Proving g is a Function of u in Partial Differentiation

In summary, by changing the variables to u = x^2-y^2 and v=2xy, it is shown that the function f(x,y) satisfies the differential equation y{\partial f \over \partial x} + x{\partial f \over \partial y} = 0 is only a function of x^2-y^2. This is confirmed by the fact that \frac{\partial g}{\partial v} = 0, showing that g is only a function of u.
  • #1
Gregg
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0

Homework Statement



The function f(x,y) satisfies the d.e.

[tex] y{\partial f \over \partial x} + x{\partial f \over \partial y} = 0 [/tex]

By changing to new vars [tex]u = x^2-y^2[/tex] and [tex]v=2xy[/tex] show that f is a function of [tex]x^2-y^2[/tex] only.

Homework Equations



[tex] \frac{\partial }{\partial x}=\frac{\partial u}{\partial x}\frac{\partial }{\partial u}+\frac{\partial v}{\partial x}\frac{\partial }{\partial v} [/tex]

[tex]\frac{\partial }{\partial y}=\frac{\partial u}{\partial y}\frac{\partial }{\partial u}+\frac{\partial v}{\partial y}\frac{\partial }{\partial v}[/tex]

The Attempt at a Solution



[tex] y (\frac{\partial u}{\partial x}\frac{\partial }{\partial u}+\frac{\partial v}{\partial x}\frac{\partial }{\partial v}) g + x (\frac{\partial u}{\partial y}\frac{\partial }{\partial u}+\frac{\partial v}{\partial y}\frac{\partial }{\partial v}) g = y{\partial f \over \partial x} + x{\partial f \over \partial y} = 0 = 0[/tex]

[tex]\left(2y^2+2x^2\right)\frac{\partial g}{\partial v}=x\frac{\partial f}{\partial y}+y\frac{\partial f}{\partial x}=0[/tex]

I did that and so

[tex] \frac{\partial g}{\partial v}=0 [/tex]

How does this confirm g=g(u)? Is is because the slope of g with respect to v is 0? The so called function of v is just a constant or 0?
 
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  • #2
I didn't check your arithmetic, but to answer your question, if g is a function of u and v and gv = 0, then yes, that does show g is only a function of u.
 

Related to Proving g is a Function of u in Partial Differentiation

What is partial differentiation?

Partial differentiation is a mathematical process used to calculate the rate of change of a multivariable function with respect to one of its variables while holding the other variables constant. It is a way to analyze how a function changes in response to changes in one specific variable.

What is the difference between partial differentiation and ordinary differentiation?

The main difference between partial differentiation and ordinary differentiation is that in partial differentiation, only one variable is varied while all others are kept constant, whereas in ordinary differentiation, all variables are allowed to vary. Additionally, partial differentiation results in partial derivatives, which represent the rate of change with respect to each individual variable, while ordinary differentiation results in a single derivative representing the overall rate of change.

Why is partial differentiation important in science?

Partial differentiation is important in science because many natural phenomena and physical systems are dependent on multiple variables. By using partial differentiation, scientists can analyze the behavior and relationships between these variables in a more precise and accurate manner. It is also a crucial tool in the fields of physics, engineering, economics, and many other areas of science.

What is the process for performing partial differentiation?

To perform partial differentiation, you must first identify the variable you want to differentiate with respect to. Then, you take the derivative of the function with respect to that variable, treating all other variables as constants. This results in a partial derivative. You can repeat this process for each variable in the function to obtain multiple partial derivatives.

What are some common applications of partial differentiation?

Partial differentiation has many applications in science and engineering. Some common examples include determining the maximum or minimum value of a function, optimizing a system, and analyzing the rate of change of a system with respect to different variables. It is also used in fields such as thermodynamics, fluid mechanics, and economics to model and understand complex systems.

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