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## Multivariable calculus

### Course: Multivariable calculus > Unit 2

Lesson 2: Gradient and directional derivatives- Gradient
- Finding gradients
- Gradient and graphs
- Visual gradient
- Gradient and contour maps
- Directional derivative
- Directional derivative, formal definition
- Finding directional derivatives
- Directional derivatives and slope
- Why the gradient is the direction of steepest ascent

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# Gradient

The gradient captures all the partial derivative information of a scalar-valued multivariable function. Created by Grant Sanderson.

## Want to join the conversation?

- What is a partial derivative operator?Especially what is operator?(11 votes)
**Operation**is just a function. Just another name for it.**Operator**is a symbol which correspondences to a function.

Example:

Addition is a*operation*. In other words, it is a**function**. It's domain is (R x R) (where R is a set of real numbers), and its' codomain is R. (you take two real numbers and obtain a result, one real number)

You can write it like this: +(5,3)=8. It's a familiar function notation, like f(x,y), but we have a**symbol**+ instead of f. But there is other, slightly more popular way: 5+3=8. When there aren't any parenthesis around, one tends to call this + an*operator*. But it's all just words.

Partial derivative operator, nabla, upside-down triangle, is a symbol for taking the gradient, which was explained in the video.

Sidenote: (Sometimes the word "operator" is interchangeable with "operation", but you see this all the time. Words like "cook" (the person) and "(to) cook" are almost the same, because we tend to think of things that do the actions as the*actions themselves*)(63 votes)

- This dude is 3Blue1Brown isn't he?(26 votes)
- Yes! He is. His major was math at Stanford!(16 votes)

- What was the other name for gradient? At1:48ish(5 votes)
- It is nabla or del. "del f" is how you would pronounce the gradient of f. (or grad f).

nabla is an upside-down Greek delta.(12 votes)

- He expressed the gradient like we do in matrix using the square brackets thingy. So gradient is a vector & a matrix?(2 votes)
- The gradient is only a vector. A vector in general is a matrix in the ℝˆn x 1th dimension (It has only one column, but n rows).(8 votes)

- For nabla, is the order of the components in the vector dependent on the order of the variables in the function call? For example, would:
`del f(y, x)`

be equal to:

?`<df/dy, df/dx>`

Another example of my question would be:`del f(x1, x2, x3, x4)`

translates to:

.`<df/dx1, df/dx2, df/dx3, df/dx4>`

(3 votes)- Good question! At first it kind of seems an obvious thing to state, but we can't make assumptions in math now can we.

To answer your question, in my experience we always calculate the gradient in order of the operands, like you described.

Happy learning!

- Convenient Colleague(4 votes)

- At1:05, when we take the derivative of f in respect to x, therefore take y = sin(y) as a constant, why doesn't it disappear in the derivative?(2 votes)
- A constant disappears in the derivative when it is added.

f(x) = c * g(x)

=> f'(x) = c *g'(x)

In this case g(x) = x² and c = sin(y)

But if we had :

f(x) = g(x) +c

Then we would have: f'(x) = g'(x)

When the constant is added, it disappears :)(6 votes)

- The function f (x,y) =x^2 * sin (y) is a three dimensional function with two inputs and one output and the gradient of f is a two dimensional vector valued function. So isn't he incorrect when he says that the dimensions of the gradient are the same as the dimensions of the function. I think it is always one less.(2 votes)
- The dimension of the gradient is
**always**the same as the dimension of the input space.

This is due to the way we construct the gradient : we add a component for each variables.

In this case we have a two-dimensional input space, and therefore a two-dimensional gradient :)(4 votes)

- Is it possible to include the subtitles in the videos like the way Youtube does?(2 votes)
- you can do it now. Sorry if the comment's useless after two years(2 votes)

- I love this new series of videos.

Hoping you'll put some Tutorial notes with it.

How would you describe the way the Total Derivative works versus the Full (del operator) Derivative ?(2 votes) - In the computer display, there is something called gradient. Does it have anything to do with the gradient being discussed here in Math?(0 votes)
- The gradient that you are referring to—a gradual change in color from one part of the screen to another—could be modeled by a mathematical gradient.

Since the gradient gives us the steepest rate of increase at a given point, imagine if you:

1) Had a function that plotted a downward-facing paraboloid (like x^2+y^2+z = 0. Take a look at the graph on Wolfram Alpha)

2) Looked at that plot from the top down

3) Programmed your computer so the steeper the slope of the vector at a given point, the darker the color it placed at that point.

