Can anybody help me find time conplexity of this recursive function?
int test(int m, int n) {
if(n == 0)
return m;
else
return (3 + test(m + n, n - 1));
The test(m+n, n-1) is called n-1 times before base case which is if (n==0), so complexity is O(n)
Also, this is a duplicate of Determining complexity for recursive functions (Big O notation)
It is really important to understand recursion and the time-complexity of recursive functions.
The first step to understand easy recursive functions like that is to be able to write the same function in an iterative way. This is not always easy and not always reasonable but in case of an easy function like yours this shouldn't be a problem.
So what happens in your function in every recursive call?
is n > 0?
If yes:
m = m + n + 3
n = n - 1
If no:
return m
Now it should be pretty easy to come up with the following (iterative) alternative:
int testIterative(int m, int n) {
while(n != 0) {
m = m + n + 3;
n = n - 1;
}
return m;
}
Please note: You should pay attention to negative n. Do you understand what's the problem here?
Time complexity
After looking at the iterative version, it is easy to see that the time-complexity is depending on n: The time-complexity therefore is O(n).
Related
int silly(int n, int m) {
if (n < 1) return m;
else if (n < 10)
return silly(n/2, m);
else
return silly(n - 2, m);
}
Is this algorithm O(log n) or O(n) in terms of Big-Oh notation?
If the optimizer is really good it's O(1). The code is equivalent to simply return m.
Taking it as given, we can discount the if( n < 10 ) condition because that's a few iterations when n is small. We're looking for worst case when n is large.
That just leaves recursing into silly(n - 2, m) which is counting down every other integer. That's n/2 operations. We drop the constant making it O(n).
I want to know the time complexity of the code attached.
I get O(n^2logn), while my friends get O(nlogn) and O(n^2).
SomeMethod() = log n
Here is the code:
j = i**2;
for (k = 0; k < j; k++) {
for (p = 0; p < j; p++) {
x += p;
}
someMethod();
}
The question is not very clear about the variable N and the statement i**2.
i**2 gives a compilation error in java.
assuming someMethod() takes log N time(as mentioned in question), and completely ignoring value of N,
lets call i**2 as Z
someMethod(); runs Z times. and time complexity of the method is log N so that becomes:
Z * log N ----------------------------------------- A
lets call this expression A.
Now, x+=p runs Z^2 times (i loop * j loop) and takes constant time to run. that makes the following expression:
( Z^2 ) * 1 = ( Z^2 ) ---------------------- B
lets call this expression B.
The total run time is sum of expression A and expression B. which brings us to:
O((Z * log N) + (Z^2))
where Z = i**2
so final expression will be O(((i**2) * log N) + ((i**2)^2))
if we can assume i**2 is i^2, the expression becomes,
O(((i^2) * log N) + (i^4))
Considering only the higher order variables, like we consider n^2 in n^2 + 2n + 5, the complexity can be expressed as follows,
i^4
Based on the picture, the complexity is O(logNI2 + I4).
We cannot give a complexity class with one variable because the picture does not explain the relationship between N and I. They must be treated as separate variables.
And likewise, we cannot eliminate the logNI2 term because the N variable will dominate the I variable in some regions of N x I space.
If we treat N as a constant, then the complexity class reduces to O(I4).
We get the same if we treat N as being the same thing as I; i.e. there is a typo in the question.
(I think there is mistake in the way the question was set / phrased. If not, this is a trick question designed to see if you really understood the mathematical principles behind complexity involving multiple independent variables.)
I am trying to calculate the time complexity of this function
Code
int Almacen::poner_items(id_sala s, id_producto p, int cantidad){
it_prod r = productos.find(p);
if(r != productos.end()) {
int n = salas[s - 1].size();
int m = salas[s - 1][0].size();
for(int i = n - 1; i >= 0 && cantidad > 0; --i) {
for(int j = 0; j < m && cantidad > 0; ++j) {
if(salas[s - 1][i][j] == "NULL") {
salas[s - 1][i][j] = p;
r->second += 1;
--cantidad;
}
}
}
}
else {
displayError();
return -1;
}
return cantidad;
}
the variable productos is a std::map and its find method has a time complexity of Olog(n) and other variable salas is a std::vector.
I calculated the time and I found that it was log(n) + nm but am not sure if it is the correct expression or I should leave it as nm because it is the worst or if I whould use n² only.
Thanks
The overall function is O(nm). Big-O notation is all about "in the limit of large values" (and ignores constant factors). "Small" overheads (like an O(log n) lookup, or even an O(n log n) sort) are ignored.
Actually, the O(n log n) sort case is a bit more complex. If you expect m to be typically the same sort of size as n, then O(nm + nlogn) == O(nm), if you expect n ≫ m, then O(nm + nlogn) == O(nlogn).
