Real Analysis - Differentiation in R^n - Example of a specific function

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SUMMARY

The discussion focuses on constructing a continuous function f: R² → R that has partial derivatives at the origin (0,0) with both f₁(0,0) and f₂(0,0) non-zero, yet the vector (f₁(0,0), f₂(0,0)) does not indicate the direction of maximal change. This problem originates from TBB's Elementary Real Analysis and serves as a challenge to understand the conditions under which the theorem regarding directional derivatives applies. Participants emphasize the importance of analyzing the theorem's statement and proof to identify the necessary qualities that the function must lack.

PREREQUISITES
  • Understanding of partial derivatives in multivariable calculus
  • Familiarity with the concept of directional derivatives
  • Knowledge of continuous functions in R²
  • Ability to analyze mathematical theorems and their conditions
NEXT STEPS
  • Study the theorem related to directional derivatives in multivariable calculus
  • Explore examples of continuous functions that fail to meet specific conditions
  • Learn about the implications of the gradient in relation to maximal change
  • Investigate counterexamples in real analysis to deepen understanding
USEFUL FOR

Students of multivariable calculus, educators teaching real analysis, and anyone interested in the nuances of differentiation in higher dimensions.

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Homework Statement



Give an example of a continuous function f:R^2→R having partial derivatives at (0,0) with
f_1 (0,0)≠0,f_2 (0,0)≠0
But the vector (f_1 (0,0),f_2 (0,0)) does not point in the direction of maximal change, even though there is such a direction.

(If this is too difficult to read, please see the PDF for a nicer version)

Note that this is a problem from TBB's Elementary Real Analysis

Homework Equations



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The Attempt at a Solution



I have no idea how to attempt the construction of such a function. Any tips, suggestions, or a walkthrough of how to find such a function would be greatly appreciated. If you feel like giving me an answer, please explain it because understanding this is the most important part of this.
 

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I think you've recognized that this problem has you finding a "counterexample" to a well-known theorem in multivariate calc (it's not really a counterexample to the the theorem, because the theorem is true). If I were you, I would look carefully at the statement and proof of that theorem to see what conditions a function needs to satisfy in order for the theorem to apply. Then try to construct a function that doesn't have the necessary qualities.

Also, the fact that the problem doesn't use the word "gradient" may be a hint.
 

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