# Binary Heap Algorithms

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1 CS Data Structures and Algorithms Lecture Slides Wednesday, April 5, 2009 Glenn G. Chappell Department of Computer Science University of Alaska Fairbanks Glenn G. Chappell

2 Review Binary Search Trees Efficiency B.S.T. (balanced & average case) Sorted Array B.S.T. (worst case) Retrieve Logarithmic Logarithmic Insert Logarithmic Delete Logarithmic Binary Search Trees have poor worst-case performance. But they have very good performance: On average. If balanced. But we do not know an efficient way to make them stay balanced. Can we efficiently keep a Binary Search Tree balanced? 5 Apr 2009 CS Spring

3 Unit Overview Tables & Priority Queues Major Topics Introduction to Tables Priority Queues Binary Heap algorithms Heaps & Priority Queues in the C++ STL 2- Trees Other balanced search trees Hash Tables Prefix Trees Tables in various languages 5 Apr 2009 CS Spring 2009

4 Review Introduction to Tables [/4] Our ultimate value-oriented ADT is Table. Three primary operations: Retrieve (by key). Insert (key-data pair). Delete (by key). What do we use a Table for? To hold data accessed by key fields. For example: Customers accessed by phone number. Students accessed by student ID number. Any other kind of data with an ID code. To hold set data. Data in which the only question we ask is whether a key lies in the data set. To hold arrays whose indices are not nonnegative integers. arr2["hello"] = ; To hold array-like data sets that are sparse. arr[6] = ; arr[ ] = 2; 5 Apr 2009 CS Spring

5 Review Introduction to Tables [2/4] How can we implement a Table? We know several lousy ways: A Sequence holding key-data pairs. Sorted or unsorted. Array-based or Linked-List-based. A Binary Search Tree holding key-data pairs. Sorted Array Unsorted Array Sorted Linked List Unsorted Linked List Binary Search Tree Balanced** Binary Search Tree Retrieve Logarithmic Logarithmic Insert Constant (?)* Constant Logarithmic Delete Logarithmic *Amortized constant time, if we might have to reallocate. (Also, we are allowing multiple equivalent keys here. If we do not, then when we insert, we need to find any existing item with the given key, which is linear time.) **Of course, we do not (yet!) know any way to guarantee the tree will stay balanced, unless we can restrict ourselves to read-only operations (no insert, delete). 5 Apr 2009 CS Spring

6 Review Introduction to Tables [/4] In special situations, the (amortized) constant-time insertion for an unsorted array and the logarithmic-time retrieve for a sorted array can be combined! Insert all data into an unsorted array, sort the array, then use Binary Search to retrieve data. This is a good way to handle Table data with separate filling & searching phases (and few or no deletes). Note: We will be talking about some complicated Table implementations. But sometimes a simple solution is the best. 5 Apr 2009 CS Spring

7 Review Introduction to Tables [4/4] Sorted Array Unsorted Array Sorted Linked List Unsorted Linked List Binary Search Tree Balanced (how?) Binary Search Tree Retrieve Logarithmic Logarithmic Insert Constant (?) Constant Logarithmic Delete Logarithmic Idea #: Restricted Table Perhaps we can do better if we do not implement a Table in its full generality. Idea #2: Keep a Tree Balanced Balanced Binary Search Trees look good, but how to keep them balanced efficiently? Idea #: Magic Functions Use an unsorted array of key-data pairs. Allow array items to be marked as empty. Have a magic function that tells the index of an item. Retrieve/insert/delete in constant time? (Actually no, but this is still a worthwhile idea.) We will look at what results from these ideas: From idea #: Priority Queues From idea #2: Balanced search trees (2- Trees, Red-Black Trees, B-Trees, etc.) From idea #: Hash Tables 5 Apr 2009 CS Spring

8 Review Priority Queues What a Priority Queue Is A Priority Queue is a restricted-access Table. Just as a Queue is a restricted Sequence. A Priority Queue is not a Queue! The key is called the priority. We can: Retrieve (getfront) highest priority item. Insert any item (key, data). Delete highest priority item. 5 Apr 2009 CS Spring

9 Review Priority Queues Applications, Implementation A PQ is useful when we have items to process and some are more important than others. PQs can be used to do sorting. Insert all items, then retrieve/delete all items. The resulting sequence is sorted by priority. Note: Once again, a sorted container gives us a sorting algorithm. However, as with Insertion Sort, instead of using a separate container to sort with, we prefer to use an in-place version of the algorithm. We will call this Heap Sort. The most interesting thing about Priority Queues is their usual implementation: a Binary Heap. 5 Apr 2009 CS Spring

10 What is a Binary Heap? Definition We define a Binary Heap (usually just Heap) to be a complete Binary Tree that Notes Is empty, Or else The root s key (priority) is than the key of each of the root s children, if any, and Each of the root s subtrees is a Binary Heap. This is a Maxheap. If we reverse the order, so that the root s key is than the keys of its children, we get a Minheap. The text presents Heap as an ADT with essentially the same operations as a Priority Queue. I am not doing this. As we will see, a Binary Heap is a good basis for an implementation of a Priority Queue Apr 2009 CS Spring

11 What is a Binary Heap? Complete BTs (again) We discussed an array implementation for a complete Binary Tree: Put the nodes in an array, in the order in which they would be added to a complete Binary Tree. No pointers/arrows/indices are required. We store only the array of data items and the number of nodes Logical Structure Physical Structure Put the root, if any, at index 0. The left child of node k is at index 2k +. It exists if 2k + < size. The right child is similar, but at 2k + 2. The parent of node k is at index (k )/2 [int division]. It exists if k > 0. 5 Apr 2009 CS Spring 2009

