x_{n}) \end{bmatrix}.$$
To simplify things in your mind, really try to see that all that we have here is $p$ different versions of the previous case (i.e., real-valued functions of several variables). For all intents and purposes, each function $f_{i}(x)$ in the above column vector has nothing to do with any of the other functions in the column vector.

To differentiate such an object, we go one entry at a time in the column vector, using the method of the previous section to differentiate each real-valued function of several variables. According to the previous section, the derivative of a real-valued function of several variables is a row vector obtained by doing single-variable calculus with respect to each of the variables. Symbolically,
$$Df_{i}(a)= \begin{bmatrix} \partial_{x_{1}}f_{i}(a) & \partial_{x_{2}}f_{i}(a) & \cdots & \partial_{x_{n}}f_{i}(a) \end{bmatrix}.$$
All that we do to form the $p\times n$ derivative matrix $Df(a)$ is assemble each of these individually obtained row vectors into a single matrix:
$$Df(a)= \begin{bmatrix} \partial_{x_{1}}f_{1}(a) & \partial_{x_{2}}f_{1}(a) & \cdots & \partial_{x_{n}}f_{1}(a)\\ \partial_{x_{1}}f_{2}(a) & \partial_{x_{2}}f_{2}(a) & \cdots & \partial_{x_{n}}f_{2}(a)\\ \vdots & \vdots & \ddots & \vdots\\ \partial_{x_{1}}f_{p}(a) & \partial_{x_{2}}f_{p}(a) & \cdots & \partial_{x_{n}}f_{p}(a) \end{bmatrix}.$$
Here are two (hopefully) helpful ways you can use for intuitively carrying out the process of differentiating a function $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$:

1) You can think of "pushing" the derivative symbol $D$ onto the components of $f$:
$$D_{p\times n}f(a)= \begin{bmatrix} D_{1\times n}f_{1}(a)\\ D_{1\times n}f_{2}(a)\\ \vdots\\ D_{1\times n}f_{p}(a) \end{bmatrix},$$
where $D_{p\times n}$ is the derivative we want to compute and $D_{1\times n}$ is the derivative we know how to compute from the case of a real-valued function of several variables from the second "Computing Derivatives" section above.

2) Since you know you will need to compute a $p\times n$ matrix, across the top of your matrix title the $n$-columns by $x_{1}, x_{2},\ldots, x_{n}$ (or possibly $\partial _{x_{1}}$, $\partial_{x_{2}}$, $\ldots,$ $\partial_{x_{n}}$), and the $p$ rows by $f_{1}$, $f_{2}$, $\ldots,$ $f_{p}$. By selecting a particular row you reduce the problem to considering a real-valued function of several variables. By selecting a column in this row, you reduce the problem to a real-valued function of a single variable. For example, if you select row $2$ and column $3$, you are now only dealing with the real-valued function $f_{2}(x_{1},x_{2},x_{3},\ldots, x_{n})$ as a function of the single-variable $x_{3}$ ($x_{1}, x_{2}, x_{4}, \ldots, x_{n}$ are thought of as constants). To obtain the $p\times n$ derivative matrix, perform the partial derivatives of the functions obtained by examining what row and column you are in.

Last edited:
GJA said:
Hi, Peter.
Though it may take some time to see, the case of mappings from $\mathbb{R}^{n}$ to $\mathbb{R}^{p}$ is the natural generalization of mappings from $\mathbb{R}^{n}$ to $\mathbb{R}$ (i.e., real-valued functions of several variables), which is itself the generalization of mappings from $\mathbb{R}\rightarrow\mathbb{R}$. I will do my best to outline this flow below.

The Derivative as a Linear Operator
The derivative a function (whether it be $f:\mathbb{R}\rightarrow\mathbb{R}$, $f:\mathbb{R}^{n}\rightarrow\mathbb{R},$ or $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$) at a point $a$ is the linear mapping that most closely resembles $f$ at $a$ (Note: This is why the definition of the derivative (as you've posted previously) involves the term
$$\|f(x)-f(a)-Df(a)(x-a)\|,$$
because this term is the error the linear mapping has with respect to the true function values near $a$, and we want this error to be zero in the limit that $x\rightarrow a$).

Whatever the case may be ($f:\mathbb{R}\rightarrow\mathbb{R}$, $f:\mathbb{R}^{n}\rightarrow\mathbb{R},$ or $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$), since the derivative linear operator must approximate $f$ it must be a mapping whose domain and range copies of $\mathbb{R}$ are of the same dimension as the domain and range copies of $\mathbb{R}$ for $f$. Hence

\begin{align*} f:\mathbb{R}\rightarrow\mathbb{R}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}\rightarrow\mathbb{R}\\ f:\mathbb{R}^{n}\rightarrow\mathbb{R}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}^{n}\rightarrow\mathbb{R}\\ f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}\qquad &\Longleftrightarrow\qquad Df:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p} \end{align*}

Computing Derivatives: Real-Valued Function of a Single Variable
Thinking in terms of matrices and linear operators in single-variable calculus is not typically emphasized because the "matrices" representing the derivative linear operator are all $1\times 1$ (i.e., numbers), see correspondence table above.

