Easy second order partial derivative

In summary, a second order partial derivative is a mathematical concept that measures the rate of change of a function with respect to two different independent variables. It is calculated by taking the partial derivative of the first order partial derivative with respect to the second independent variable, while holding the first independent variable constant. The notation used for a second order partial derivative is d²f/dx², and its geometric interpretation is the rate of change of the slope of a curve in a specific direction, representing the curvature of the function at a given point. This concept is important in science as it allows for the analysis of complex functions in multiple dimensions, and is commonly used in fields such as physics, engineering, and economics.
  • #1
sainistar
4
0
Hello Experts

I have a simple question.

Given V as the function of Z and Y,
Given Z as the function of R and L, [itex]Z=R+s*L [/itex]
Given Y as the function of G and C, [itex]Y=G+s*C [/itex]
Assume we also know [itex]\frac{\partial V}{\partial Z}[/itex] and [itex]\frac{\partial^2 V}{\partial Z \partial Y}[/itex]
If we want to know [itex]\frac{\partial V}{\partial R}[/itex], it will be equal to [itex]\frac{\partial V}{\partial Z} * \frac{\partial Z}{\partial R}[/itex]
Question:
How can we find [itex]\frac{\partial^2 V}{\partial R\partial G}[/itex], by using what is given and known?

Regards
Aman
 
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  • #2


Hello Aman,

To find \frac{\partial^2 V}{\partial R\partial G}, we can use the chain rule of partial derivatives. The chain rule states that for a function f(x,y) with x and y both functions of another variable t, the partial derivative of f with respect to t can be found by multiplying the partial derivatives of f with respect to x and y by the corresponding derivatives of x and y with respect to t.

In this case, we have V as a function of Z and Y, where Z is a function of R and L and Y is a function of G and C. So we can use the chain rule to find \frac{\partial V}{\partial R} and \frac{\partial V}{\partial G}, and then multiply them together to find \frac{\partial^2 V}{\partial R\partial G}.

Using the chain rule, we have:

\frac{\partial V}{\partial R} = \frac{\partial V}{\partial Z} * \frac{\partial Z}{\partial R}
\frac{\partial V}{\partial G} = \frac{\partial V}{\partial Y} * \frac{\partial Y}{\partial G}

Substituting the given functions for Z and Y, we have:

\frac{\partial V}{\partial R} = \frac{\partial V}{\partial Z} * s
\frac{\partial V}{\partial G} = \frac{\partial V}{\partial Y} * 1

Then, to find \frac{\partial^2 V}{\partial R\partial G}, we multiply these two partial derivatives together:

\frac{\partial^2 V}{\partial R\partial G} = \frac{\partial V}{\partial Z} * s * \frac{\partial V}{\partial Y}

So, using the given information and the chain rule, we can find \frac{\partial^2 V}{\partial R\partial G}.

I hope this helps!
 

FAQ: Easy second order partial derivative

What is a second order partial derivative?

A second order partial derivative is a mathematical concept that measures the rate of change of a function with respect to two different independent variables. It is the derivative of a first order partial derivative.

How is a second order partial derivative calculated?

A second order partial derivative is calculated by taking the partial derivative of the first order partial derivative with respect to the second independent variable, while holding the first independent variable constant.

What is the notation used for a second order partial derivative?

The notation used for a second order partial derivative is d²f/dx², where f is the function and x is the independent variable.

What is the geometric interpretation of a second order partial derivative?

The geometric interpretation of a second order partial derivative is the rate of change of the slope of a curve in a specific direction. It represents the curvature of the function at a given point.

Why is the concept of second order partial derivatives important in science?

Second order partial derivatives are important in science because they allow us to analyze the behavior of complex functions in multiple dimensions. They are frequently used in fields such as physics, engineering, and economics to model and understand real-world phenomena.

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