Multivariable calculus proof involving the partial derivatives of an expression

  • #1
KungPeng Zhou
22
7
Homework Statement
Supposed f is a function of several variables that satisfies the equation f(tx, ty, tz) =t^{n}f(x, y, z),(t as any number).
Prove:
##xf_{x}+yf_{y}+zf_{z}=nf(x, y, z) ##
Relevant Equations
Partial derivative related formulas
For the first equation:
##f(tx, ty, tz)=f(u, v, w) ##, ##u=tx, v=ty, w=tz##,##k=f(u, v, w) ####
t^{n}f_{x}=\frac{\partial f}{\partial u} \cdot \frac{\partial u}{\partial x}##
As the same calculation
##xf_{x}+yf_{y}+zf_{z}=[\frac{\partial f}{\partial x} + \frac{\partial f}{\partial y} +\frac{\partial f}{\partial z}] t^{1-n}##
But I can't continue with it.
 
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  • #2
KungPeng Zhou said:
Homework Statement: Supposed f is a function of several variables that satisfies the equation f(tx, ty, tz) =t^{n}f(x, y, z),(t as any number).
Prove:
##xf_{x}+yf_{y}+zf_{z}=nf(x, y, z) ##
Relevant Equations: Partial derivative related formulas

For the first equation:
##f(tx, ty, tz)=f(u, v, w) ##, ##u=tx, v=ty, w=tz##,##k=f(u, v, w) ####
t^{n}f_{x}=\frac{\partial f}{\partial u} \cdot \frac{\partial u}{\partial x}##
As the same calculation
##xf_{x}+yf_{y}+zf_{z}=[\frac{\partial f}{\partial x} + \frac{\partial f}{\partial y} +\frac{\partial f}{\partial z}] t^{1-n}##
But I can't continue with it.
Introducing the variables ##u, v, w## looks unnecessary to me. Why not partially differentiate with respect to ##t##?
 
  • #3
KungPeng Zhou said:
Prove:
##xf_{x}+yf_{y}+zf_{z}=nf(x, y, z) ##
Note that this equation is somewhat sloppy. The arguments of the function ##f## are given on the right-hand side, but the arguments of the functions ##f_x, f_y## and ##f_z## on the left-hand side are not. More precise and logical would be:$$xf_{x}(x, y, x)+yf_{y}(x, y, z)+zf_{z}(x, y, z)=nf(x, y, z)$$Or, in shorthand:$$xf_{x}+yf_{y}+zf_{z}=nf$$Where the arguments ##(x, y, z)## are understood by default.
 
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  • #4
Note that ##n\cdot f(x,y,z) = \left. \dfrac{d}{dt}\right|_{t=1} \left(t^n f(x,y,z)\right)=\left. \dfrac{d}{dt}\right|_{t=1}f(tx,ty,tz).##
 
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  • #5
PeroK said:
Note that this equation is somewhat sloppy. The arguments of the function ##f## are given on the right-hand side, but the arguments of the functions ##f_x, f_y## and ##f_z## on the left-hand side are not. More precise and logical would be:$$xf_{x}(x, y, x)+yf_{y}(x, y, z)+zf_{z}(x, y, z)=nf(x, y, z)$$Or, in shorthand:$$xf_{x}+yf_{y}+zf_{z}=nf$$Where the arguments ##(x, y, z)## are understood by default.
Ok, I have solved it. I need to defferential f(tx, ty, tz) with respect to t
 

Related to Multivariable calculus proof involving the partial derivatives of an expression

What is a partial derivative?

A partial derivative of a function of multiple variables is its derivative with respect to one of those variables, with all other variables held constant. It measures how the function changes as only that one variable changes.

How do you prove that mixed partial derivatives are equal?

To prove that mixed partial derivatives are equal, you generally use Clairaut's theorem (also known as Schwarz's theorem). This theorem states that if the second mixed partial derivatives of a function are continuous, then the mixed partial derivatives are equal. Formally, if \( f \) is a function of \( x \) and \( y \), then \( \frac{\partial^2 f}{\partial x \partial y} = \frac{\partial^2 f}{\partial y \partial x} \) provided the mixed partials are continuous.

What is the chain rule in multivariable calculus?

The chain rule in multivariable calculus is a formula for computing the derivative of the composition of multiple functions. If \( z \) is a function of \( x \) and \( y \), and both \( x \) and \( y \) are functions of \( t \), then the chain rule states that \( \frac{dz}{dt} = \frac{\partial z}{\partial x}\frac{dx}{dt} + \frac{\partial z}{\partial y}\frac{dy}{dt} \).

How do you find the critical points of a multivariable function?

To find the critical points of a multivariable function, you need to find the points where all the first partial derivatives are zero. That is, if \( f \) is a function of \( x \) and \( y \), you must solve the system of equations \( \frac{\partial f}{\partial x} = 0 \) and \( \frac{\partial f}{\partial y} = 0 \). The solutions to this system are the critical points.

What is a gradient and how is it used?

The gradient of a multivariable function is a vector that consists of all its first partial derivatives. For a function \( f(x, y) \), the gradient is \( \nabla f = \left( \frac{\partial f}{\partial x}, \frac{\partial f}{\partial y} \right) \). The gradient points in the direction of the steepest ascent of the function and its magnitude indicates the rate of increase in that direction. It is used in optimization problems to find local maxima and minima.

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