# 25 - 16:36:27 CS 61B Lecture 25 Monday Todays reading 1 25...

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10/25/10 16:36:27 1 25 CS 61B: Lecture 25 Monday, October 25, 2010 Today’s reading: Goodrich & Tamassia, Sections 8.1-8.3. PRIORITY QUEUES =============== A priority queue, like a dictionary, contains _entries_ that each consist of a key and an associated value. However, whereas a dictionary is used when we want to be able to look up arbitrary keys, a priority queue is used to prioritize entries. Define a total order on the keys (e.g. alphabetical order). You may identify or remove the entry whose key is the lowest (but no other entry). This limitation helps to make priority queues fast. However, an entry with any key may be inserted at any time. For concreteness, let’s use Integer objects as our keys. The main operations: - insert() adds an entry to the priority queue; - min() returns the entry with the minimum key; and - removeMin() both removes and returns the entry with the minimum key. 5 --------- | --------- --------- |4: womp| v |4: womp| | | |7: gong|-insert(k, v)->|7: gong|-removeMin()->|7: gong|-min() | | ^ |5: hoot| | |5: hoot| | --------- | --------- v --------- v hoot (4, womp) (5, hoot) Priority queues are most commonly used as "event queues" in simulations. Each value on the queue is an event that is expected to take place, and each key is the time the event takes place. A simulation operates by removing successive events from the queue and simulating them. This is why most priority queues return the minimum, rather than maximum, key: we want to simulate the events that occur first first. public interface PriorityQueue { public int size(); public boolean isEmpty(); Entry insert(Object k, Object v); Entry min(); Entry removeMin(); } See page 340 of Goodrich & Tamassia for how they implement an "Entry". Binary Heaps: An Implementation of Priority Queues --------------------------------------------------- A _complete_binary_tree_ is a binary tree in which every row is full, except possibly the bottom row, which is filled from left to right as in the illustration below. Just the keys are shown; the associated values are omitted. 2 index: 0 1 2 3 4 5 6 7 8 9 10 / \ / \ ------------------------------------------------ 5 3 | | 2 | 5 | 3 | 9 | 6 | 11 | 4 | 17 | 10 | 8 | / \ / \ ------------------------------------------------ 9 6 11 4 ^ / \ / | 17 10 8 \--- array index 0 intentionally left empty. A _binary_heap_ is a complete binary tree whose entries satisfy the _heap-order_property_: no child has a key less than its parent’s key. Observe that every subtree of a binary heap is also a binary heap, because every subtree is complete and satisfies the heap-order property. Because they are complete, binary heaps are often stored as arrays of entries, ordered by a level-order traversal of the tree, with the root at index 1. This mapping of tree nodes to array indices is called _level_numbering_. Observe that if a node’s index is i, its children’s indices are 2i and 2i+1, and its parent’s index is floor(i/2). Hence, no node needs to store explicit references to its parent or children. (Array index 0 is left empty to make the indexing work out this nicely. If we instead put the root at index 0, node i’s children are at 2i+1 and 2i+2, and its parent is at floor([i-1]/2).)

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