Big Omg(f1) subs of Big Omg(f2) iff f1 element BigOmg(f2)

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In summary, the problem asks to prove that if two functions are asymptotically bounded, then the function defined by the intersection of the two sets is asymptotically bounded. The proof given uses the contradiction that if one function is not in the other, then it must be in the set of functions asymptotically bounded below by the other.
  • #1
zzmanzz
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Homework Statement



I usually struggle with proofs and would appreciate some help with the following problem:

Prove the following fact:

[tex] \Omega(f_1) \subseteq \Omega(f_2) [/tex] iff [tex] f_1 \in \Omega(f_2) [/tex]

Homework Equations



[tex] \Omega(f_1) [/tex] is the set of functions g s.t.
[tex] g(n) \geq c f_1(n) [/tex] where [tex] n_1 \geq n_o [/tex]

The Attempt at a Solution


[/B]
1. [tex] \Omega(f_1) = g_1 (n_1) \geq c f_1(n_1) [/tex]
[tex] \Omega(f_2) = g_2 (n_1) \geq c f_2(n_2) [/tex]

2. [tex] f_1 \in \Omega(f_2) = f_2(n) \geq c f_1(n) [/tex]

2. implies that
[tex] g_2(n) \geq f_2(n) \geq c f_1(n) [/tex]

which shows that [tex] \Omega(f_1) \subseteq \Omega(f_2) [/tex] from 1.

Is this on the right track? Thanks
 
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  • #2
##\Omega(f_1) = g_1 (n_1) \geq c f_1(n_1)##
You can't set a set of functions equal to a single function.
##g_2(n) \geq f_2(n) \geq c f_1(n)##
Where would the first inequality come from?

Also be careful that you don't reuse "c" for different things.
 
  • #3
mfb said:
You can't set a set of functions equal to a single function.
Where would the first inequality come from?

Also be careful that you don't reuse "c" for different things.

Thank you for the response. I think I may have made a mistake in the notation so I went back to the textbook to make sure that everything is accurate for this Big Omega which provides an asymptotic lower bound for functions.

1.

[tex] \Omega(f_1) = \{ g_1(n) :\exists c_1, \exists n_0 , c_1 > 0, s.t. 0 \leq c_1 f_1 (n_1) \leq g_1 (n_1) \quad n_1 \geq n_0 \} [/tex] [tex] \Omega(f_2) = \{ g_2(n) :\exists c_2, \exists n_0 , c_2 > 0, s.t. 0 \leq c_2 f_2 (n_2) \leq g_2 (n_2) \quad n_2 \geq n_0 \} [/tex]

2.

[tex] f_1 \in \Omega(f_2) = \quad \exists c_3, \exists n_0 , c_3 > 0 \quad f_2(n_3) \leq c_3 f_1(n_3) \quad n_3 \geq n_0 [/tex]

Maybe here I can use a proof by contradiction and state that :

[tex] f_1 \notin \Omega(f_2) [/tex]

implies 3.

[tex] \quad \exists c_3, \exists n_0 , c_3 > 0 \quad f_2(n_3) \geq c_3 f_1(n_3) \quad n_3 \geq n_0 [/tex]

but in order to have:

[tex] \Omega(f_1) \subseteq \Omega(f_2) [/tex]

3 can't be true because this fould imply that [tex] f_2 [/tex] is not a lower bound on [tex] f_1 [/tex]
 
  • #4
zzmanzz said:
Maybe here I can use a proof by contradiction and state that :

[tex] f_1 \notin \Omega(f_2) [/tex]

implies 3.

[tex] \quad \exists c_3, \exists n_0 , c_3 > 0 \quad f_2(n_3) \geq c_3 f_1(n_3) \quad n_3 \geq n_0 [/tex]
It doesn't imply that. As an example, ##f_1(x)=x^2 |\sin(\pi x/2)| \notin \Omega(x)## but there is no ##c_3, n_0## where ##x \geq c_3 f_1(n_3) \quad \forall n_3 \geq n_0##.

##\notin## needs a bit more thought what it means.
 
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  • #5
Thanks for the example - very helpful.

I think that
[tex] f_1 \notin \Omega(f_2) [/tex]

means that f1 is not in the set of functions asymptotically bounded below by f2and implies

[tex] \forall c_1 > 0 ,n_0, \exists n > n_0 \quad c_1 f_2(n) > f_1(n) [/tex]In this adjustment of the formula, we say that some n exists where this is not true. I think earlier I made the mistake of giving an asymptotic upper bound definition for the contradiction.

For your example:

[tex] f_1(x)=x^2 |\sin(\pi x/2)| \notin \Omega(x) [/tex]

we would have:

[tex] c_1 x > x^2 |\sin(\pi x/2)| [/tex]

which is true whenever the sin part is negative.

However, I think that this contradiction still works for the proof because we just showed that

[tex] f_1 \notin \Omega(f_2) [/tex]
[tex] \forall c_1 > 0 ,n_0, \exists n > n_0 \quad c_1 f_2(n) > f_1(n) [/tex]
can't have
[tex] \Omega(f_1) \subseteq \Omega(f_2) [/tex]
where
[tex] f_2(n) > f_1(n)[/tex]
 
  • #6
That is good.
zzmanzz said:
which is true whenever the sin part is negative.
The sine part has to be zero (note the absolute value of it - I wanted to avoid negative numbers), but the key point here is the existence of some n where the right side is small.

You'll have to show ##f_1 \in \Omega(f_1)## to complete that direction.
If ##f_1 \notin \Omega(f_2)## and ##f_1 \in \Omega(f_1)## then ##\Omega(f_1)## cannot be a subset of ##\Omega(f_2)##.
 

Related to Big Omg(f1) subs of Big Omg(f2) iff f1 element BigOmg(f2)

What is the significance of Big Omg(f1) subs of Big Omg(f2)?

The notation "Big Omg(f1) subs of Big Omg(f2)" is used to indicate that the function f1 is a subset of the function f2 in terms of their growth rates. This means that f1 grows no faster than f2.

What does "iff" mean in this context?

"iff" stands for "if and only if," which is a logical statement used to show that two statements are equivalent. In this case, it means that the functions f1 and f2 have the same growth rate.

How is the Big O notation used in this concept?

The Big O notation is used to describe the asymptotic behavior of a function. In this context, it is used to compare the growth rates of two functions and determine if one is a subset of the other.

Can you provide an example of Big Omg(f1) subs of Big Omg(f2)?

One example is the functions f1(n) = 2n and f2(n) = 3n. Both of these functions have a linear growth rate, so they belong to the same Big O notation "family." However, f1 grows slower than f2, so we can say that f1 is a subset of f2 in terms of their growth rates.

What is the importance of understanding "Big Omg(f1) subs of Big Omg(f2)" in computer science?

In computer science, understanding the growth rate of algorithms and data structures is crucial for analyzing their time and space complexity. The notation "Big Omg(f1) subs of Big Omg(f2)" allows us to compare the efficiency of different algorithms and determine which one is more time or space-efficient.

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