# Heaps

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## Data Structures and Algorithms

### 5th lecture, October 15, 2015

http://www.sw.it.aoyama.ac.jp/2015/DA/lecture5.html

### Martin J. Dürst

© 2009-15 Martin J. Dürst 青山学院大学

# Today's Schedule

• Summary of last lecture, homework
• Priority queue as an ADT
• Efficient implementation of priority queue
• Complete binary tree
• Heap
• Heap sort
• How to use `irb`

# Summary of Last Lecture

• The order (of growth)/(asymptotic) time complexity of an algorithm can be calculated from the number of the most frequent basic operations
• Calculation can use a summation or a recurrence (relation)
• The big-O notation compactly express the inherent efficiency of an algorithm
• An abstract data type (ADT) combines data and the operations on this data
• Stack and queue are typical examples of ADTs
• Each ADT can be implemented in different ways
• Depending on implementation, the time complexity of each operation of an ADT can change

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# Priority Queue

Example from IT:
Queue for process management, ...
Operations:
• Creation: new, init
• Check for emptiness: empty?
• Return and remove item with highest priority: getNext/delMax/...
• Return item with highest priority (without removal): findMax/peekAtNext/...

# Simple Implementations

 Implementation Array (ordered) Array (unordered) Linked list (ordered) Linked list (unordered) `insert` O(n) O(1) O(n) O(1) `getNext` O(1) O(n) O(1) O(n) `findMax` O(1) O(n) O(1) O(n)

Time complexity for each operation differs for different implementations.

But there is always an operation that needs O(n).

Is it possible to improve this?

# Complete Binary Tree

Definition based on tree structure:

• Allmost all internal nodes (except maybe one node) have have 2 children
• All tree layers except the lowermost are full
• The lowermost tree layer is filled from the left

# Implementing a Complete Binary Tree with an Array

• Implementing a tree by allocating individual nodes and connecting them with pointers is complicated
• Compared to this, operations on an array are simple
• A complete binary tree can be implemented with an array as follows:
(this is also how Knuth defines a complete dinary tree)

Give each node in the complete binary tree with n nodes a number so that:

• Number 0 stays unused
• Each node has a number between 1 and n (inclusive)
• The root has number 1
• The number of the parent of node i is ⌊i/2⌋ (i>1)

# Heap

A heap is a

• Complete binary tree where
• Each parent always has higher priority than its children

⇒ The root always has the highest priority

We need the following operations for implementing a heap::

• Addition and removal of data items
• Restauration of invariants

# Invariant

• A condition that is always maintained in a data structure or algorithm (especially loop)
• Very important for data structures
• Can be used in proofs (properties of data structures, correctness of algorithms, ...)
• After an operation on (change to) a data structure, it may be necessary to restore invariants

# Restoring Heap Invariants

If the priority at a given node is too high: Use `heapify_up`

• Compare priority with parent
• If parent priority is lower, exchange with parent
• Continue until parent priority is higher

If the priority at a given node is too low: Use `heapify_down`

• Compare priority with both children
• If necessary, exchange with child with higher priority
• Continue at exchanged child until exchange becomes unnecessary

Implementation: 5heap.rb

# Implementing a Priority Queue with a Heap

 Implementation `Heap` (implemented as an `Array`) `insert` O(log n) `findMax` O(1) `getNext` O(log n)

# Heap Sort

• Use priority queue to sort by (decreasing) priority
1. Create a heap from all the items to be sorted
2. Remove items from heap one-by-one: They will be ordered by (decreasing) priority
• Implementation optimization:
Use space at the end of the array to store removed items
⇒ The items will end up in the array in increasing order
• Time complexity:O(n log n)
• Addition and removal of items is O(log n) for each item
• To sort n items, the total complexity is O(n log n)

# How to use `irb`

`irb`: Interactive Ruby, a 'command prompt' for Ruby

Example usage:

```C:\Algorithms>irb
=> true
irb(main):002:0> h = Heap.new
=> #<Heap:0x2833d60 @array=[nil], @size=0>
irb(main):003:0> h.insert 3
=> #<Heap:0x2833d60 @array=[nil, 3], @size=1>
irb(main):004:0> h.insert(5).insert(7)
=> #<Heap:0x2833d60 @array=[nil, 7, 3, 5], @size=3>
...```

# Other Kinds of Heaps

• Priority queues can be used as components in many different algorithms
• Often, two priority queues need to be joined
• With the 'usual' heap, joining is O(n)
• With a binomial queue, joining is O(log n)
• With a Fibonacci heap, joining can be improved to O(1)

# Ideas to Improve Implementation of Priority Queue

• Started with two simple implementations
• New idea: Combining both implementations/finding a balance between the two implementations
• Not completely ordered, but also not completely unordered
→ Partially ordered, just to the extent necessary to find highest priority item

# Conceptual Layers

• Application: Heap sort
• Conceptual data structure: Heap
• Actual data structure: Complete binary tree
• Internal implementation: Array

# Summary

• A priority queue is an important ADT
• Implementing a priority queue with an array or a linked list is not efficient
• In a heap, each parent has higher priority than its children
• In a heap, the highest priority item is at the root of a complete binary tree
• A heap is an efficient implementation of a priority queue
• Many data structures are defined using invariants
• A heap can be used for sorting, using heap sort

# Report: Manual Sorting

Deadline: November 3rd, 2015 (Wednesday), 19:00.

Where to submit: Box in front of room O-529 (building O, 5th floor)

Format:

• A4, double-sided 4 pages (2 sheets of paper, stapled in upper left corner; NO cover page)
• Easily readable handwriting (NO printouts)
• Name (kanji and kana), student number, course name and report name at the top of the front page

Problem: Propose and describe an algorithm/algorithms for manual sorting, for the following two cases:

1. One person sorts 6000 pages
2. 20 people together sort 60000 pages

Each page is a sheet of paper of size A4, where a 10-digit number is printed in big letters.

The goal is to sort the pages by increasing number. There is no knowledge about how the numbers are distributed.

You can use the same algorithm for both cases, or a different algorithm.

Details:

• Describe the algorithm(s) in detail, so that e.g. your friends who don't understand computers can execute them.
• Describe the equipment/space that you need.
• Calculate the overall time needed for each case.
• Analyse the time complexity (O()) of the algorithm(s).
• Comment on the relationship to other algorithms you know, and on the special needs of manual (as opposed to computer) execution.
• If you use any Web pages, books, ..., list them as references at the end of your report
Caution: Use IRIs (e.g. http://ja.wikipedia.org/wiki/情報), not URLs (e.g. http://ja.wikipedia.org/wiki/%E6%83%85%E5%A0%B1)

# Homework

(for next week, no need to submit)

1. Implement joining two (normal) heaps.
2. Think about the time complexity of creating a heap:
`heapify_all` will be called n/2 times and may take up to O(log n) each time.
Therefore, one guess for the overall time complexity is O(n log n).
However, this upper bound can be improved by careful analysis.
3. Find five different applications of sorting.
4. Bring a small pair of scissors (to cut paper) to the next lecture.

# Glossary

priority queue

complete binary tree

heap
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internal node

restauration

invariant

sort

decreasing (order)

increasing (order)

join

binomial queue
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