Multi-Variable Calculus: Partial Derivatives Using Level Curves

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SUMMARY

This discussion focuses on the application of partial derivatives using level curves in multi-variable calculus. The participant outlines the definitions of first and second-order partial derivatives, including f_x, f_y, f_{xx}, f_{xy}, and f_{yy}. Key observations include the relationship between the signs of these derivatives and the behavior of the function f at specific points. The participant seeks clarity on how to analytically confirm their qualitative observations regarding the function's behavior.

PREREQUISITES
  • Understanding of partial derivatives and their notation
  • Familiarity with level curves and their interpretation
  • Knowledge of limit definitions of derivatives
  • Basic concepts of multivariable calculus
NEXT STEPS
  • Study the limit definition of partial derivatives in detail
  • Explore the geometric interpretation of level curves in multivariable functions
  • Learn about the implications of second-order partial derivatives
  • Investigate applications of partial derivatives in optimization problems
USEFUL FOR

Students and educators in mathematics, particularly those studying or teaching multi-variable calculus, as well as anyone interested in understanding the practical applications of partial derivatives and level curves.

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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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).
 

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