Partial Derivate of multivariable cos

In summary, the partial derivative of R with respect to t is vt cos x. The partial derivative of v with respect to t is -vt sin x, and the partial derivative of x with respect to t is -vt sin x.
  • #1
Adyssa
203
3

Homework Statement



Partial derivative of R = vt cos x with respect to t

Homework Equations



NA

The Attempt at a Solution



Keeping the other variables constant, I get 'v cos x'. (t is 1)

I just want to check this, because it's part of a larger problem and I feel my answer is way off because of this function. I'm a bit rusty on my calculus, it's been ... 13 years since I did it in high school!

Also, if I calculate the partial derivative with respect to x, I get '-vt sin x'?
 
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  • #2
If v and x are functions of t, then vt cos(x) is actually a product of three terms. You will need to differentiate this using the Product Rule.
 
  • #3
OK, I think I'm confused as to what a Partial Derivative is then. According to the wiki page example:

[URL]http://upload.wikimedia.org/math/c/e/b/cebdc47218ace0b076c636d3e073f2c5.png[/URL]

[URL]http://upload.wikimedia.org/math/b/0/b/b0b10dc199691e38d0c9ccf8d7aec19a.png[/URL]

In the above example the xy term becomes y, whereas if the product rule was used, it would be x'y + y'x = y + x, or have I missed something?

Let me elaborate on the problem I'm trying to solve, it's an error bound and the equation is:

|Error(R)| <= |dR/dv(v)||Error(v)| + |dR/dt(t)||Error(t)| + |dR/dx(x)||Error(x)|

where dR/dv(v) is the Partial Derivative of R = vt cos x wrt v (multiplied by the error in v), and similarly for the two other derivatives wrt t and x.
 
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  • #4
I take it x is the angle a projectile is being launched at, so it's not a function of time. Usually, the angle is denoted by a Greek letter, like θ, and the displacement in the horizontal direction, which is a function of time, is denoted by x.

So as far as your original post goes, your partial derivatives are correct.
 
  • #5
Oh, yes it is an angle (in radians) and the symbol was phi, but I didn't have it at hand, so I just used x. I didn't realize that would alter the equation. Thanks for the clarity! :)
 
  • #6
Adyssa said:
OK, I think I'm confused as to what a Partial Derivative is then. According to the wiki page example:

[URL]http://upload.wikimedia.org/math/c/e/b/cebdc47218ace0b076c636d3e073f2c5.png[/URL]

[URL]http://upload.wikimedia.org/math/b/0/b/b0b10dc199691e38d0c9ccf8d7aec19a.png[/URL]

In the above example the xy term becomes y, whereas if the product rule was used, it would be x'y + y'x = y + x, or have I missed something?
You have missed that the derivative of y with respect to x is 0. So one of the terms you get when you use the product rule on "xy" is 0. By the way, you shouldn't write (xy)'=x'y+xy', because this notation doesn't reveal what functions you're taking the derivative of. I like the notation [tex]\frac{d}{dx}(xy)=\bigg(\frac{d}{dx}x\bigg)y+x\bigg(\frac{d}{dx}y\bigg)=1y+x0=y.[/tex] Alternatively, you can define two functions f and g by [itex]f(x)=x[/itex] for all x, and [itex]g(x)=y[/itex] for all x. (Note that this makes g a constant function). Then you can write (fg)'(x)=f'(x)g(x)+f(x)g'(x)=1y+x0=y.

Regarding what a partial derivative is...don't think of it as something different from an ordinary derivative. It isn't. If you're asked to compute the partial derivative of xy2 with respect to x, it means this: Let f be the function defined by f(t)=ty2 for all t. Find f'(x) (i.e. the derivative of f, evaluated at x). If you're asked to compute the partial derivative of xy2 with respect to y, it means this: Let g be the function defined by g(t)=xt2 for all t. Find g'(y) (i.e. the derivative of g, evaluated at y).

The partial derivative of vt cos x with respect to t...that's the ordinary derivative of the function that takes t to vt cos x (as opposed to e.g. the function that takes x to vt cos x).

See what I mean when I say that a partial derivative isn't really something different from an ordinary derivative? The "with respect to" part of it is just telling you which function to take an ordinary derivative of.
 
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  • #7
Thanks Fredrik, that clears things up a lot, I appreciate the thorough explanation.
 

Related to Partial Derivate of multivariable cos

1. What is a partial derivative of a multivariable cosine function?

A partial derivative of a multivariable cosine function is a mathematical concept used to find the rate of change of the function with respect to one of its variables while holding the other variables constant. It is denoted by ∂/∂x and is calculated by treating all other variables as constants and differentiating the function with respect to the chosen variable.

2. Why is it important to calculate partial derivatives of multivariable cosine functions?

Partial derivatives of multivariable cosine functions are important because they allow us to analyze the behavior of a function in multiple dimensions. They also help us find the direction of steepest increase or decrease of the function at a specific point, which is useful in optimization problems.

3. How do you find the partial derivative of a multivariable cosine function?

To find the partial derivative of a multivariable cosine function, we treat all other variables as constants and differentiate the function with respect to the chosen variable. For example, to find ∂f/∂x of the function f(x,y) = cos(x+y), we would treat y as a constant and differentiate cos(x+y) with respect to x, giving us ∂f/∂x = -sin(x+y).

4. Can a multivariable cosine function have more than one partial derivative?

Yes, a multivariable cosine function can have multiple partial derivatives. This is because there are multiple ways to differentiate the function with respect to different variables. For example, in the function f(x,y) = cos(x+y), we can calculate both ∂f/∂x and ∂f/∂y.

5. How are partial derivatives of multivariable cosine functions used in real-world applications?

Partial derivatives of multivariable cosine functions are used in various fields, including physics, engineering, economics, and finance. They are used to model and analyze complex systems with multiple variables, such as heat flow, fluid dynamics, and market trends. They are also essential in the development of machine learning algorithms and statistical models.

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