I'm sorry for the question title but I can't find a simpler way to put it. Basically, my algorithm involves quicksort for O(nm/k) elements, where k ranges from 8 to m. I wonder what the total complexity for this is, and how to deduce it? Thank you!
Drop the division inside the logarithms and we get nmlog(mn) * (1/8 + ... + 1/m) = O(nmlog(mn)log(m)) = O(mnlog(m)^2 + mnlog(m)log(n)). [I used the fact that the harmonic series is asymptotically ln(m))
Note that the fact that we dropped the divisions inside the logarithms means that we got an upper bound rather than an exact bound (but a better one than the naive approach of taking the biggest term multiplied by m).
Related
for(i=1;i<=n;i=i*2)
{
for(j=1;j<=i;j++)
{
}
}
How the complexity of the following code is O(nlogn) ?
Time complexity in terms of what? If you want to know how many inner loop operations the algorithm performs, it is not O(n log n). If you want to take into account also the arithmetic operations, then see further below. If you literally are to plug in that code into a programming language, chances are the compiler will notice that your code does nothing and optimise the loop away, resulting in constant O(1) time complexity. But only based on what you've given us, I would interpret it as time complexity in terms of whatever might be inside the inner loop, not counting arithmetic operations of the loops themselves. If so:
Consider an iteration of your inner loop a constant-time operation, then we just need to count how many iterations the inner loop will make.
You will find that it will make
1 + 2 + 4 + 8 + ... + n
iterations, if n is a square number. If it is not square, it will stop a bit sooner, but this will be our upper limit.
We can write this more generally as
the sum of 2i where i ranges from 0 to log2n.
Now, if you do the math, e.g. using the formula for geometric sums, you will find that this sum equals
2n - 1.
So we have a time complexity of O(2n - 1) = O(n), if we don't take the arithmetic operations of the loops into account.
If you wish to verify this experimentally, the best way is to write code that counts how many times the inner loop runs. In javascript, you could write it like this:
function f(n) {
let c = 0;
for(i=1;i<=n;i=i*2) {
for(j=1;j<=i;j++) {
++c;
}
}
console.log(c);
}
f(2);
f(4);
f(32);
f(1024);
f(1 << 20);
If you do want to take the arithmetic operations into account, then it depends a bit on your assumptions but you can indeed get some logarithmic coefficients to account for. It depends on how you formulate the question and how you define an operation.
First, we need to estimate number of high-level operations executed for different n. In this case the inner loop is an operation that you want to count, if I understood the question right.
If it is difficult, you may automate it. I used Matlab for example code since there was no tag for specific language. Testing code will look like this:
% Reasonable amount of input elements placed in array, change it to fit your needs
x = 1:1:100;
% Plot linear function
plot(x,x,'DisplayName','O(n)', 'LineWidth', 2);
hold on;
% Plot n*log(n) function
plot(x, x.*log(x), 'DisplayName','O(nln(n))','LineWidth', 2);
hold on;
% Apply our function to each element of x
measured = arrayfun(#(v) test(v),x);
% Plot number of high level operations performed by our function for each element of x
plot(x,measured, 'DisplayName','Measured','LineWidth', 2);
legend
% Our function
function k = test(n)
% Counter for operations
k = 0;
% Outer loop, same as for(i=1;i<=n;i=i*2)
i = 1;
while i < n
% Inner loop
for j=1:1:i
% Count operations
k=k+1;
end
i = i*2;
end
end
And the result will look like
Our complexity is worse than linear but not worse than O(nlogn), so we choose O(nlogn) as an upper bound.
Furthermore the upper bound should be:
O(n*log2(n))
The worst case is n being in 2^x. x€real numbers
The inner loop is evaluated n times, the outer loop log2 (logarithm basis 2) times.
I am trying to rank these functions — 2n, n100, (n + 1)2, n·lg(n), 100n, n!, lg(n), and n99 + n98 — so that each function is the big-O of the next function, but I do not know a method of determining if one function is the big-O of another. I'd really appreciate if someone could explain how I would go about doing this.
Assuming you have some programming background. Say you have below code:
void SomeMethod(int x)
{
for(int i = 0; i< x; i++)
{
// Do Some Work
}
}
Notice that the loop runs for x iterations. Generalizing, we say that you will get the solution after N iterations (where N will be the value of x ex: number of items in array/input etc).
so This type of implementation/algorithm is said to have Time Complexity of Order of N written as O(n)
Similarly, a Nested For (2 Loops) is O(n-squared) => O(n^2)
If you have Binary decisions made and you reduce possibilities into halves and pick only one half for solution. Then complexity is O(log n)
Found this link to be interesting.
