I'm trying to calculate the time complexity of the following code snippet
sum = 0;
for(i = 0; i <= n; i++) {
for(j = 1; j <= i; j++) {
if(i % j == 0) {
for(k = 0; k <= n; k++) {
sum = sum + k;
}
}
}
}
What i think , that out of N iterations of First loop, only 1 value which is 0 allowed to enter K loop and from i = 1.....N, K loop never runs.
So, only 1 value of I runs j loop N times and k loop N times and for other values of I only J loop runs N times
So, is the TC = O(N^2) ?
Here let d(n) is the number of divisors of n.
I see your program doing O(n) work(innermost loop) for O( d(n) ) number of divisors of each i (i looping from 0 to n in outermost loop: O(n) ).
Its complexity is O( n * d(n) * n ).
Reference
for large n, d() ~ O( exp( log(n)/log(log n) ) ).
So the overall complexity is O( n^(2 + 1/log(log n) ) ).
I've got another answer. Lets replace the inner loop with an abstract func():
for(i=0;i<=n;i++) {
for(j=1;j<=i;j++) {
if(i%j==0) {
func();
}
}
}
Firstly, forgetting the calls to func(), the complexity M to calculate all (i % j) is O(n^2).
Now, we can ask ourselves how many times the func() is called. It's called once for each divisor of i. That is it is called d(i) times for each i. This is a Divisor summatory function D(n). D(n) ~ n log n for large n.
So func() is called n log n times. At the same time the func() itself has complexity of O(n). So it gives the complexity P = O(n * n log n).
So total complexity is M + P = O(n^2) + O(n^2 log n) = O(n^2 log n)
Edit
Vow, thanks for downvote! I guess I need to prove it using python.
This code prints out n, how many times the inner loop is called for n, and outputs ratio of the latter and Divisor summatory function
import math
n = 100000
i = 0
z = 0
gg = 2 * 0.5772156649 - 1
while i < n:
j = 1
while j <= i:
if i % j == 0:
#ignoring the most inner loop just calculate the number of times it is called
z+=1
j+=1
if i > 0 and i % 1000 == 0:
#Exact divisor summatory function, to make z/Di converge to 1.0 quicker
Di = (i * math.log(i) + i * gg)
#prints n Di z/Di
print str(i) + ": " + str(z) + ": " + str(z/Di)
i+=1
Output sample:
24000: 245792: 1.00010672544
25000: 257036: 1.00003672445
26000: 268353: 1.00009554815
So the most inner loop is called n * log n times, and total complexity is n^2 * log n
Related
for (i = 1; i <= n; i++)
{
for (j = n; j >= i; j--)
}
I'm struggling with this algorithm. I can't even know what time complexity of this algorithm is? I've checked using online software it shows me only o(n).
First of all, the algorithm should be something like this:
for (i = 1; i <= n; i++)
for (j = n; j >= i; j--)
DoSomeWork(i, j); // <- Payload which is O(1)
To find out the time complexity, let's count how many times DoSomeWork will be executed:
i : j : executed, times
----------------------------
1 : 1..n : n
2 : 2..n : n - 1
3 : 3..n : n - 2
.. : ... ...
n : n..n : 1
So far so good, DoSomeWork will be executed
n + n - 1 + n - 2 + ... + 2 + 1 = (n + 1) * n / 2
times; time complexity for your case is
O((n + 1) * n / 2) = O((n + 1) * n) = O(n * n) + O(n) = O(n * n)
Nested loops are not necessary have quadratic time complexity, e.g.
for (i = 1; i <= n; i++)
for (j = n; j >= i; j /= 2) // note j /= 2, instead of j--
DoSomeWork(i, j);
has O(n * log(n)) time complexity
Think about this a little. How many times do we enter into the outer loop? It's n times, as you surely already know, since we have step1, ..., stepn, in total n steps.
So, we have
n * average(inner)
In the first step, the inner loop has n steps. Then it has n - 1 steps and so on, on the final step we have 1 step in the inner loop.
so, the inner loop has:
n, n-1, ..., 1 steps in the respective iterations.
Since addition is both commutative and associative, we have:
(n + 1) + (n - 1 + 2) + ...
that is (n+1)/2 on average.
Since we worry about the scenario when n -> infinity, adding 1 to it or dividing it by 2 is less of a concern, so we roughly have n * n steps, hence it has a complexity of O(n * n), don't listen to the tool that says otherwise, unless you have some extra information about your algorithm to share with us.
I am stuck on a review question for my upcoming midterms, and any help is greatly appreciated.
Please see function below:
void george(int n) {
int m = n; //c1 - 1 step
while (m > 1) //c2 - log(n) steps
{
for (int i = 1; i < m; i++) //c3 - log(n)*<Stuck here>
int S = 1; //c4 - log(n)*<Stuck here>
m = m / 2; //c5 - (1)log(n) steps
}
}
I am stuck on the inner for loop since i is incrementing and m is being divided by 2 after every iteration.
If m = 100:
1st iteration m = 100: loop would run 100, i iterates 100 times + 1 for last check
2nd iteration m = 50: loop would run 50 times, i iterates 50 times + 1 for last check
..... and so on
Would this also be considered log(n) since m is being divided by 2?
External loop executes log(n) times
Internal loop executes n + n/2 + n/4 +..+ 1 ~ 2*n times (geometric progression sum)
Overall time is O(n + log(n)) = O(n)
Note - if we replace i < m with i < n in the inner loop, we will obtain O(n*log(n)) complexity, because in this case we have n + n + n +.. + n operations for inner loops, where number of summands is log(n)
This is the loop structure :
for (int i = 1 ; i < n ; i++) {
for (int j = 0 ; j < n ; j += i) {
// do stuff
}
}
My guess was O(nlogn) as it clearly cannot be O(n^2) since the increment in j is increasing and it clearly cannot be O(n sqrt(n)) since the increment is not that high. But I have no idea how to prove it formally.