That would effectively draw a circular color gradient, where the part of the circle near (x,y) = (0,0) would be lighter and would grow darker as you moved further out in the x and y directions.(5 votes)

## Video transcript

- [Voiceover] So here I'm
gonna talk about the gradient. And in this video, I'm only gonna describe how you compute the gradient, and in the next couple ones I'm gonna give the
geometric interpretation. And I hate doing this, I hate showing the computation before the geometric intuition since usually it should
go the other way around, but the gradient is one
of those weird things where the way that you compute it actually seems kind of
unrelated to the intuition and you'll see that. We'll connect them in the next few videos. But to do that, we need to know what both of them actually are. So on the computation side of things, let's say you have some sort of function. And I'm just gonna make it
a two-variable function. And let's say it's f of x, y,
equals x-squared sine of y. The gradient is a way of packing together all the partial derivative
information of a function. So let's just start by computing
the partial derivatives of this guy. So partial of f with respect to x is equal to, so we look at this and we
consider x the variable and y the constant. Well in that case sine
of y is also a constant. As far as x is concerned, the derivative of x is 2x so we see that this will be 2x times that constant sine of y, sine of y. Whereas the partial derivative with respect to y. Now we look up here and we say x is considered a constant so x-squared is also considered a constant so this is just a
constant times sine of y, so that's gonna equal that same constant times the cosine of y, which is the derivative of sine. So now what the gradient does is it just puts both of these together in a vector. And specifically, maybe
I'll change colors here, you denote it with a little
upside-down triangle. The name of that symbol is nabla, but you often just pronounce it del, you'd say del f or gradient of f. And what this equals is a vector that has those two
partial derivatives in it. So the first one is the partial derivative with respect to x, to x times sine of y. And the bottom one, partial
derivative with respect to y X-squared cosine of y. And notice, maybe I should emphasize, this is actually a vector-valued function. So maybe I'll give it a
little bit more room here and emphasize that it's got an x and a y. This is a function that takes in a point in two-dimensional space and outputs a two-dimensional vector. So you could also imagine doing this with three different variables. Then you would have three
partial derivatives, and a three-dimensional output. And the way you might
write this more generally is we could go down here
and say the gradient of any function is equal to a vector with
its partial derivatives. Partial of f with respect to x, and partial of f with respect to y. And in some sense, we call
these partial derivatives. I like to think as the
gradient as the full derivative cuz it kind of captures
all of the information that you need. So a very helpful mnemonic device with the gradient is to
think about this triangle, this nabla symbol as being a vector full of partial derivative operators. And by operator, I just mean like partial with respect to x, something where you
could give it a function, and it gives you another function. So you give this guy the function f and it gives you this expression, this multi-variable function as a result. So the nabla symbol is this vector full of different partial derivative operators. And in this case it might
just be two of them, and this is kind of a weird thing because it's like what, this is a vector, it's
got like operators in it, that's not what I thought vectors do. But you can kind of see where it's going. It's really just, you can think of it as a memory trick, but in some sense it's a
little bit deeper than that. And really when you take this triangle and you say ok let's take this triangle and you can kind of imagine
multiplying it by f, really it's like an operator
taking in this function and it's gonna give you another function. It's like you take this
triangle and you put an f in front of it, and you can imagine, like this part gets
multipled, quote unquote multiplied with f, this
part gets quote unquote multiplied with f but
really you're just saying you take the partial
derivative with respect to x and then with y, and on and on. And the reason for doing this, this symbol comes up a
lot in other contexts. There are two other operators
that you're gonna learn about called the divergence and the curl. We'll get to those later, all in due time. But it's useful to think about this vector-ish thing of partial derivatives. And I mean one weird thing about it, you could say ok so this
nabla symbol is a vector of partial derivative operators. What's its dimension? And it's like how many
dimensions do you got? Because if you had a
three-dimensional function that would mean that you should treat this like it's got three different
operators as part of it. And you know I'd kinda,
finish this off down here, and if you had something
that was 100-dimensional it would have 100
different operators in it and that's fine. It's really just again, kind of a memory trick. So with that, that's how
you compute the gradient. Not too much too it, it's pretty much just partial derivatives, but you smack em into a vector where it gets fun and
where it gets interesting is with the geometric interpretation. I'll get to that in
the next couple videos. It's also a super important tool for something called the
directional derivative. So you've got a lot of fun stuff ahead.