Incidentally, this is not a question about C++.
In general when using big O notation, you only leave the most dominant term when taking all variables to infinity.
n by itself is much larger than log n at infinity, so even without m you can (and generally should) drop the log n term, so O(nm) looks fine to me.
In non-theoretical use cases, it is sometimes important to understand the actual complexity (for non-infinite inputs), since sometimes algorithms that are slow at infinity can produce better results for shorter inputs (there are some examples where O(1) algorithms have such a terrible constant that an exponential algorithm does better in real life). quick sort is considered a practical example of an O(n^2) algorithm that often does better than it's O(n log n) counterparts.
Read about "Big O Notation" for more info.
let
k = productos.size()
n = salas[s - 1].size()
m = salas[s - 1][0].size()
your algorithm is O(log(k) + nm). You need to use a distinct name for each independent variable
Now it might be the case that there is a relation between k, n, m and you can re-label with a reduced set of variables, but that is not discernible from your code, you need to know about the data.
It may also be the case that some of these terms won't grow large, in which case they are actually constants, i.e. O(1).
E.g. you may know k << n, k << m and n ~= m , which allows you describe it as O(n^2)
Assuming do_something is O(1), how do I calculate the time complexity of this function?
function run(n) {
for(var i = 1; i <= n; i++) {
var counter = 1;
while(counter <= n) {
for (var j = counter; j >= 1; j--) {
do_something();
}
counter = counter * 2;
}
}
}
I'm assuming the initial for loop means the that the complexity will be n and the inner while loop means log(n). Is this true?
How do I calculate the complexity of everything?
Thanks.
It's not trivial to calculate the complexity of everything because there are functions (e. g. collatz conjecture) where no exact analysis is known. In such a case you can do an empirical analysis where you guess which run time class would match best.
Related to your sample:
The outer for loop is called n times
The while loop is called log(n) times
The inner for loop is called counter times where counter grows 2^m. But counter is recalculated log(n) times
That means the inner exponential for loop (2^m) is called m=log(n) times.
So you have 2^(log n) = n log(2) = n
You can also do it vice versa. The inner exponential function is called log times. Means log(2^m) -> (2^n = 2^m) -> m = n.
You can also simply create a sum of 2^m from m=0 to m=log(n) which is 2n-1 -> O(n).
Then you have to multiply linear time of outer for loop with linear time of while loop (incl. inner for loop). So you have O(n*n) = O(n²) time.
The exact amount of steps can be calculated.
The outer loop runs n times
The while and inner-most loop is run counter times for each of the values of counter, these are 1, 2, 4, 8, ..., 2^(bitLength(n)-1)
which sums to 2^bitLength(n) - 1
where bitLength(n) = trunc(ln(n)/ln(2))+1
Multiplying these two, yields the exact amount of steps:
n * (2^bitLength(n) - 1)
The approximate complexity is O(n2), because O(2^bitLength(n) - 1) = O(n)
For this problem, we are given a function f which calculates the nth fibonacci number in the standard way:
f(n)
{
if(n == 0)
return 0;
if(n == 1)
return 1;
else return f(n-1) + f(n-2);
}
I am then tasked with calculating the number of arithmetic operations performed by the function on an input n. I know that each call to f performs 3 operations (2 minus, 1 addition) and I've then attempted to find it by expanding the function to try and find some sort of recurrence relation and then working out how many arithmetic operations get done. But then I get stuck because this doesn't come out as the right answer.
The correct answer is f does 3f(n+1)-3 arithmetic operations on an input n. Please could someone explain this to me? It is important as we are later asked to find the time complexity using this as a starting point.
Thanks very much,
Niamh
Time complexity of your algorithm is close to 2^(n) . Hence it is O( 2^n )
Here is an example of how your algorithm will solve if n = 6
You algorithm does T(n) = T(n-1) + T(n-2) + O(1) .
T(n-1) = T(n-2) + T(n-3) + O(1)
T(n-2) = T(n-3) + T(n-4) + O(1)
.
.
.
T(2) = T(1) + T(0) + O(1)
Have a look at Fibonacci time complexity
T(n) = T(n-1) + T(n-2) + O(1) which is equal to
T(n) = O(2^(n-1) ) + O(2^(n-2) )+ O(1) = O(2^n)
perhaps n/2 times additon operations performed ;
for(i=3;i<=n;i++)
let n=4;
variable a,b=0,c=1;
[a=b+c;b=c;c=a;].........(1) //now a=1;b=1;c=1;
[a=b+c,b=c,c=a)...............(2) //now a=2;b=1;c=2;
output:0112.
Note:if n is Odd No then addition operation performed (n/2)+1 times;
Note:if n is Even No then addition operation performed (n/2) times;
!Please correct me if i am wrong!