Dragon 1: You may ask us one question, then you must guess which dragon is which

Dragon 2: He’s lying. You may get three questions

Dragon 3: Oh no. It’s definitely one question

]]>arr is of length n

Right rotate array by k elements

Time complexity O(n) and space complexity O(1)

Sample Input:

arr = {1,2 ,3,4,5}

n = 5

k = 2

Output :

arr = {4,5,1,2,3}

(1961) ]]>

Answer: The easiest way to do this would be to start from the first floor and drop the egg. If it doesn’t break, move on to the next floor. If it does break, then we know the maximum floor the egg will survive is 0. If we continue this process, we will easily find out the maximum floors the egg will survive with just one egg. So the maximum number of tries is 100 that is when the egg survives even at the 100th floor.

Can we do better? Of course we can. Let’s start at the second floor. If the egg breaks, then we can use the second egg to go back to the first floor and try again. If it does not break, then we can go ahead and try on the 4th floor (in multiples of 2). If it ever breaks, say at floor x, then we know it survived floor x-2. That leaves us with just floor x-1 to try with the second egg. So what is the maximum number of tries possible? It occurs when the egg survives 98 or 99 floors. It will take 50 tries to reach floor 100 and one more egg to try on the 99th floor so the total is 51 tries. Wow, that is almost half of what we had last time.

Can we do even better? Yes we can (Bob, the builder). What if we try at intervals of 3? Applying the same logic as the previous case, we need a max of 35 tries to find out the information (33 tries to reach 99th floor and 2 more on 97th and 98th floor).

Interval – Maximum tries

1 – 100

2 – 51

3 – 35

4 – 29

5 – 25

6 – 21

7 – 20

8 – 19

9 – 19

10 – 19

11 – 19

12 – 19

13 – 19

14 – 20

15 – 20

16 – 21

So picking any one of the intervals with 19 maximum tries would be fine.

Update: Thanks to RiderOfGiraffes for this solution.

Instead of taking equal intervals, we can increase the number of floors by one less than the previous increment. For example, let’s first try at floor 14. If it breaks, then we need 13 more tries to find the solution. If it doesn’t break, then we should try floor 27 (14 + 13). If it breaks, we need 12 more tries to find the solution. So the initial 2 tries plus the additional 12 tries would still be 14 tries in total. If it doesn’t break, we can try 39 (27 + 12) and so on. Using 14 as the initial floor, we can reach up to floor 105 (14 + 13 + 12 + … + 1) before we need more than 14 tries. Since we only need to cover 100 floors, 14 tries is sufficient to find the solution.

Therefore, 14 is the least number of tries to find out the solution.

]]>Don’t just use pop() to “dig” through your stack to find the max—do something that lets you return the max in constant time.

Solution

We could have an instance variable where we hold the max, but there’s a problem—when we pop that item from our stack it’s no longer the max. Now we have to “dig” through our stack to find the new max. Ideally we’d keep track of the current max as well as what the new max will be when that max is popped.

The trick is to have two instances of Stack inside our MaxStack. One holds the actual stack contents, while the other (call it maxesStack) holds the maxes. Whenever we push() an item, if it’s larger than the top item in maxesStack, we also push it to maxesStack. Whenever we pop() an item, if it’s the same as the top item in maxesStack(), we also pop() it from maxesStack.

So at any given point we can get the overall max in constant time be peeking at the top item in maxesStack.

]]>]]>“How many unique areas of human knowledge have the right size of passionate users to make it as a Stack Exchange site?”