For: Himanshu
While the Link explains how log(base2)N complexity comes into picture very well, Lets me put the same in my words.
Suppose you have a Pre-Sorted List like:
1,2,3,4,5,6,7,8,9,10
Now, you have been asked to Find whether 10 exists in the list. The first solution that comes to mind is Loop through the list and Find it. Which means O(n). Can it be made better?
Approach 1:
As we know that List of already sorted in ascending order So:
Break list at center (say at 5).
Compare the value of Center (5) with the Search Value (10).
If Center Value == Search Value => Item Found
If Center < Search Value => Do above steps for Right Half of the List
If Center > Search Value => Do above steps for Left Half of the List
For this simple example we will find 10 after doing 3 or 4 breaks (at: 5 then 8 then 9) (depending on how you implement)
That means For N = 10 Items - Search time was 3 (or 4). Putting some mathematics over here;
2^3 + 2 = 10 for simplicity sake lets say
2^3 = 10 (nearly equals --- this is just to do simple Logarithms base 2)
This can be re-written as:
Log-Base-2 10 = 3 (again nearly)
We know 10 was number of items & 3 was the number of breaks/lookup we had to do to find item. It Becomes
log N = K
That is the Complexity of the alogorithm above. O(log N)
Generally when a loop is nested we multiply the values as O(outerloop max value * innerloop max value) n so on. egfor (i to n){ for(j to k){}} here meaning if youll say for i=1 j=1 to k i.e. 1 * k next i=2,j=1 to k so i.e. the O(max(i)*max(j)) implies O(n*k).. Further, if you want to find order you need to recall basic operations with logarithmic usage like O(n+n(addition)) <O(n*n(multiplication)) for log it minimizes the value in it saying O(log n) <O(n) <O(n+n(addition)) <O(n*n(multiplication)) and so on. By this way you can acheive with other functions as well.
Approach should be better first generalised the equation for calculating time complexity. liken! =n*(n-1)*(n-2)*..n-(n-1)so somewhere O(nk) would be generalised formated worst case complexity like this way you can compare if k=2 then O(nk) =O(n*n)
Suppose we have an algorithm that is of order O(2^n). Furthermore, suppose we multiplied the input size n by 2 so now we have an input of size 2n. How is the time affected? Do we look at the problem as if the original time was 2^n and now it became 2^(2n) so the answer would be that the new time is the power of 2 of the previous time?
Big 0 is not for telling you the actual running time, just how the running time is affected by the size of input. If you double the size of input the complexity is still O(2^n), n is just bigger.
number of elements(n) units of work
1 1
2 4
3 8
4 16
5 32
... ...
10 1024
20 1048576
There's a misunderstanding here about how Big-O relates to execution time.
Consider the following formulas which define execution time:
f1(n) = 2^n + 5000n^2 + 12300
f2(n) = (500 * 2^n) + 6
f3(n) = 500n^2 + 25000n + 456000
f4(n) = 400000000
Each of these functions are O(2^n); that is, they can each be shown to be less than M * 2^n for an arbitrary M and starting n0 value. But obviously, the change in execution time you notice for doubling the size from n1 to 2 * n1 will vary wildly between them (not at all in the case of f4(n)). You cannot use Big-O analysis to determine effects on execution time. It only defines an upper boundary on the execution time (which is not even guaranteed to be the minimum form of the upper bound).
Some related academia below:
There are three notable bounding functions in this category:
O(f(n)): Big-O - This defines a upper-bound.
Ω(f(n)): Big-Omega - This defines a lower-bound.
Θ(f(n)): Big-Theta - This defines a tight-bound.
A given time function f(n) is Θ(g(n)) only if it is also Ω(g(n)) and O(g(n)) (that is, both upper and lower bounded).
You are dealing with Big-O, which is the usual "entry point" to the discussion; we will neglect the other two entirely.
Consider the definition from Wikipedia:
Let f and g be two functions defined on some subset of the real numbers. One writes:
f(x)=O(g(x)) as x tends to infinity
if and only if there is a positive constant M such that for all sufficiently large values of x, the absolute value of f(x) is at most M multiplied by the absolute value of g(x). That is, f(x) = O(g(x)) if and only if there exists a positive real number M and a real number x0 such that
|f(x)| <= M|g(x)| for all x > x0
Going from here, assume we have f1(n) = 2^n. If we were to compare that to f2(n) = 2^(2n) = 4^n, how would f1(n) and f2(n) relate to each other in Big-O terms?