However, let's emphasize the matrix notation via an example. Take $f:\mathbb{R}\rightarrow\mathbb{R}$ to be $f(x)=x^{2}.$ The domain and range spaces are collections of $1\times 1$ column vectors. The derivative of $f$ at a point $a\in\mathbb{R}$ will be a linear mapping from $\mathbb{R}\rightarrow\mathbb{R}$, and so can be expressed expressed as a $1\times 1$ matrix:
$$Df(a)=[2a].$$
This matrix acts on a $1\times 1$ column vector $v\in\mathbb{R}$ by matrix multiplication to produce a column vector in the range copy of $\mathbb{R}$:
$$Df(a)v=[2a][v_{1}]=[2av_{1}].$$

Computing Derivatives: Real-Valued Function of Several Variables
Now we are thinking of functions $f:\mathbb{R}^{n}\rightarrow\mathbb{R}$. From the table above, we know that the derivative will be a linear mapping from $\mathbb{R}^{n}$ to $\mathbb{R}$, which means that it can be represented by a $1\times n$ matrix.

For example, take $f(x):\mathbb{R}^{3}\rightarrow\mathbb{R}$ to be defined as $f(x)=f(x_{1},x_{2},x_{3})=x_{1}^{2}+2x_{2}x_{3}+x_{3}^{2}$ and note $v\in\mathbb{R}^{3}$ is the column vector
$$v= \begin{bmatrix} v_{1}\\ v_{2}\\ v_{3} \end{bmatrix}.$$

This next step is critical: The components of the $1\times 3$ derivative matrix are obtained by doing single-variable calculus. In other words, the first component is obtained by doing single-variable calculus on $f$ with respect to $x_{1}$ (i.e., differentiating with respect to $x_{1}$ only and thinking of $x_{2}$ and $x_{3}$ as constants), the second component is obtained by doing single-variable calculus on $f$ with respect to $x_{2}$ (i.e., differentiating with respect to $x_{2}$ only and thinking of $x_{1}$ and $x_{3}$ as constants), and the third component is obtained by doing single-variable calculus on $f$ with respect to $x_{3}$ (i.e., differentiating with respect to $x_{3}$ only and thinking of $x_{1}$ and $x_{2}$ as constants). Hence,
$$Df(x) = \begin{bmatrix} 2x_{1}& 2x_{3}& 2x_{2}+2x_{3} \end{bmatrix},$$
or, using $x\mapsto a$,
$$Df(a) = \begin{bmatrix} 2a_{1}& 2a_{3}& 2a_{2}+2a_{3} \end{bmatrix}.$$
Now, this derivative operator acts on $v$ through matrix multiplication to produce a $1\times 1$ column vector in $\mathbb{R}$:
$$Df(a)v= \begin{bmatrix} 2a_{1}& 2a_{3}& 2a_{2}+2a_{3} \end{bmatrix} \begin{bmatrix} v_{1}\\ v_{2}\\ v_{3} \end{bmatrix} =[2a_{1}v_{1}+2a_{3}v_{2}+(2a_{2}+2a_{3})v_{3}].$$

Again, the key here is that each column of the above $1\times n$ derivative matrix is obtained by doing single-variable calculus.

Computing Derivatives: Vector-Valued Functions of Several Variables
Now we are considering $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}.$ From our table above, we know that we must eventually construct a linear mapping from $\mathbb{R}^{n}$ to $\mathbb{R}^{p}$; i.e., a matrix of dimension $p\times n$.

This case is actually no different from the one before. All that is happening is that we now have $p$ different functions of $n$ variables that we collect in a column vector of height $p$:
$$f(x)=f(x_{1},x_{2},\ldots, x_{n})= \begin{bmatrix} f_{1}(x)\\ f_{2}(x)\\ \vdots\\ f_{p}(x) \end{bmatrix} = \begin{bmatrix} f_{1}(x_{1},x_{2},\ldots, x_{n})\\ f_{2}(x_{1},x_{2},\ldots, x_{n})\\ \vdots\\ f_{p}(x_{1},x_{2},\ldots, x_{n}) \end{bmatrix}.$$
To simplify things in your mind, really try to see that all that we have here is $p$ different versions of the previous case (i.e., real-valued functions of several variables). For all intents and purposes, each function $f_{i}(x)$ in the above column vector has nothing to do with any of the other functions in the column vector.

