Let m be the size of Array A and n be the size of Array B. What is the complexity of the following while loop?
while (i<n && j<m){ if (some condition) i++ else j++}
Example for an array: A=[1,2,3,4] B=[1,2,3,4] the while loop executes at most 5+4 times O(m+n).
Example for an array: A=[1,2,3,4,7,8,9,10] B=[1,2,3,4] the while loop executes at most 4 times O(n).
I am not able to figure out how to represent the complexity of the while loop.
One common approach is to describe the worst-case time complexity. In your example, the worst-case time complexity is O(m + n), because no matter what some condition is during a given loop iteration, the total number of loop iterations is at most m + n.
If it's important to emphasize that the time complexity has a lesser upper bound in some cases, then you'll need to figure out what those cases are, and find a way to express them. (For example, if a given algorithm takes an array of size n and has worst-case O(n2) time, it might also be possible to describe it as "O(mn) time, where m is the number of distinct values in the array" — only if that's true, of course — where we've introduced an extra variable m to let us capture the impact on the performance of having more vs. fewer duplicate values.)
Related
I am a beginner, and i have this fundamental doubt...
What is the Big-O runtime of this simple algorithm ?
Is it O(n2) [ cause of two for loops ] or O(n3) [ including product of two numbers which itself should be O(n) ] ?
MaxPairwiseProductNaive(A[1 . . . n]):
product ← 0
for i from 1 to n:
for j from i + 1 to n:
product ← max(product, A[i] · A[j])
return product
Both your first and second loops are O(n), so together they are O(n^2).
Inside your loop, neither the max or multiplication depend on the number of array elements. For languages like C, C++, C#, Java, etc., getting a value from an array does not add time complexity as n increases, so we say that is O(1).
While the multiplication and max will take time, they are also O(1), because they will always run at a constant time, regardless of n. (I will note that the values here will get extremely large for arrays containing values >1 because of the max, so I'm guessing some languages might start to slow down past a certain value, but that's just speculation.)
All in all, you have
O(n):
O(n):
O(1)
So the total is O(n^2)
Verification:
As a reminder, while time complexity analysis is a vital tool, you can always measure it. I did this in C# and measured both the time and number of inner loop executions.
That trendline is just barely under executions = 0.5n^2. Which makes sense if you think about low numbers of n. You can step through your loops on a piece of paper and immediately see the pattern.
n=5: 5 outer loop iterations, 10 total inner loop iterations
n=10 10 outer loop iterations, 45 total inner loop iterations
n=20 20 outer loop iterations, 190 total inner loop iterations
For timing, we see an almost identical trend with just a different constant. This indicates that our time, T(n), is directly proportional to the number of inner loop iterations.
Takeaway:
The analysis which gave O(n^2) worked perfectly. The statements within the loop were O(1) as expected. The check didn't end up being necessary, but it's useful to see just how closely the analysis and verification match.
i have a question about O-notation. (big O)
In my code, i am using a for loop to iterate through an array of users.
The for loop has if-statements that makes it break out of the loop, if the rigth user is found.
My question is how i measure the O-notation?
Is the O-notation is O(N) as i loop through all the users in the array?
Or is the O-notation O(1), as the loop breaks and never runs again?
O notation defines an "order of" relationship between an amount of work (however measured) and the number of items processed (usually 'n'). So "O(n)" means "in direct proportion to the number of items n". "O(1)" means simply "constant". If a loop processes every item once then the amount of work is intuitively in direct proportion to n, but let's say that your exit condition gets hit on average half way through, we might be tempted to say that this is O(n/2), but instead we still say that it is O(n) because the relationship to n is still direct/linear. Similarly if you were to assess the relationship to be O(7n^3 + 2n), you'd say the relationship was simply O(n^3) because n^3 is the term that dominates as n grows large.
The answer to your specific question is therefore O(n) because the number of iterations is in direct proportion to n. All that this says is that if N user records take M milliseconds to process, 2N should take about 2M milliseconds.
It is probably worth noting that O notation is strictly concerned with worst case and not the average cost of algorithms (although I have started to find that it is quite common for people to use it in the latter sense). It is always a good idea to specify to avoid ambiguity.
Big O notation answers the following two questions:
If there are N data elements, how many steps will the algorithm take?
How will the performance of the algorithm change if the number of data elements increases?
Best-case scenario in your case is that the user you are searching for is found at the first index. Time complexity in this case would be O(1) because number of steps taken by the algorithm are constant and do not change if the number of elements in the array are changed.
The worst-case scenario is that your loop will have to iterate over all the users. That makes the time complexity to be O(N) because number of steps taken by the algorithm will be directly proportional to the number of elements in the array.
Big O notation generally refers to the worst-case scenario, so you can say that the time complexity in your case is O(N).
Best case complexity of for loop is O(1) and worst case complexity is O(N). In linear search best case is O(N) and worst case is O(N). It also depends on the approach followed by you to solve problem. Like for(int i = n; i>1; i=i/2) in this case complexity is O(log(N). Complexity of if else condition is O(1).
I'm trying to find out which is the Theta complexity of this algorithm.
(a is a list of integers)
def sttr(a):
for i in xrange(0,len(a)):
while s!=[] and a[i]>=a[s[-1]]:
s.pop()
s.append(i)
return s
On the one hand, I can say that append is being executed n (length of a array) times, so pop too and the last thing I should consider is the while condition which could be executed probably 2n times at most.