Each time complexity of the inner loop is based on the value of i is n/i. Hence, total time would be n + n/2 + n/3 + ... + n/n = n(1+1/2+1/3+...+1/n).
As we know 1+1/2+1/3+...+1/n is a harmonic sereis and asymptotically is log(n). Hence, the algorithm is run in O(nlog(n)).
In the book Programming Interviews Exposed it says that the complexity of the program below is O(N), but I don't understand how this is possible. Can someone explain why this is?
int var = 2;
for (int i = 0; i < N; i++) {
for (int j = i+1; j < N; j *= 2) {
var += var;
}
}
You need a bit of math to see that. The inner loop iterates Θ(1 + log [N/(i+1)]) times (the 1 + is necessary since for i >= N/2, [N/(i+1)] = 1 and the logarithm is 0, yet the loop iterates once). j takes the values (i+1)*2^k until it is at least as large as N, and
(i+1)*2^k >= N <=> 2^k >= N/(i+1) <=> k >= log_2 (N/(i+1))
using mathematical division. So the update j *= 2 is called ceiling(log_2 (N/(i+1))) times and the condition is checked 1 + ceiling(log_2 (N/(i+1))) times. Thus we can write the total work
N-1 N
∑ (1 + log (N/(i+1)) = N + N*log N - ∑ log j
i=0 j=1
= N + N*log N - log N!
Now, Stirling's formula tells us
log N! = N*log N - N + O(log N)
so we find the total work done is indeed O(N).
Outer loop runs n times. Now it all depends on the inner loop.
The inner loop now is the tricky one.
Lets follow:
i=0 --> j=1 ---> log(n) iterations
...
...
i=(n/2)-1 --> j=n/2 ---> 1 iteration.
i=(n/2) --> j=(n/2)+1 --->1 iteration.
i > (n/2) ---> 1 iteration
(n/2)-1 >= i > (n/4) ---> 2 iterations
(n/4) >= i > (n/8) ---> 3 iterations
(n/8) >= i > (n/16) ---> 4 iterations
(n/16) >= i > (n/32) ---> 5 iterations
(n/2)*1 + (n/4)*2 + (n/8)*3 + (n/16)*4 + ... + [n/(2^i)]*i
N-1
n*∑ [i/(2^i)] =< 2*n
i=0
--> O(n)
#Daniel Fischer's answer is correct.
I would like to add the fact that this algorithm's exact running time is as follows:
Which means:
I have a short program here:
Given any n:
i = 0;
while (i < n) {
k = 2;
while (k < n) {
sum += a[j] * b[k]
k = k * k;
}
i++;
}
The asymptotic running time of this is O(n log log n). Why is this the case? I get that the entire program will at least run n times. But I'm not sure how to find log log n. The inner loop is depending on k * k, so it's obviously going to be less than n. And it would just be n log n if it was k / 2 each time. But how would you figure out the answer to be log log n?
For mathematical proof, inner loop can be written as:
T(n) = T(sqrt(n)) + 1
w.l.o.g assume 2 ^ 2 ^ (t-1)<= n <= 2 ^ (2 ^ t)=>
we know 2^2^t = 2^2^(t-1) * 2^2^(t-1)
T(2^2^t) = T(2^2^(t-1)) + 1=T(2^2^(t-2)) + 2 =....= T(2^2^0) + t =>
T(2^2^(t-1)) <= T(n) <= T(2^2^t) = T(2^2^0) + log log 2^2^t = O(1) + loglogn
==> O(1) + (loglogn) - 1 <= T(n) <= O(1) + loglog(n) => T(n) = Teta(loglogn).
and then total time is O(n loglogn).
Why inner loop is T(n)=T(sqrt(n)) +1?
first see when inner loop breaks, when k>n, means before that k was at least sqrt(n), or in two level before it was at most sqrt(n), so running time will be T(sqrt(n)) + 2 ≥ T(n) ≥ T(sqrt(n)) + 1.
Time Complexity of a loop is O(log log n) if the loop variables is reduced / increased exponentially by a constant amount. If the loop variable is divided / multiplied by a constant amount then complexity is O(Logn).
Eg: in your case value of k is as follow. Let i in parenthesis denote the number of times the loop has been executed.
2 (0) , 2^2 (1), 2^4 (2), 2^8 (3), 2^16(4), 2^32 (5) , 2^ 64 (6) ...... till n (k) is reached.
The value of k here will be O(log log n) which is the number of times the loop has executed.
For the sake of assumption lets assume that n is 2^64. Now log (2^64) = 64 and log 64 = log (2^6) = 6. Hence your program ran 6 times when n is 2^64.
I think if the codes are like this, it should be n*log n;
i = 0;
while (i < n) {
k = 2;
while (k < n) {
sum += a[j] * b[k]
k *= c;// c is a constant bigger than 1 and less than k;
}
i++;
}
Okay, So let's break this down first -
Given any n:
i = 0;
while (i < n) {
k = 2;
while (k < n) {
sum += a[j] * b[k]
k = k * k;
}
i++;
}
while( i<n ) will run for n+1 times but we'll round it off to n times.
now here comes the fun part, k<n will not run for n times instead it will run for log log n times because here instead of incrementing k by 1,in each loop we are incrementing it by squaring it. now this means it'll take only log log n time for the loop. you'll understand this when you learn design and analysis of algorithm
Now we combine all the time complexity and we get n.log log n time here I hope you get it now.