Partial differentiation and partial derivatives

In summary, the problem involves finding the partial derivatives of x and y with respect to s and t, given two equations involving x, y, s, and t. The equations are solved using Cramer's Rule, but the simpler method is to just differentiate directly.
  • #1
Tabiri
16
0

Homework Statement



If ##xs^2 + yt^2 = 1## (1) and ##x^2s + y^2t = xy - 4,## (2) find ##\frac{\partial x}{\partial s}, \frac{\partial x}{\partial t}, \frac{\partial y}{\partial s}, \frac{\partial y}{\partial t}## at ##(x,y,s,t) = (1,-3,2,-1)##.

Homework Equations



Pretty much those just listed above.

The Attempt at a Solution



Alright so I spent quite a while on this one. First of all, I took the differentials of both the equations, (1) and (2), and came up with $$s^2\,dx + 2sx\,ds + t^2\,dy + 2ty\,dt = 0 $$ for (1) and $$ 2xs\,dx + x^2\,ds + 2yt\,dy + y^2\,dt = y\,dx + x\,dy$$ for (2).

Then, substituting in ##(x,y,s,t) = (1,-3,2,-1)## for the respective variables I came up with $$4\,dx + 4\,ds + \,dy + 6\,dt = 0 $$ for (1) and $$4\,dx + \,ds + 6\,dy + 9\,dt = -3\,dx + \,dy$$ for (2).

Simplifying and moving things around a bit, I got $$ 4\,dx + 4\,ds = -\,dy - 6\,dt $$ for (1) and $$ 7\,dx + \,ds = -5\,dy - 9\,dt $$ for (2).

Then I used Cramer's Rule to solve these two equations for ##\,dx## and ##\,ds.##. For ##\,dx## I got $$ \,dx = \frac{
\begin{vmatrix}
-\,dy - 6\,dt & 4 \\
-5\,dy - 9\,dt & 1
\end{vmatrix}
}{
\begin{vmatrix}
4 & 4 \\
7 & 1
\end{vmatrix}
}

= \frac{-\,dy - 6\,dt + 4(5\,dy + 9\,dt)}{-24} = \frac{-\,dy - 6\,dt + 20\,dy + 36\,dt}{-24} = \frac{19\,dy + 30\,dt}{-24} = \frac{-19\,dy - 30\,dt}{24}$$

$$ \,ds = \frac{
\begin{vmatrix}
4 & -\,dy - 6\,dt \\
7 & -5\,dy - 9\,dt
\end{vmatrix}
}{
\begin{vmatrix}
4 & 4 \\
7 & 1
\end{vmatrix}
} = \frac{4(-5\,dy - 9\,dt) + 7(\,dy + 6\,dt)}{-24} = \frac{-20\,dy - 36\,dt + 7\,dy + 42\,dt}{-24} = \frac{-13\,dy + 6\,dt}{-24} = \frac{13\,dy - 6\,dt}{24}.
$$

Then I changed the equations around to $$\,dy + 6\,dt = -4\,dx - 4\,ds$$ for (1) and $$5\,dy + 9\,dt = -7\,dx - \,ds$$ for (2). Using Cramer's Rule again to solve for ##\,dy## and ##\,dt## I got
$$\,dy = \frac{-6\,dx + 30\,ds}{21}$$
$$\,dt = \frac{-13\,dx - 19\,ds}{21}.$$

Then I wanted to find ##\frac{\partial x}{\partial s}##, so I took what I got for ##\,dy## and ##\,dt## plugged it into (1), ##4\,dx + 4\,ds + \,dy + 6\,dt = 0,## and got $$4\,dx + 4\,ds + \frac{-6\,dx + 30\,ds}{21} + 6(\frac{-13\,dx - 19\,ds}{21}) = 0.$$ Multiplying this all out, everything just cancels to zero and I can't find ##\frac{\partial x}{\partial s}##. I've checked the math for the applications of Cramer's Rule and can't find anything wrong there, so... what am I doing wrong?
 
Last edited:
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  • #2
Tabiri said:

Homework Statement



If ##xs^2 + yt^2 = 1## (1) and ##x^2s + y^2t = xy - 4,## (2) find ##\frac{\partial x}{\partial s}, \frac{\partial x}{\partial t}, \frac{\partial y}{\partial s}, \frac{\partial y}{\partial t}## at ##(x,y,s,t) = (1,-3,2,-1)##.



Homework Equations



Pretty much those just listed above.



The Attempt at a Solution



Alright so I spent quite a while on this one. First of all, I took the differentials of both the equations, (1) and (2), and came up with $$s^2\,dx + 2sx\,ds + t^2\,dy + 2ty\,dt = 0 $$ for (1) and $$ 2xs\,dx + x^2\,ds + 2yt\,dy + y^2\,dt = y\,dx + x\,dy$$ for (2).

Then, substituting in ##(x,y,s,t) = (1,-3,2,-1)## for the respective variables I came up with $$4\,dx + 4\,ds + \,dy + 6\,dt = 0 $$ for (1) and $$4\,dx + \,ds + 6\,dy + 9\,dt = -3\,dx + \,dy$$ for (2).