From this I can say that this algorithm is at most 4*n so it is THETA(n).
But isn't it amortised analysis?
On the other hand I can say this:
There are 2 nested cycles. The for cycle is being executed exactly n times. The while cycle could be executed at most n times since I have to remove item in each iteration. So the complexity is THETA(n*n).
I want to compute THETA but don't know which of these two options is correct. Could you give me advice?
The answer is THETA(n) and your arguments are correct.
This is not amortized analysis.
To get to amortized analysis you have to look at the inner loop. You can't easily say how fast the while will execute if you ignore the rest of the algorithm. Naive approach would be O(N) and that's correct since that's the maximum number of iterations. However, since we know that the total number of executions is O(N) (your argument) and that this will be executed N time we can say that the complexity of the inner loop is O(1) amortized.
According to Wikibooks, the Andrew's algorithm runs in linear time if all the points are already sorted. We will take the case of sorted points.
However, in the pseudo code it says:
for i = 1, 2, ..., n:
while L contains at least two points and the sequence of last two points
of L and the point P[i] does not make a counter-clockwise turn:
remove the last point from L
append P[i] to L
Now, here we can see a for loop and a while loop nested inside of the for loop. According to my logic reasoning, if there is a loop inside a loop, it simply can not have in a linear time complexity.
Where am I making the mistake?
Thanks!
EDIT: By analyzing the code, I deduced following.
for i loop--------O(n)
while loop----O(i-2) worst case
remove----O(1)
append--------O(1)
Now, if the while loop had the time complexity of O(n), the overall complexity would be O(n^2). But because it is smaller, the overall complexity should be O((i-2) * n), which I think is bigger than O(n) because i increments to n...
I am not really sure how to calculate this correctly...
Well you do have linear complexity because:
For (i=1 ... n) grants an n factor to the complexity so until now O(n)
In the nested while loop you have the condition (L size >= 2 && it will also check if you do make a counter-clockwise turn(that should be done in constant time)). So this may apear to scale the complexity to a factor of n as well(that would produce an quadratic complexity O(n*n))
But now the thing is the body of the nested while loop can be executed at most N times because there you are pop-ing elements from L; and you are not pushing elements in L except once for every i. So in the execution of the algorithm the push(append) statement will be executed exactly N times and thus the POP(remove last element) can be executed at most N times regardless the fact it is nested in an enclosing for loop. Thus, the complexity remains O(n) = linear complexity.
Below is some pseudocode I wrote that, given an array A and an integer value k, returns true if there are two different integers in A that sum to k, and returns false otherwise. I am trying to determine the time complexity of this algorithm.
I'm guessing that the complexity of this algorithm in the worst case is O(n^2). This is because the first for loop runs n times, and the for loop within this loop also runs n times. The if statement makes one comparison and returns a value if true, which are both constant time operations. The final return statement is also a constant time operation.
Am I correct in my guess? I'm new to algorithms and complexity, so please correct me if I went wrong anywhere!
Algorithm ArraySum(A, n, k)
for (i=0, i<n, i++)
for (j=i+1, j<n, j++)
if (A[i]+A[j]=k)
return true
return false
Azodious's reasoning is incorrect. The inner loop does not simply run n-1 times. Thus, you should not use (outer iterations)*(inner iterations) to compute the complexity.
The important thing to observe is, that the inner loop's runtime changes with each iteration of the outer loop.
It is correct, that the first time the loop runs, it will do n-1 iterations. But after that, the amount of iterations always decreases by one:
n - 1
n - 2
n - 3
…
2
1
We can use Gauss' trick (second formula) to sum this series to get n(n-1)/2 = (n² - n)/2. This is how many times the comparison runs in total in the worst case.
From this, we can see that the bound can not get any tighter than O(n²). As you can see, there is no need for guessing.
Note that you cannot provide a meaningful lower bound, because the algorithm may complete after any step. This implies the algorithm's best case is O(1).
Yes. In the worst case, your algorithm is O(n2).
Your algorithm is O(n2) because every instance of inputs needs time complexity O(n2).
Your algorithm is Ω(1) because there exist one instance of inputs only needs time complexity Ω(1).
Following appears in chapter 3, Growth of Function, of Introduction to Algorithms co-authored by Cormen, Leiserson, Rivest, and Stein.
When we say that the running time (no modifier) of an algorithm is Ω(g(n)), we mean that no mater what particular input of size n is chosen for each value of n, the running time on that input is at least a constant time g(n), for sufficiently large n.
Given an input in which the summation of first two elements is equal to k, this algorithm would take only one addition and one comparison before returning true.
Therefore, this input costs constant time complexity and make the running time of this algorithm Ω(1).
No matter what the input is, this algorithm would take at most n(n-1)/2 additions and n(n-1)/2 comparisons before returning value.
Therefore, the running time of this algorithm is O(n2)
In conclusion, we can say that the running time of this algorithm falls between Ω(1) and O(n2).
We could also say that worst-case running of this algorithm is Θ(n2).
You are right but let me explain a bit:
This is because the first for loop runs n times, and the for loop within this loop also runs n times.
Actually, the second loop will run for (n-i-1) times, but in terms of complexity it'll be taken as n only. (updated based on phant0m's comment)
So, in worst case scenerio, it'll run for n * (n-i-1) * 1 * 1 times. which is O(n^2).
in best case scenerio, it's run for 1 * 1 * 1 * 1 times, which is O(1) i.e. constant.