Derivative of best approximation

So g'(x) approximates f'(x) well (in the sense of the infinity norm), but f'(x) is not in the space of functions used for the approximation.In summary, the best approximation of a continuous, differentiable function f(x) in the infinity norm from a finite dimensional vector space may not have a derivative that is well approximated by the derivative of the approximation. This can be seen through an example of g(x)=f(x)+sin(1000000x)/1000, where g'(x) approximates f'(x) well but f'(x) is not in the space of functions used for the approximation.
  • #1
ekkilop
29
0
Say that we have a continuous, differentiable function f(x) and we have found the best approximation (in the sense of the infinity norm) of f from some set of functions forming a finite dimensional vector space (say, polynomials of degree less than n or trigonometric polynomials of degree less than n or basically anything satisfying the Haar condition).

What can be said about how well the derivative, f'(x), is approximated by the derivative of the approximation?

Thank you.
 
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  • #2
ekkilop said:
What can be said about how well the derivative, f'(x), is approximated by the derivative of the approximation?
Not very much. Take g(x)=f(x)+sin(1000000x)/1000 as an approximation to f(x). Then g'(x) = f'(x)+1000*cos(x).
 

Related to Derivative of best approximation

What is the "Derivative of best approximation"?

The derivative of best approximation is a mathematical concept that involves finding the rate of change of a function at a specific point using the method of least squares. It is used to find an approximate solution to a problem when an exact solution is not possible.

How is the "Derivative of best approximation" calculated?

The derivative of best approximation is calculated using the method of least squares, which involves minimizing the sum of the squared differences between the actual data points and the predicted values. This is done by finding the slope of the line that best fits the data points.

What is the significance of the "Derivative of best approximation"?

The derivative of best approximation is significant because it allows us to find an approximate solution to a problem when an exact solution is not possible. It is commonly used in fields such as statistics, economics, and engineering to make predictions and estimate values.

What are the limitations of the "Derivative of best approximation"?

One limitation of the derivative of best approximation is that it only provides an approximate solution, which may not be completely accurate. Additionally, it assumes that the data follows a linear trend, and may not work well for data with non-linear relationships.

How is the "Derivative of best approximation" used in real life?

The derivative of best approximation is used in various real-life applications, such as predicting stock prices, analyzing trends in data, and estimating values for scientific experiments. It is also used in fields like economics to make projections and in engineering to design and optimize systems.

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