Understanding Big O Notation: Limit Definition and Reconciling Statements

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In summary: Can you explain what epsilon might be used for here?The limit definition of "big O" given by the limit one should be able to take, as above.The relation ##|u|\leq \epsilon |f| \implies |u|=O(\epsilon)## ... looks like a typo.Possibly the author just refers to the order in "epsilon".
  • #1
ericm1234
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I am trying to reconcile the following statement:

" ||u||<=eps*||f|| means ||u||=O(eps) ("||u|| is order eps")... "

with the limit definition of "big O"; considering it's not clear that ||u|| here even depends on eps:

" lim as eps goes to 0 of ||u||/eps, by definition of "big O", should equal some constant "; is it necessarily ||f||?

I understand that, for example, sin(x)=O(x) because lim as x goes to 0 of sin(x)/x is bounded.

So to casually say something is "order epsilon", if it doesn't depend on epsilon, I am not sure how to reconcile that with the definition of "big O" given by the limit one should be able to take, as above.
 
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  • #3
I paraphrased for the sake of drawing attention to my (pedantic) question;
The original statement, from a book on finite element, was:
||u-u_s||<=eps*||u-u_s||_E <=eps^2*||f|| (the middle norm is energy norm)

the following text then goes:
"The point of course is that ||u-u_s||_E (energy norm) is of order eps, whereas ||u-u_s|| is of order eps^2."

So maybe you can explain based on this context then?

And sorry for lack of "tex" skills.
 
  • #4
It looks like the author is not using regular big-O notation and/or conventions here.
Please provide a citation.

Possibly the author just refers to the order in "epsilon".
What is epsilon supposed to stand for?
 
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  • #5
Well, that was sort of what I figured..but anyway, Brenner, Mathematical Theory of Finite Element, p.6 bottom.

Thanks
 
  • #6
I don't know that one - the best way to be sure is to look for the same lesson in another text and compare.

If I said that |y| = x|k| while |z|=x^2|k| then you could say that y = O(x) while z=O(x^2).
I'm thinking that the dependent variable in the inequalities above is epsilon.
 
  • #7
Thanks; do you believe (as in, is it plausible) that the author means: ||u-u_s||/||f||<=eps means that thing less than eps is "of order epsilon"?
 
  • #8
Really need the context.
But, especially if ||f|| is just a number, it would be fair to say that ||u-u_s||=O(eps)
i.e. it seems one could expand the expressions as ##\sum_n a_n\epsilon^n ## ... whatever epsilon is supposed to represent. You are the one with the book in front of you.
 

1. What is Big O notation and why is it important in computer science?

Big O notation is a way of measuring the time and space complexity of an algorithm. It represents the worst-case scenario of how an algorithm's performance will scale as the input size increases. It is important in computer science because it allows us to compare and analyze the efficiency of different algorithms and choose the best one for a particular problem.

2. How is Big O notation calculated?

Big O notation is calculated by looking at the number of operations an algorithm performs in the worst-case scenario. It does not take into account constants or lower order terms, but focuses on the dominant term that determines the overall complexity.

3. What are the different types of Big O notation and their time complexities?

The most common types of Big O notation are:

  • O(1) - Constant time
  • O(log n) - Logarithmic time
  • O(n) - Linear time
  • O(nlog n) - Linearithmic time
  • O(n^2) - Quadratic time

The time complexities increase in the order listed above, with O(1) being the most efficient and O(n^2) being the least efficient.

4. How is Big O notation used in real-world scenarios?

Big O notation is used in real-world scenarios to analyze and improve the performance of algorithms and data structures. It helps in optimizing code, reducing processing time, and improving overall efficiency. It is also useful for predicting how an algorithm will perform as the input size increases and identifying potential bottlenecks.

5. Can Big O notation be used to compare algorithms with different input sizes?

Yes, Big O notation can be used to compare algorithms with different input sizes. This is because it focuses on the growth rate of an algorithm rather than the actual execution time. It allows us to compare the efficiency of algorithms without being affected by the specific input size or machine specifications.

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