Analysis:Why does Weierstrass Aprox. Thm require compactness?

  • Thread starter mscudder3
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In summary, the Weierstrass approximation theorem states that continuous functions on a compact set in R can be approximated by polynomials. This statement was tested earlier today and it was found that unbounded continuous functions are not so easy to approximate. Even bounded functions may not be approximated.
  • #1
mscudder3
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The Weierstrass Approximation Theorem states "Let f be a continuous function on a compact set K in R. Therefore, f can be uniformly approximated by polynomials.

This profound statement was testing my logic earlier today. I was pondering why K must be compact. I figure if it is not bounded, say R itself, then p(sub n) would continue to converge to a value, while say some f=sinx was slowly oscillating about the x axis? (i.e. while p reaches its limit, f goes beyond it)

I was hoping someone could explain why it does not converge on say (0.1), a bounded and closed set. How do the functions act at these boundary points?

Thanks!
 
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  • #2
The point is that continuous functions on compact sets are bounded. Unbounded continuous functions may not be so easy to approximate with polynomials.

On (0,1) you could have the function f(x) = 1/x + 1/(x-1). This function will have the vertical asymptotes x = 0 and x = 1, and no polynomial can uniformally approximate that to any degree. (0,1) is not a closed set however. If it were, as you say, closed and bounded, it would imply that it was compact.

Even bounded functions may not be approximated, your example sin(x) is such a function. The reason is simply that any non-constant polynomial will approach infinity as x gets large, hence no non-constant polynomial will uniformally approximate sin(x) to any degree.
 
  • #3
Thanks but I was wondering about the detail of one of the points you made.
From the above:
"The function will have the vertical asymptotes x = 0 and x = 1, and no polynomial can uniformally approximate that to any degree."

Why exactly is this? Sorry if this is overly thorough, I just cannot see the argument. TO rephrase, what property of polynomials informs us that they may not aprox. a function on an open set (given that x is bounded).
 
  • #4
Of course, some functions may be approximated, a trivial example being a constant function on (0,1). But in general, the theorem cannot extended to open sets. I brought a counter-example, and it relies on the simple fact that no polynomial can have vertical asymptotes. Note that this would be required, as (if I name my function f(x)) for any polynomial P(x), |P(x)-f(x)| being bounded on (0,1) would require that P(x) to approach infinity as x approaches 0.
 

1. What is the Weierstrass Approximation Theorem?

The Weierstrass Approximation Theorem is a fundamental result in mathematical analysis that states that any continuous function on a closed and bounded interval can be approximated by a sequence of polynomials.

2. Why does the Weierstrass Approximation Theorem require compactness?

The Weierstrass Approximation Theorem requires compactness because it is a necessary condition for the existence of a maximum and minimum value of a function on a closed and bounded interval. This allows us to construct a sequence of polynomials that converge uniformly to the continuous function.

3. What is compactness in mathematical analysis?

In mathematical analysis, compactness is a property of a set that ensures that every sequence in the set has a convergent subsequence. In simpler terms, it means that the set is closed and bounded.

4. How does compactness relate to the Weierstrass Approximation Theorem?

The Weierstrass Approximation Theorem requires compactness because it guarantees the existence of a maximum and minimum value of a function on a closed and bounded interval, which is necessary for constructing a sequence of polynomials that converges uniformly to the function.

5. What are the implications of the Weierstrass Approximation Theorem in mathematical analysis?

The Weierstrass Approximation Theorem has many important implications in mathematical analysis, such as proving the existence of solutions to differential equations, establishing the convergence of numerical methods, and providing a powerful tool for approximating functions in various mathematical and scientific fields.

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