Optimizing Polynomial Approximations for C2 Functions on Closed Intervals

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In summary, the problem is to show that for a function f:R-->R of class C2, given a positive number \epsilon and b>0, there exists a polynomial p such that the error between p and f, p' and f', and p'' and f'' is less than \epsilon for all x in [0,b]. The solution involves choosing a polynomial q to approximate f'', using the definition of the derivative, and considering the accumulated error in approximating f' by p'.
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Demon117
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1. Suppose that f:R-->R is of class C2. Given b>0 and a positive number [tex]\epsilon[/tex], show that there is a polynomial p such that

|p(x)-f(x)|<[tex]\epsilon[/tex], |p'(x)-f'(x)|<[tex]\epsilon[/tex], |p"(x)-f"(x)|<[tex]\epsilon[/tex] for all x in [0,b].



The Attempt at a Solution



First I choose a polynomial q that approximates f''. If |q - f''|<[tex]\eta[/tex] throughout [0,b], and if p is the polynomial such that p''=q, p(0)=f(0), and p'(0)=f'(0), then I come to this question: How big can |p' - f'| and |p - f| be in terms of [tex]\eta[/tex] and b? I think I am thinking about this correctly, but I cannot come to a conclusion. Should I use the definition of the derivative namely:

For all [tex]\epsilon[/tex]>0, there is a [tex]\delta[/tex]>0, such that when 0<|t-x|<[tex]\delta[/tex], this guarantees that |(p(t)-p(x))/(t-x) - L|<[tex]\epsilon[/tex]; where L is the derivative at x.
 
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Observe that [tex](p' - f')' = p'' - f''[/tex]; what does this tell you about the accumulated error in approximating [tex]f'[/tex] by [tex]p'[/tex], as you move across the interval from [tex]0[/tex] to [tex]b[/tex]?
 

Related to Optimizing Polynomial Approximations for C2 Functions on Closed Intervals

What are polynomial approximations?

Polynomial approximations are mathematical functions that are used to approximate the behavior of more complex functions. They are typically used when it is difficult or impossible to find an exact solution to a problem.

How are polynomial approximations calculated?

Polynomial approximations are calculated using a process called polynomial regression. This involves finding the best-fitting polynomial curve that approximates a set of data points. The degree of the polynomial is determined by the number of data points and the desired level of accuracy.

What are the advantages of using polynomial approximations?

One of the main advantages of using polynomial approximations is that they are relatively simple and easy to calculate, making them useful for solving complex problems. They also provide a good balance between accuracy and simplicity, and can be used to approximate a wide range of functions.

What are the limitations of polynomial approximations?

One of the main limitations of polynomial approximations is that they may not accurately represent the behavior of a function outside of the range of data points used to calculate the approximation. They also may not be suitable for highly nonlinear functions or functions with rapidly changing behavior.

How are polynomial approximations used in science?

Polynomial approximations are used in a variety of scientific fields, including physics, engineering, and computer science. They are particularly useful for modeling and predicting the behavior of complex systems and processes, and are commonly used in data analysis and machine learning.

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