Multi-Variable Calculus: Partial Derivatives Using Level Curves

In summary, the conversation is about a bonus problem on a homework assignment where the student is having trouble setting it up. They provide their best guesses for the equations involved and discuss their initial observations. They also mention the possibility of providing an analytical explanation and their professor's requirement for a "sufficiently precise qualitative explanation."
  • #1
Dembadon
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Homework Statement



This is a bonus problem on our homework, and I'm having trouble figuring out how to setup what I need.

calc3bonus.jpg


Homework Equations



Here are my best guesses:
[tex]f_x=\frac{\partial f}{\partial x}[/tex]
[tex]f_y=\frac{\partial f}{\partial y}[/tex]
[tex]f_{xx}=\frac{\partial}{\partial x}\left(\frac{\partial f}{\partial x}\right)[/tex]
[tex]f_{xy}=\frac{\partial}{\partial y}\left(\frac{\partial f}{\partial x}\right)[/tex]
[tex]f_{yy}=\frac{\partial}{\partial y}\left(\frac{\partial f}{\partial y}\right)[/tex]
[tex]f_x=\frac{f(x_0+h,y_0)-f(x,y)}{h}[/tex]
[tex]f_y=\frac{f(x_0,y_0+h)-f(x,y)}{h}[/tex]

The Attempt at a Solution



My professor said that a "sufficiently precise qualitative explanation" (whatever the hell that means) will be good enough. If it's possible, I would rather provide an analytical explanation. Maybe with the limit definition of the derivative? I'm having trouble figuring out what I need to use, and I have a feeling it's embarrassingly simple. :frown:

Initial observations:

(a) If [itex]f[/itex] is increasing at [itex]P[/itex] then [itex]\frac{\partial f}{\partial x}[/itex] is positive. If [itex]f[/itex] is decreasing, then [itex]\frac{\partial f}{\partial x}[/itex] is negative, right?

(b) Same line of reasoning from (a), but holding [itex]x[/itex] constant.

(c) For [itex]f_{xx}[/itex] the contours appear to be closer together for [itex]x<x_0[/itex] than for [itex]x>x_0[/itex]. This indicates that [itex]f_{xx}[/itex] is negative, right?

(d) For [itex]f_{xy}[/itex] , I think this means that I'm supposed to observe how [itex]f_x[/itex] changes when [itex]f_y[/itex] changes, right?

(e) For [itex]f_{yy}[/itex] the contours appear to be closer together for [itex]y>y_0[/itex] than for [itex]y<y_0[/itex]. This indicates that [itex]f_{yy}[/itex] is positive, right?
 
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  • #2
Intuitively, fx is something like "the (instantaneous) change in f in the x-direction only" - so what do you expect?

You can confirm this suspicion analytically if you look at the definition of fx[/b]. Just take h > 0 and check the signs of the denominator and the numerator (you'll have to assume that the level curves are a reasonable representation, e.g. that f doesn't rise to 12 in between the level curves for 6 and 4).
 

Related to Multi-Variable Calculus: Partial Derivatives Using Level Curves

1. What is multi-variable calculus?

Multi-variable calculus is a branch of mathematics that deals with functions of several variables. It involves studying the rates of change of multivariable functions, and how these functions behave in different directions.

2. What are partial derivatives?

Partial derivatives are derivatives of functions with more than one variable. They represent the rate of change of a function with respect to one of its variables, while holding all other variables constant.

3. How are partial derivatives calculated using level curves?

Partial derivatives can be calculated using level curves by drawing a curve on a 3-dimensional graph where the function has a constant value. The slope of this curve at a particular point represents the partial derivative of the function at that point.

4. Why are partial derivatives important in multi-variable calculus?

Partial derivatives are important in multi-variable calculus because they allow us to study the behavior of a multivariable function in different directions. They are also used in optimization problems to find maximum and minimum values of a function.

5. What are some real-world applications of partial derivatives using level curves?

Partial derivatives using level curves have many real-world applications, such as in economics to analyze supply and demand functions, in physics to study the motion of objects in multiple dimensions, and in engineering to optimize designs of structures or systems.

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