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Home Heap Priority Queue Implementations  
Priority Queue Implementations
In Chapter 20 we looked at an implementation of a Priority Queue based on an array. The items in the array are unsorted, so it is easy to add a new item (at the end), but harder to remove an item, because we have to search for the item with the highest priority. An alternative is an implementation based on a sorted list. In this case when we insert a new item we traverse the list and put the new item in the right spot. This implementation takes advantage of a property of lists, which is that it is easy to insert a new node into the middle. Similarly, removing the item with the highest priority is easy, provided that we keep it at the beginning of the list. Performance analysis of these operations is straightforward. Adding an item to the end of an array or removing a node from the beginning of a list takes the same amount of time regardless of the number of items. So both operations are constant time. Any time we traverse an array or list, performing a constanttime operation on each element, the run time is proportional to the number of items. Thus, removing something from the array and adding something to the list are both linear time. So how long does it take to insert and then remove n items from a Priority Queue? For the array implementation, n insertions takes time proportional to n, but the removals take longer. The first removal has to traverse all n items; the second has to traverse n1, and so on, until the last removal, which only has to look at 1 item. Thus, the total time is 1 + 2 + ··· + n, which is (still) n^{2}/2  n/2. So the total for the insertions and the removals is the sum of a linear function and a quadratic function. The leading term of the result is quadratic. The analysis of the list implementation is similar. The first insertion doesn't require any traversal, but after that we have to traverse at least part of the list each time we insert a new item. In general we don't know how much of the list we will have to traverse, since it depends on the data and what order they are inserted, but we can assume that on average we have to traverse half of the list. Unfortunately, even traversing half of the list is still a linear operation. So, once again, to insert and remove n items takes time proportional to n^{2}. Thus, based on this analysis we cannot say which implementation is better; both the array and the list yield quadratic run times. If we implement a Priority Queue using a heap, we can perform both insertions and removals in time proportional to log n. Thus the total time for n items is n log n, which is better than n^{2}. That's why, at the beginning of the chapter, I said that a heap is a particularly efficient implementation of a Priority Queue.


Home Heap Priority Queue Implementations 