whats is the big O notation of this function f(x) = logn + 3n i have ridden big o notation but i am confuse in this function so please help me
It is simply O(n).
When you have a composite of multiple parts in big O notation which are added, you have to choose the biggest one. In this case it is O(3n), but there is no need to include constants inside parentheses, so we are left with O(n).
https://en.wikipedia.org/wiki/Big_O_notation
It's O(n).
Proof:
lim n->inf f(n)/3n = lim n->inf (logn + 3n) / 3n = [0 + 1] = 1
so f(n) and 3n have the same asimptotic behavior
according to the "O" notation constants are dropped so f(n) is O(3n) = O(n)
Related
I want to Express each of the following functions using Big-O notation.
a(n) = 2n + 3n^2 + nlog(n)
b(n) = 5nlog(n) + 10n^3 + n^2
for a(n) I assumed that the answer would be O(n^2) However apparently it is O(n^3)
this is the same for b(n) where I assumed the notation would be O(n^3) however it is O(n^4). Is it a rule to round up the power when writing the notation? Why would this be the case? Isn't the notation supposed to take the upper-bound?
You are right, a(n) = O(n2) and b(n) = O(n3).
However, notice that a(n) is also O(n3) and indeed O(n1000). Usually though, we want to express the tightest bound we can find.
My textbook describes the relationship as follows:
There is a very nice mathematical intuition which describes these classes too. Suppose we have an algorithm which has running time N0 when given an input of size n, and a running time of N1 on an input of size 2n. We can characterize the rates of growth in terms of the relationship between N0 and N1:
Big-Oh Relationship
O(log n) N1 ≈ N0 + c
O(n) N1 ≈ 2N0
O(n²) N1 ≈ 4N0
O(2ⁿ) N1 ≈ (N0)²
Why is this?
That is because if f(n) is in O(g(n)) then it can be thought of as acting like k * g(n) for some k.
So for example if f(n) = O(log(n)) then it acts like k log(n), and now f(2n) ≈ k log(2n) = k (log(2) + log(n)) = k log(2) + k log(n) ≈ k log(2) + f(n) and that is your desired equation with c = k log(2).
Note that this is a rough intuition only. An example of where it breaks down is that f(n) = (2 + sin(n)) log(n) = O(log(n)). The oscillating 2 + sin(n) bit means that f(2n)-f(n) can be basically anything.
I personally find this kind of rough intuition to be misleading and therefore worse than useless. Others find it very helpful. Decide for yourself how much weight you give it.
Basically what they are trying to show is just basic algebra after substituting 2n for n in the functions.
O(log n)
log(2n) = log(2) + log(n)
N1 ≈ c + N0
O(n)
2n = 2(n)
N1 ≈ 2N0
O(n²)
(2n)^2 = 4n^2 = 4(n^2)
N1 ≈ 4N0
O(2ⁿ)
2^(2n) = 2^(n*2) = (2^n)^2
N1 ≈ (N0)²
Since O(f(n)) ~ k * f(n) (almost by definition), you want to look at what happens when you put 2n in for n. In each case:
N1 ≈ k*log 2n = k*(log 2 + log n) = k*log n + k*log 2 ≈ N0 + c where c = k*log 2
N1 ≈ k*(2n) = 2*k*n ≈ 2N0
N1 ≈ k*(2n)^2 = 4*k*n^2 ≈ 4N0
N1 ≈ k*2^(2n) = k*(2^n)^2 ≈ N0*2^n ≈ N0^2/k
So the last one is not quite right, anyway. Keep in mind that these relationships are only true asymptotically, so the approximations will be more accurate as n gets larger. Also, f(n) = O(g(n)) only means g(n) is an upper bound for f(n) for large enough n. So f(n) = O(g(n)) does not necessarily mean f(n) ~ k*g(n). Ideally, you want that to be true, since your big-O bound will be tight when that is the case.
log n^2 is equivalent to 2logn which grows at the same rate as logn, as I disregard the factors and constants. but if I was to square the whole term so that I end up with (logn)^2 is it also big theta of logn?
No. If f is any unbounded function then f(n)^2 is not O(f).
Because f(n)^2 = O(f) means there's a c and N such that n > N implies f(n)^2 <= cf(n). Which implies f(n) <= c, and so f is bounded.
log(n) is unbounded, so log(n)^2 is not O(log(n)).
log (n^2) = 2 log(n)
and as you know x^2 is not in thetha(x).
Think this way: let N=log(n). Then f1(N)=N^2 where f2(N)=N, obviously,
N=o(N^2)!=theta(N^2), i.e., log(n)=o((log(n))^2)!=theta((log(n))^2).
Also, lim {n->inf} f2(n) / f1(n) = lim {n->inf} 1 / log(n) = 0, by definition of small o (https://en.wikipedia.org/wiki/Big_O_notation) it implies f2(n)=o(f1(n)).
I have this:
a) f(n) = n
b) f(n) = 1045n
c) f(n) = n2 + 70
d) f(n) = 7n + 3
e) f(n) = Cn + D (where C and D are both constants)
f) f(n) = 8
g) f(n) = n3 + n + 1
h) f(n) = 4n + 2log n + 5
I want to check if the Big O notation of them is O(n).
How can I determinate it?
And how to find the Big-O notation for the functions below:
a) f(n) = 3n3 + n
b) f(n) = 3 log n + 5n
c) f(n) = 3n2 + 5n + 4
d) f(n) = 3n3 + n2 + 5n + 99
f(x) is O(g(x)) if there exists a constant c such that f(x) < c*g(x)
You should look at the "biggest" asymptoticall factor in your function (highest exponent or something like that) and that would be your big O
Example: f(n) = 2^n + n^5 + n^2 + n*log(n) + n
This function has 5 different factors that influence big O, sorted from biggest to smallest, so this one would be O(2^n). Drop the 2^n and now f(n) is O(n^5).
Constants are O(1).
Hope I explained it well
Generally the Big O notation of a function is measured by the largest power of n that appears. In your case, this would be n², since the only other factor is the 70 which is constant.
Edit: Original post only contained the function f(n) = n² + 70.
See this answer.
In short, there's no set way to determine Big O results. Strictly speaking any function which eventually (for some n) will always be bigger than your function, is Big O of it. In practice you're looking for as tight a bound as you can get. If the only components of the function are polynomial in n, then the Big O will just be the largest power of n that appears (or rather, n to that power). Another useful thing to know is that log n is of a lower order than n (but higher than constant).
2^n + 6n^2 + 3n
I guess it's just O(2^n), using the highest order term, but what is the formal approach to go about proving this?
You can prove that 2^n + n^2 + n = O(2^n) by using limits at infinity. Specifically, f(n) is O(g(n)) if lim (n->inf.) f(n)/g(n) is finite.
lim (n->inf.) ((2^n + n^2 + n) / 2^n)
Since you have inf/inf, an indeterminate form, you can use L'Hopital's rule and differentiate the numerator and the denominator until you get something you can work with:
lim (n->inf.) ((ln(2)*2^n + 2n + 1) / (ln(2)*2^n))
lim (n->inf.) ((ln(2)*ln(2)*2^n + 2) / (ln(2)*ln(2)*2^n))
lim (n->inf.) ((ln(2)*ln(2)*ln(2)*2^n) / (ln(2)*ln(2)*ln(2)*2^n))
The limit is 1, so 2^n + n^2 + n is indeed O(2^n).
Please see:
Big O Notation Homework--Code Fragment Algorithm Analysis?
Big O, how do you calculate/approximate it?
Can someone please explain the difference between Big-O and Little-O Notation?
Big-O for Eight Year Olds? (not meant to be insulting)