Greetings,
I am in an introductory real analysis course. Our professor claims that one of the problems in our text likely cannot be solved, so he assigned us a much simpler problem (provable directly from the standard multivariable analogue which assumes that the function is continuously differentiable). I began working on the original problem to try to figure out why it can't be solved as asked.
Note: I am not terribly proficient with LaTeX, so please forgive any abuses of notation. Likely, I am abusing notation due to my troubles in generating the proper formulas.
The problem states:
Letbe differentiable,
. Assume that the set of derivatives
is convex. Prove that there existswhich satisfies
.
My professor says that he doesn't see where the author was going with this, and he doesn't believe convexity to be enough. He believes a stronger condition, such as continuity of the derivative, is required.
So, I set off to work. Here is what I came up with so far:
Ifthen the solution follows trivially, so assume
Differentiability ofimplies that for every
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Claim:
Proof:
So, assume that for someno such delta exists. This implies that
, a contradiction.
Becauseis an interval, with no loss of generality, assume
.
Given, choose
for each
as shown to exist in the claim above.
The collectionis an open cover for
. By covering compactness, there exists a finite subcover.
Choose one such subcoverindexed so that
implies
.
Next, if any ball, then remove
from
(Here is a good example of abuse of notation, and I apologize. I am not sure what other symbols are available to use in place of this to show the refinement of the finite open cover.)
What should be left is an open cover with the following properties:
Let.
Let
Assume
Let
Essentially, I am determining midpoints between each point in my partition of. By construction,
and each
So, by repeated applications of the Taylor Approximation Theorem, we have:
Becausewe have
Finally, we have a convex combination of derivative matrices that when multiplied byare a distance of no more than epsilon from
. So, we can find derivative matrices that are close to the average derivative.
It is clear that the limit exists and tends to. However, because our set of derivatives
is not closed, this limit is not necessarily contained in
.
This is the point I was able to get to on my own. Now, I want to try to understand under what circumstances such a limit would be contained in the set. It is possible that it requires continuity of the derivative, although I am hoping that I will see something else. Personally, my intuition is telling me that the author was correct, and convexity is enough, but I am not quite grasping the final argument.
Here is what I gather (described in a rather informal manner, as I have not yet figured out the mathematics required to prove my claim nor even begin to describe it):
Whenis small and
is large, it implies a large range of values for which the derivative matrix at x adequately maps the change in the function over a large distance. Therefore, locally, the larger
is, the more linear the function appears. Linearity implies constancy of the derivative, and a constant derivative is locally continuous. Therefore, the limit as
approaches zero tends to weight the convex combination more heavily with derivatives at points in neighborhoods that are mostly linear, yet weighing derivatives less heavily at points in neighborhoods that are extremely non-linear. So, I would like to say that the convexity of the set of derivatives somehow implies Riemann integrability, allowing me to use the Fundamental Theorem of Calculus to discover the average derivative.
Now, I also know that I can use uniform approximations of the derivative to uncover a continuous family of functions that converge pointwise to my derivative, and then I could further use Baire's theorem to show that my derivative has a dense set of continuity points. However, I am not sure how to proceed. We have not yet gotten to Lebesgue measures, so I don't yet feel comfortable using that.
Would anyone have any suggestions of how I might proceed from here? Either proving that convexity of the set of derivatives is a sufficient condition for the general analogue to the Mean Value Theorem, or if that is not possible, providing insight into why it is not?


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