Example of Big O of 2^n

Question

So I can picture what an algorithm is that has a complexity of n^c, just the number of nested for loops.

for (var i = 0; i < dataset.len; i++ {
    for (var         


        

Answer 1

Here are two simple examples in python with Big O/Landau (2^N):

#fibonacci 
def fib(num):    
    if num==0 or num==1:
        return num
    else:
        return fib(num-1)+fib(num-2)

num=10
for i in range(0,num):
    print(fib(i))


#tower of Hanoi
def move(disk , from, to, aux):
    if disk >= 1:
        # from twoer , auxilart 
        move(disk-1, from, aux, to)
        print ("Move disk", disk, "from rod", from_rod, "to rod", to_rod)
        move(disk-1, aux, to, from)

n = 3
move(n, 'A', 'B', 'C')

Answer 2

Think about e.g. iterating over all possible subsets of a set. This kind of algorithms is used for instance for a generalized knapsack problem.

If you find it hard to understand how iterating over subsets translates to O(2^n), imagine a set of n switches, each of them corresponding to one element of a set. Now, each of the switches can be turned on or off. Think of "on" as being in the subset. Note, how many combinations are possible: 2^n.

If you want to see an example in code, it's usually easier to think about recursion here, but I can't think od any other nice and understable example right now.

Answer 3

Consider that you want to guess the PIN of a smartphone, this PIN is a 4-digit integer number. You know that the maximum number of bits to hold a 4-digit number is 14 bits, 2^14 is 16384. So, you will have to guess the value, the 14-bit correct combination let's say, of this PIN.

One way is to ask your friend who trusts you and give it to you. The other way is to brute force. So, for simplicity, consider this simple 2-bit word that you want to guess right, each bit has 2 possible values, 0 or 1. So, all the possibilities are:

00
01
10
11

We know from logic circuits design that all possibilities of an n-bit word will be 2^n possible combinations. So, 2^2 is 4 possible combinations as we saw earlier.

The same applies to the 14-bit integer PIN, so guessing the PIN would require you to solve a 2^14 possible outcome puzzle, hence an algorithm of time complexity O(2^n).

So, those types of problems, where combinations of elements in a set S differs, and you will have to try to solve the problem by trying all possible combinations, will have this O(2^n) time complexity. But, the exponentiation base does not have to be 2. In the example above it's of base 2 because each element, each bit, has two possible values which will not be the case in other problems.

Another good example of O(2^n) algorithms is the recursive knapsack. Where you have to try different combinations to maximize the value, where each element in the set, has two possible values, whether we take it or not.

The Edit Distance problem is an O(3^n) time complexity since you have 3 decisions to choose from for each of the n characters string, deletion, insertion, or replace.

Answer 4

Algorithms with running time O(2^N) are often recursive algorithms that solve a problem of size N by recursively solving two smaller problems of size N-1.

This program, for instance prints out all the moves necessary to solve the famous "Towers of Hanoi" problem for N disks in pseudo-code

void solve_hanoi(int N, string from_peg, string to_peg, string spare_peg)
{
    if (N<1) {
        return;
    }
    if (N>1) {
        solve_hanoi(N-1, from_peg, spare_peg, to_peg);
    }
    print "move from " + from_peg + " to " + to_peg;
    if (N>1) {
        solve_hanoi(N-1, spare_peg, to_peg, from_peg);
    }
}

Let T(N) be the time it takes for N disks.

We have:

T(1) = O(1)
and
T(N) = O(1) + 2*T(N-1) when N>1

If you repeatedly expand the last term, you get:

T(N) = 3*O(1) + 4*T(N-2)
T(N) = 7*O(1) + 8*T(N-3)
...
T(N) = (2^(N-1)-1)*O(1) + (2^(N-1))*T(1)
T(N) = (2^N - 1)*O(1)
T(N) = O(2^N)

To actually figure this out, you just have to know that certain patterns in the recurrence relation lead to exponential results. Generally T(N) = ... + C*T(N-1) with C > 1means O(x^N). See:

https://en.wikipedia.org/wiki/Recurrence_relation

Answer 5

Here is a code clip that computes value sum of every combination of values in a goods array(and value is a global array variable):

fun boom(idx: Int, pre: Int, include: Boolean) {
    if (idx < 0) return
    boom(idx - 1, pre + if (include) values[idx] else 0, true)
    boom(idx - 1, pre + if (include) values[idx] else 0, false)
    println(pre + if (include) values[idx] else 0)
}

As you can see, it's recursive. We can inset loops to get Polynomial complexity, and using recursive to get Exponential complexity.

Answer 6

  int Fibonacci(int number)
 {
  if (number <= 1) return number;

  return Fibonacci(number - 2) + Fibonacci(number - 1);
 }

Growth doubles with each additon to the input data set. The growth curve of an O(2N) function is exponential - starting off very shallow, then rising meteorically. My example of big O(2^n), but much better is this:

public void solve(int n, String start, String auxiliary, String end) {
   if (n == 1) {
       System.out.println(start + " -> " + end);
   } else {
       solve(n - 1, start, end, auxiliary);
       System.out.println(start + " -> " + end);
       solve(n - 1, auxiliary, start, end);
   }

In this method program prints all moves to solve "Tower of Hanoi" problem. Both examples are using recursive to solve problem and had big O(2^n) running time.