Simplifying and moving things around a bit, I got $$ 4\,dx + 4\,ds = -\,dy - 6\,dt $$ for (1) and $$ 7\,dx + \,ds = -5\,dy - 9\,dt $$ for (2).

Then I used Cramer's Rule to solve these two equations for ##\,dx## and ##\,ds.##. For ##\,dx## I got $$ \,dx = \frac{
\begin{vmatrix}
-\,dy - 6\,dt & 4 \\
-5\,dy - 9\,dt & 1
\end{vmatrix}
}{
\begin{vmatrix}
4 & 4 \\
7 & 1
\end{vmatrix}
}

= \frac{-\,dy - 6\,dt + 4(5\,dy + 9\,dt)}{-24} = \frac{-\,dy - 6\,dt + 20\,dy + 36\,dt}{-24} = \frac{19\,dy + 30\,dt}{-24} = \frac{-19\,dy - 30\,dt}{24}$$

$$ \,ds = \frac{
\begin{vmatrix}
4 & -\,dy - 6\,dt \\
7 & -5\,dy - 9\,dt
\end{vmatrix}
}{
\begin{vmatrix}
4 & 4 \\
7 & 1
\end{vmatrix}
} = \frac{4(-5\,dy - 9\,dt) + 7(\,dy + 6\,dt)}{-24} = \frac{-20\,dy - 36\,dt + 7\,dy + 42\,dt}{-24} = \frac{-13\,dy + 6\,dt}{-24} = \frac{13\,dy - 6\,dt}{24}.
$$

Then I changed the equations around to $$\,dy + 6\,dt = -4\,dx - 4\,ds$$ for (1) and $$5\,dy + 9\,dt = -7\,dx - \,ds$$ for (2). Using Cramer's Rule again to solve for ##\,dy## and ##\,dt## I got
$$\,dy = \frac{-6\,dx + 30\,ds}{21}$$
$$\,dt = \frac{-13\,dx - 19\,ds}{21}.$$

Then I wanted to find ##\frac{\partial x}{\partial s}##, so I took what I got for ##\,dy## and ##\,dt## plugged it into (1), ##4\,dx + 4\,ds + \,dy + 6\,dt = 0,## and got $$4\,dx + 4\,ds + \frac{-6\,dx + 30\,ds}{21} + 6(\frac{-13\,dx - 19\,ds}{21}) = 0.$$ Multiplying this all out, everything just cancels to zero and I can't find ##\frac{\partial x}{\partial s}##. I've checked the math for the applications of Cramer's Rule and can't find anything wrong there, so... what am I doing wrong?

I cannot understand what you are trying to do, or why you want to do it. The easiest way is just to differentiate directly. Letting ##x_s = \partial x/\partial s##, etc, we have:
[tex] (1) \Longrightarrow s^2 x_s + 2 s x + t^2 y_s = 0 \\
(2) \Longrightarrow x^2 + 2 s x x_s + 2 t y y_s = x y_s + y x_s
[/tex]
which you can solve for ##x_s,y_s##. It is much easier if you first substitute in the numerical values of ##x,y,s,t##.
 
  • #3
As in solve for ##y_s## in (1) and then plug that value for ##y_s## into (2)?

Edit: Also, I know that the answer for ##\frac{\partial x}{\partial s}## is ##\frac{-19}{13}##
 
  • #4
Tabiri said:
As in solve for ##y_s## in (1) and then plug that value for ##y_s## into (2)?

Edit: Also, I know that the answer for ##\frac{\partial x}{\partial s}## is ##\frac{-19}{13}##

You have two equations in the two unknowns ##x_s, y_s##, and these equations are linear in these unknowns. They happen to be nonlinear in ##s,t,x,y##, but that does not matter, since we are already given numerical values for them. The things we don't know are ##x_s, y_s## and ##x_t,y_t##.
 
  • #5
Yeah, I just did it that way and got the right answer. Thanks!
 

FAQ: Partial differentiation and partial derivatives

1. What is partial differentiation?

Partial differentiation is a mathematical concept used to find the rate of change of a function with respect to one of its variables while holding the other variables constant. It allows us to analyze how a function changes in one direction while keeping all other variables fixed.

2. What is a partial derivative?

A partial derivative is the result of performing partial differentiation on a multivariable function. It represents the rate of change of the function with respect to one of its variables, holding all other variables constant. It is denoted by ∂f/∂x, where f is the function and x is the variable.

3. Why is partial differentiation important?

Partial differentiation is important because it allows us to analyze the behavior of a function in a specific direction while keeping other variables constant. This is useful in many fields, including physics, economics, and engineering, as it helps us understand how changes in one variable affect the overall function.

4. How is partial differentiation different from ordinary differentiation?

The main difference between partial differentiation and ordinary differentiation is that in partial differentiation, we are only considering the change in one variable, while holding all other variables constant. In ordinary differentiation, we are finding the change in the function with respect to one variable, without considering the other variables.

5. What are some applications of partial differentiation?

Partial differentiation has many applications in mathematics and various fields of science. Some common applications include optimization problems in economics, finding critical points in multivariable calculus, and analyzing the behavior of functions in physics. It is also used in computer science for image processing and machine learning algorithms.

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