12 What is a Binary Heap? Implementation The usual implementation of a Binary Heap uses this array-based complete Binary Tree Logical Structure Physical Structure There are no required order relationships between siblings. None of the standard traversals gives any sensible ordering. In practice, we usually use Heap to mean a Binary Heap using this array representation. In order to base a Priority Queue on a Heap, we need to know how to implement the operations. getfront is easy (right?). Next we look at delete & insert. 5 Apr 2009 CS Spring

13 Operations Delete [/2] In a Priority Queue, we can delete the highest-priority item. In a Maxheap, this corresponds to the root. How do we delete the root item, while maintaining the Heap properties? We cannot delete the root node (unless it is the only node). The Heap will have one less item, and so the last node must go away. But the last item is not going away. Solution: Move the last node s item to the root; delete the last node. We do this by swapping the items (which has other advantages, as we will see) Ick! But now we have another problem: This is no longer a Heap. How do we fix it? 5 Apr 2009 CS Spring 2009

14 Operations Delete [2/2] To fix: Trickle down the new root item. If the root is all its children, stop. Otherwise, swap the root item with its largest child and recursively fix the proper subtree. (Why largest?) Done Apr 2009 CS Spring

15 Operations Insert To insert into a Heap, add a new node at the end. But if we put our new value in this node, then we may not have a Heap. Solution: Trickle up. If new value is greater than its parent, swap them. Repeat at new position Done Apr 2009 CS Spring

16 Operations Using an Array Heap insert and delete are usually given a random-access range. The item to insert or delete is last item of the range; the rest is a Heap. Action of Heap insert: That s where we want to put the item, initially (right?). Given Heap Action of Heap delete: Item to insert New Heap That s where the swap puts it (right?). Given Heap New Heap Item deleted Note that Heap algorithms can do all their work using swap. This usually allows for both speed and safety. 5 Apr 2009 CS Spring

17 Efficiency What is the order of the three main Priority Queue operations, if we use a Binary Heap implementation based on a complete Binary Tree stored in an array? getfront Constant time. insert Logarithmic time. Assuming no reallocation, that is, assuming the array is large enough to hold the new item. As on the previous slide, the way that Heaps are used often guarantees that this is the case. ( time if possible reallocation.) The number of operations is roughly the height of the tree. Since the tree is balanced, the height is O(log n). delete Logarithmic time. No reallocation, of course. Other comments as for insert. We conclude that a Heap is an excellent basis for an implementation of a Priority Queue. Better than linear time! 5 Apr 2009 CS Spring

18 Write It! TO DO Write the Heap insert algorithm. Prototype is shown below. The item to be inserted is the final item in the given range. All other items should form a Heap already. Done. See heapalgs.h, on the web page. // Requirements on types: // RAIter is a random-access iterator type. template<typename RAIter> void heapinsert(raiter first, RAIter last); 5 Apr 2009 CS Spring

19 An Efficient Make Operation To turn a random-access range (array?) into a Heap, we could do n Heap inserts. Each insert operation is O(log n), and so making a Heap in this way is O(n log n). However, we can make a Heap faster than this. Place each item into a partially-made Heap, in backwards order. Trickle each item down through its descendants. For most items, there are not very many of these Bottom items: no trickling necessary This Heap make method is linear time! Apr 2009 CS Spring

20 Heap Sort Introduction Our last sorting algorithm is Heap Sort. This is a sort that uses Heap algorithms. We can think of it as using a Priority Queue, where the priority of an item is its value except that the algorithm is in-place, using no separate data structure. Procedure: Make a Heap, then delete all items, using the delete procedure that places the deleted item in the top spot. We do a make operation, which is O(n), and n getfront/delete operations, each of which is O(log n). Total: O(n log n). 5 Apr 2009 CS Spring

21 Heap Sort Properties Heap Sort can be done in-place. We can create a Heap in a given array. As each item is removed from the Heap, put it in the array element that is removed from the Heap. Starting the delete by swapping root and last items does this. Results Ascending order, if we used a Maxheap. Only constant additional memory required. Reallocation is avoided. Heap Sort uses less additional space than Introsort or array Merge Sort. Heap Sort: O(). Introsort: O(log n). Merge Sort on an array: O(n). Heap Sort also can easily be generalized. Doing Heap inserts in the middle of the sort. Stopping before the sort is completed. 5 Apr 2009 CS Spring

22 Heap Sort Illustration [/2] Below: Heap make operation. Next slide: Heap deletion phase. Start 2 4 Add Add Add 2 4 Note: This is what happens in memory. This is just a picture of the logical structure. 4 4 Add Now the entire array is a Heap Apr 2009 CS Spring

23 Heap Sort Illustration [2/2] Heap deletion phase: Delete 2 4 Start Delete Delete Delete (does nothing; can be skipped) Now the array is sorted. 5 Apr 2009 CS Spring

24 Heap Sort Analysis Efficiency Heap Sort is O(n log n). Requirements on Data Heap Sort requires random-access data. Space Usage Heap Sort is in-place. Stability Heap Sort is not stable. Performance on Nearly Sorted Data We have seen these together before (Iterative Merge Sort on a Linked List), but never for an array. Heap Sort is not significantly faster or slower for nearly sorted data. Notes Heap Sort can be generalized to handle sequences that are modified (in certain ways) in the middle of sorting. Recall that Heap Sort is used by Introsort, when the recursion depth of Quicksort exceeds the maximum allowed. 5 Apr 2009 CS Spring

25 Thoughts In practice, a Heap is not so much a data structure as it is an ordinary random-access sequence with a particular ordering property. Associated with Heaps are a collection of algorithms that allow us to efficiently create Priority Queues and do comparison sorting. These algorithms are the things to remember. Thus the subject heading. 5 Apr 2009 CS Spring

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