What Happens to the Laplace Transform of Asymptotic Functions?

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In summary, the conversation discusses the relationship between two functions, f(x) and g(x), and their Laplace transforms. The question is whether the Laplace transform of f(x) and g(x) are equal, given that f(x) is asymptotic to g(x). The answer is that in general, the Laplace transforms may not be equal due to the difference between f(x) and g(x) for small x values. However, for large x values, the difference may be smoothed out by the exponential kernel in the Laplace integral. The second question asks about the validity of using the inverse operator for asymptotic relations, to which the response is that this is the definition of an inverse operator.
  • #1
mhill
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let be [tex] f(x) \sim g(x) [/tex] , in the sense that for big x f(x) is asymptotic to g(x) , my question if what happens to their Laplace transform ??

i believe that [tex] \int _{0}^{\infty}dt f(t)exp(-st) \approx \int _{0}^{\infty}dt g(t)exp(-st) [/tex]

in first approximation the Laplace transform of f(x) and the Laplace transform of g(x) must be equal.

another question if we had a Linear operator L so we can define its inverse L^{-1} is it true that [tex] f(x) \sim L(g(x)) \rightarrow L^{-1} f(x)= g(x) [/tex]
 
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  • #2
The answer to your first question is "what do you mean by [itex]\approx[/itex]?". The difference between f and g may be extremely large for small x: and that difference will show up in the integral.

As for your second question- isn't that the definition of "inverse"?
 
  • #3
Yes Hallsoftivy L^{-1} means the inverse operator, but is it valid even for asymptotic relations ?? , and as for the Laplace integral if f(x) is asymptotic to g(x) and the Laplace transform of f(x) and g(x) are denoted by F(s) G(s) then would be true that [tex] F(s) \sim G(s) [/tex] ?, but i believe that having a kernel inside the integral transform exp(-st) for big values of 's' the remainder of the asymptotic relation could be make smoother
 

1. What is asymptotic analysis?

Asymptotic analysis is a method used in mathematics and computer science to analyze the behavior of a function as its input approaches infinity. It helps us understand the efficiency and performance of algorithms and mathematical functions.

2. Why is asymptotic analysis important?

Asymptotic analysis allows us to compare the performance of different algorithms and determine which one is the most efficient. It also helps in predicting the behavior of a function for large inputs, which is crucial in real-world applications.

3. What are the different types of asymptotic analysis?

The two main types of asymptotic analysis are Big O notation and Big Omega notation. Big O notation represents the upper bound of a function, while Big Omega notation represents the lower bound. There is also Big Theta notation, which represents both the upper and lower bounds of a function.

4. How is asymptotic analysis different from average case analysis?

Asymptotic analysis focuses on the behavior of a function as its input approaches infinity, while average case analysis considers the expected performance of a function for a given set of inputs. Asymptotic analysis is a more general approach and provides a broader understanding of a function's performance.

5. Can asymptotic analysis be used for all types of functions?

No, asymptotic analysis is only applicable to functions with large inputs. It is not useful for functions that have a small or constant input size, as the behavior of the function may not change significantly for different input sizes.

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