Big O notation (for calculus, not computer science)

In summary: I don't think this changes the nature of the question drastically.In summary, the conversation discusses the formal definition and intuitive interpretation of big O notation. It is mentioned that the notation requires a limit and that it is common practice to use O(f(x)) to mean "as x goes to infinity". The question of whether f(x) grows at the same rate as n-1/4 for large n is also addressed, with the conclusion that it is not always the case. An example is given to illustrate this point.
  • #1
kungal
5
0
I understand the formal definition for big O notation but is there an intuitive interpretation?. For example, if

Code:
f(x) = O(x[SUP]-1/4[/SUP])

is it reasonable to say that for large n f(x) grows at the same rate as
Code:
n[SUP]-1/4[/SUP]
?

Thanks in advance
 
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  • #2
Your first line is meaningless.
An O-notation requires a limit to which the independent variable is supposed to go.
 
  • #3
The O's should actually be Op's (my mistake) and x is a set of random variables. I don't think this changes the nature of the question drastically.

Note that the result f(x) = Op(x-1/4) is quite common so it can't be a meaningless statement.
 
  • #4
kungal said:
The O's should actually be Op's (my mistake) and x is a set of random variables. I don't think this changes the nature of the question drastically.

Note that the result f(x) = Op(x-1/4) is quite common so it can't be a meaningless statement.

Yes it is.

Is it meant that f(x) is O(x^(-1/4) ) as x goes to zero, or as x goes to, say, infinity?
That is two entirely different situations, and needs, therefore, to be specified.
Hence, the meaninglessness of your first line.
 
  • #5
I hate to disagree with Arildno but it is common practice (though perhaps "abuse of notation") to use just O(f(x)) to mean "as x goes to infinity".
 
  • #6
HallsofIvy said:
I hate to disagree with Arildno but it is common practice (though perhaps "abuse of notation") to use just O(f(x)) to mean "as x goes to infinity".
Well, if that is the general default notation, I'll make a note of that.

In my own applied maths books, they dutifully make explicit what limiting operation we are speaking about
 
  • #7
I'm glad we've cleared that up but is it reasonable to say that
if f = Op(n-1/4) then for large n f grows at the same rate as n-1/4?
 
  • #8
kungal said:
I'm glad we've cleared that up but is it reasonable to say that
if f = Op(n-1/4) then for large n f grows at the same rate as n-1/4?

Not at all.

f might, for example, become more and more strongly oscillatory as x goes to infinty, even though f's magnitude will be bounded by some constant multiplied by x^(-1/4).

For example, let
[tex]f(x)=Ax^{-\frac{1}{4}}\cos(x^{2})[/tex]

This f is definitely O(x^(-1/4)), but its rate of growth will, be:
[tex]\frac{df}{dx}\to{-2A}x^{\frac{3}{4}}\sin(x^{2}), x\to\infty[/tex]
 
  • #9
Thanks
 
  • #10
Well, O's should actually be Op's (my mistake) and x is a set of random variables.
 

Related to Big O notation (for calculus, not computer science)

1. What is Big O notation?

Big O notation is a mathematical concept used to describe the behavior or complexity of a function or algorithm as the size of the input increases. It is often used in calculus to analyze the efficiency of algorithms and the rate of growth of functions.

2. How is Big O notation used in calculus?

In calculus, Big O notation is used to analyze the behavior of functions as the input approaches a certain value, typically infinity. It helps to determine the overall trend or growth rate of a function, rather than focusing on specific values.

3. What is the difference between Big O notation and Big Theta notation?

Big O notation describes the upper bound or worst-case scenario of a function, while Big Theta notation describes both the upper and lower bounds. In other words, Big O notation gives an upper limit on the growth rate of a function, whereas Big Theta notation gives a more precise estimate of the growth rate.

4. How does Big O notation help in algorithm analysis?

Big O notation is used in algorithm analysis to determine the efficiency of an algorithm, by looking at how the algorithm's runtime or space requirements change as the input size increases. It helps to identify the best algorithm for a given problem and to optimize existing algorithms.

5. Can Big O notation be applied to any type of function?

Yes, Big O notation can be applied to any type of function, regardless of whether it is continuous or discrete. However, it is most commonly used for functions that grow at a steady rate, such as polynomials or exponential functions.

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