To differentiate such an object, we go one entry at a time in the column vector, using the method of the previous section to differentiate each real-valued function of several variables. According to the previous section, the derivative of a real-valued function of several variables is a row vector obtained by doing single-variable calculus with respect to each of the variables. Symbolically,
$$Df_{i}(a)= \begin{bmatrix} \partial_{x_{1}}f_{i}(a) & \partial_{x_{2}}f_{i}(a) & \cdots & \partial_{x_{n}}f_{i}(a) \end{bmatrix}.$$
All that we do to form the $p\times n$ derivative matrix $Df(a)$ is assemble each of these individually obtained row vectors into a single matrix:
$$Df(a)= \begin{bmatrix} \partial_{x_{1}}f_{1}(a) & \partial_{x_{2}}f_{1}(a) & \cdots & \partial_{x_{n}}f_{1}(a)\\ \partial_{x_{1}}f_{2}(a) & \partial_{x_{2}}f_{2}(a) & \cdots & \partial_{x_{n}}f_{2}(a)\\ \vdots & \vdots & \ddots & \vdots\\ \partial_{x_{1}}f_{p}(a) & \partial_{x_{2}}f_{p}(a) & \cdots & \partial_{x_{n}}f_{p}(a) \end{bmatrix}.$$
Here are two (hopefully) helpful ways you can use for intuitively carrying out the process of differentiating a function $f:\mathbb{R}^{n}\rightarrow\mathbb{R}^{p}$:

1) You can think of "pushing" the derivative symbol $D$ onto the components of $f$:
$$D_{p\times n}f(a)= \begin{bmatrix} D_{1\times n}f_{1}(a)\\ D_{1\times n}f_{2}(a)\\ \vdots\\ D_{1\times n}f_{p}(a) \end{bmatrix},$$
where $D_{p\times n}$ is the derivative we want to compute and $D_{1\times n}$ is the derivative we know how to compute from the case of a real-valued function of several variables from the second "Computing Derivatives" section above.

2) Since you know you will need to compute a $p\times n$ matrix, across the top of your matrix title the $n$-columns by $x_{1}, x_{2},\ldots, x_{n}$ (or possibly $\partial _{x_{1}}$, $\partial_{x_{2}}$, $\ldots,$ $\partial_{x_{n}}$), and the $p$ rows by $f_{1}$, $f_{2}$, $\ldots,$ $f_{p}$. By selecting a particular row you reduce the problem to considering a real-valued function of several variables. By selecting a column in this row, you reduce the problem to a real-valued function of a single variable. For example, if you select row $2$ and column $3$, you are now only dealing with the real-valued function $f_{2}(x_{1},x_{2},x_{3},\ldots, x_{n})$ as a function of the single-variable $x_{3}$ ($x_{1}, x_{2}, x_{4}, \ldots, x_{n}$ are thought of as constants). To obtain the $p\times n$ derivative matrix, perform the partial derivatives of the functions obtained by examining what row and column you are in.
Thanks GJA ... your post is extremely helpful and clarifying ...Just a simple point of clarification ...You write:

" ... ... Take $f:\mathbb{R}\rightarrow\mathbb{R}$ to be $f(x)=x^{2}.$ The domain and range spaces are collections of $1\times 1$ column vectors. The derivative of $f$ at a point $a\in\mathbb{R}$ will be a linear mapping from $\mathbb{R}\rightarrow\mathbb{R}$, and so can be expressed expressed as a $1\times 1$ matrix:
$$Df(a)=[2a].$$
This matrix acts on a $1\times 1$ column vector $v\in\mathbb{R}$ by matrix multiplication to produce a column vector in the range copy of $\mathbb{R}$:
$$Df(a)v=[2a][v_{1}]=[2av_{1}].$$ ... ... "
so $$\displaystyle D(f(a) = [2a]$$ may be thought of as the derivative and further as the rate of change of $$\displaystyle f$$ at $$\displaystyle a$$ ...

... but how do we interpret $$\displaystyle Df(a)v = [2a][v] = [2av]$$ ... ...

... and further ... what is the interpretation of $$\displaystyle Df(a)v$$ in the other higher dimensional cases ...Hope you can help ...

Peter

## What are directional derivatives?

Directional derivatives are a type of derivative in multivariable calculus that measure how a function changes in a particular direction.

## What is the notation used for directional derivatives?

The notation used for directional derivatives is Duf(x,y), where u is the unit vector representing the direction and f(x,y) is the function being evaluated.

## What is the difference between D&K and directional derivatives?

D&K, or the Dini derivative, is a generalization of directional derivatives for functions that are not necessarily differentiable. It takes into account the behavior of a function at a point in all directions, while directional derivatives only consider a specific direction.

## What is the purpose of directional derivatives in real-world applications?

Directional derivatives are useful in real-world applications, such as physics and engineering, for determining the rate of change of a function in a specific direction. This can be used to optimize processes, such as finding the steepest descent in a terrain for a hiking path.

## How do you calculate directional derivatives?

The formula for calculating the directional derivative is Duf(x,y) = ∇f(x,y) · u, where ∇f(x,y) is the gradient of the function and u is the unit vector representing the direction. This can also be written as Duf(x,y) = fx(x,y)cosθ + fy(x,y)sinθ, where fx(x,y) and fy(x,y) are the partial derivatives of the function and θ is the angle between the direction and the positive x-axis.

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