Is 2^n <= M * 4^n for some arbitrary M and n0 value? Of course! Using M = 1 and n0 = 1, it is true. Thus, 2^n is upper-bounded by O(4^n).
Is 4^n <= M * 2^n for some arbitrary M and n0 value? This is where you run into problems... for no constant value of M can you make 2^n grow faster than 4^n as n gets arbitrarily large. Thus, 4^n is not upper-bounded by O(2^n).
See comments for further explanations, but indeed, this is just an example I came up with to help you grasp Big-O concept. That is not the actual algorithmic meaning.
Suppose you have an array, arr = [1, 2, 3, 4, 5].
An example of a O(1) operation would be directly access an index, such as arr[0] or arr[2].
An example of a O(n) operation would be a loop that could iterate through all your array, such as for elem in arr:.
n would be the size of your array. If your array is twice as big as the original array, n would also be twice as big. That's how variables work.
See Big-O Cheat Sheet for complementary informations.
I just wanted to make sure I'm going in the right direction. I want to find a max value of an array by recursively splitting it and find the max of each separate array. Because I am splitting it, it would be 2*T(n/2). And because I have to make a comparison at the end for the 2 arrays, I have T(1).
So would my recurrence relation be like this:
T = { 2*T(n/2) + 1, when n>=2 ;T(1), when n = 1;
and and therefore my complexity would be Theta(nlgn)?
The formula you composed seems about right, but your analysis isn't perfect.
T = 2*T(n/2) + 1 = 2*(2*T(n/4) + 1) + 1 = ...
For the i-th iteration you'll get:
Ti(n) = 2^i*T(n/2^i) + i
now what you want to know for which i does n/2^i equals 1 (or just about any constant, if you like) so you reach the end-condition of n=1.
That would be the solution to n/2^I = 1 -> I = Log2(n). Plant it in the equation for Ti and you get:
TI(n) = 2^log2(n)*T(n/2^log2(n)) + log2(n) = n*1+log2(n) = n + log2(n)
and you get T(n) = O(n + log2(n) (just like #bdares said) = O(n) (just like #bdares said)
No, no... you are taking O(1) time for each recursion.
How many are there?
There are N leaves, so you know it's at least O(N).
How many do you need to compare to find the absolute maximum? That's O(log(N)).
Add them together, don't multiply. O(N+log(N)) is your time complexity.
I've got a homework question that's been puzzling me. It asks that you prove that the function Sum[log(i)*i^3, {i, n}) (ie. the sum of log(i)*i^3 from i=1 to n) is big-theta (log(n)*n^4).
I know that Sum[i^3, {i, n}] is ( (n(n+1))/2 )^2 and that Sum[log(i), {i, n}) is log(n!), but I'm not sure if 1) I can treat these two separately since they're part of the same product inside the sum, and 2) how to start getting this into a form that will help me with the proof.
Any help would be really appreciated. Thanks!
The series looks like this - log 1 + log 2 * 2^3 + log 3 * 3^3....(upto n terms)
the sum of which does not converge. So if we integrate it
Integral to (1 to infinity) [ logn * n^3] (integration by parts)
you will get 1/4*logn * n^4 - 1/16* (n^4)
It is clear that the dominating term there is logn*n^4, therefore it belongs to Big Theta(log n * n^4)
The other way you could look at it is -
The series looks like log 1 + log2 * 8 + log 3 * 27......+ log n * n^3.
You could think of log n as the term with the highest value, since all logarithmic functions grow at the same rate asymptotically,
You could treat the above series as log n (1 + 2^3 + 3^3...) which is
log n [n^2 ( n + 1)^2]/4
Assuming f(n) = log n * n^4
g(n) = log n [n^2 ( n + 1)^2]/4
You could show that lim (n tends to inf) for f(n)/g(n) will be a constant [applying L'Hopital's rule]
That's another way to prove that the function g(n) belongs to Big Theta (f(n)).
Hope that helps.
Hint for one part of your solution: how large is the sum of the last two summands of your left sum?
Hint for the second part: If you divide your left side (the sum) by the right side, how many summands to you get? How large is the largest one?
Hint for the first part again: Find a simple lower estimate for the sum from n/2 to n in your first expression.
Try BigO limit definition and use calculus.
For calculus you might like to use some Computer Algebra System.
In following answer, I've shown, how to do this with Maxima Opensource CAS :
Asymptotic Complexity of Logarithms and Powers