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Implement a queue with 2 stacks. Your queue should have an enqueue and a dequeue method and it should be "first in first out" (FIFO).
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Optimize for the time cost of m calls on your queue. These can be any mix of enqueue and dequeue calls.

Assume you already have a stack implementation and it gives time push and pop.

We can get runtime for m calls. Crazy, right?

Let's call our stacks stack1 and stack2.

To start, we could just push items onto stack1 as they are enqueued. So if our first 3 calls are enqueues of a, b, and c (in that order) we push them onto stack1 as they come in.

But recall that stacks are last in, first out. If our next call was a dequeue we would need to return a, but it would be on the bottom of the stack.

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Look at what happens when we pop c, b, and a one-by-one from stack1 to stack2.

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Notice how their order is reversed.

We can pop each item 1-by-1 from stack1 to stack2 until we get to a.

We could return a immediately, but what if our next operation was to enqueue a new item d? Where would we put d? d should get dequeued after c, so it makes sense to put them next to each-other . . . but c is at the bottom of stack2.

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Let's try moving the other items back onto stack1 before returning. This will restore the ordering from before the dequeue, with a now gone. So if we enqueue d next, it ends up on top of c, which seems right.

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So we're basically storing everything in stack1, using stack2 only for temporarily "flipping" all of our items during a dequeue to get the bottom (oldest) element.

This is a complete solution. But we can do better.

What's our time complexity for m operations? At any given point we have items inside our data structure, and if we dequeue we have to move all of them from stack1 to stack2 and back again. One dequeue operation thus costs . The number of dequeues is , so our worst-case runtime for these m operations is .

Not convinced we can have dequeues and also have each one deal with items in the data structure? What if our first .5m operations are enqueues, and the second .5m are alternating enqueues and dequeues. For each of our .25m dequeues, we have .5m items in the data structure.

We can do better than this runtime.

What if we didn't move things back to stack1 after putting them on stack2?

Let's call our stacks inStack and outStack.

**For enqueue**, we simply push the enqueued item onto inStack.

**For dequeue on an empty outStack**, the oldest item is at the bottom of inStack. So we dig to the bottom of inStack by pushing each item one-by-one onto outStack until we reach the bottom item, which we return.

After moving everything from inStack to outStack, the item that was enqueued the *2nd* longest ago (after the item we just returned) is at the top of outStack, the item enqueued *3rd* longest ago is just below it, etc. **So to dequeue on a non-empty outStack**, we simply return the top item from outStack.

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With that description in mind, let's write some code!

class QueueTwoStacks
{
private:
stack<int> inStack_;
stack<int> outStack_;
public:
void enqueue(int item)
{
inStack_.push(item);
}
int dequeue()
{
if (outStack_.empty()) {
// Move items from inStack to outStack, reversing order
while (!inStack_.empty()) {
int newestInStackItem = inStack_.top();
inStack_.pop();
outStack_.push(newestInStackItem);
}
// If outStack is still empty, raise an error
if (outStack_.empty()) {
throw runtime_error("Can't dequeue from empty queue!");
}
}
int result = outStack_.top();
outStack_.pop();
return result;
}
};

Each enqueue is clearly time, and so is each dequeue when outStack_ has items. Dequeue on an empty outStack_ is order of the number of items in inStack_ at that moment, which can vary significantly.

**Notice that the more expensive a dequeue on an empty outStack_ is** (that is, the more items we have to move from inStack_ to outStack_), **the more -time dequeues off of a non-empty outStack_ it wins us in the future**. Once items are moved from inStack_ to outStack_ they just sit there, ready to be dequeued in O(1) time. An item never moves "backwards" in our data structure.

We might guess that this "averages out" so that in a set of m enqueues and dequeues the total cost of all dequeues is actually just . To check this rigorously, we can use the accounting method, **counting the time cost per item instead of per enqueue or dequeue**.

So let's look at the worst case for a single item, which is the case where it is enqueued and then later dequeued. In this case, the item enters inStack_ (costing 1 push), then later moves to outStack_ (costing 1 pop and 1 push), then later comes off outStack_ to get returned (costing 1 pop).

Each of these 4 pushes and pops is time. **So our total cost per item is **. Our m enqueue and dequeue operations put m or fewer items into the system, giving a total runtime of .

People often struggle with the runtime analysis for this one. The trick is to think of the cost *per item passing through our queue*, rather than the cost per enqueue() and dequeue().

This trick generally comes in handy when you're looking at the time cost of not just one call, but "m" calls.

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