Welcome to Lesson 18!



Learning Objectives

By the end of this class, you should know...
  • What is a heap?
    • What is the difference between a max and a min heap?
  • What is the heap property of:
    • a max heap?
    • a min heap?
  • How do you build a heap from an unordered array?
  • What are the Big-O run-time of the build operation on a heap?

1. Heap Pre-Assessment

  • Below is the representation of a heap.
    • Let's get familiar with heaps and review some important concepts:
  • What does it resemble?
    • A tree?
    • A binary tree?
    • A binary search tree?
    • A balanced tree?

2. The Heap ADT

2.1. What Is a Heap?

  • A Heap is an ADT that is used to represent a Complete Binary Tree (also called an "Almost Complete Binary Tree").
  • Recall from Lesson 9 that a Complete Binary Tree is one where:
    • all levels are filled (except the last)
    • all nodes are as far left as possible
  • For efficiency, the heap data can be stored in an array.


Image source.

Note that a Heap does not satisfy the conditions for a BST.
  • Instead, a heap maintains one of the Heap Properties:

2.2. Max Heap Property

A[Parent(i)] >= A[i]

2.3. Min Heap Property

A[Parent(i)]  <= A[i]

  • Based on these properties, what kind of heap is the heap above: Max or Min?
  • We will be working with Max Heaps in this class.


2.4. Additional Properties of Heaps

  • The images shown above are only representations of a heap that help us to visualize its structure.
  • Like a hash table, the data in a heap is stored in an array.
  • However, consider that a heap is a binary tree that is stored as a linear type (an array).
  • Therefore, a certain ordering must be maintained inside the array:
  1. Root of the heap is at A[1]
  2. Parent of any node A[i]A[floor(i/2)]
  3. left_child of A[i] = A[2i]
  4. right_child A[i] = A[2i + 1]
where i is the index of a node in the array A, with 1 <= i <= size(A)



Verify that these properties are true using the diagram below:




  • Node:     53        Index:

    Node’s Parent:   Parent’s Index:


    Node:     59        Index:

    Left Child:            Left Child’s Index:

    Right Child:      Right Child’s Index:


  • Note that the above formulas need to be adjusted if you are indexing your array starting at 0 instead of 1.
  • However, typical heap implementations will simply avoid storing any data in the 0th index of the array.

2.5. Applications of Heaps

  • Used for Priority Queue
    • A Priority Queue is similar to the FIFO Queue, but each node also stores a priority value. The first out has highest priority.
  • Sorting algorithm: Heap Sort
    • Will cover at the end of heap lesson
  • Selection algorithms:
    • Efficiently find the kth smallest (or largest) element in the array.

2.6. Important Heap Algorithms

  • Heapify
  • Build Heap
  • Heap-Insert
  • Heap Sort


3. Max-Heapify

  • Given a tree which is a valid max heap -- except for node i, the max-heapify algorithm will rearrange node i and its descendants to satisfy the max-heap property.
  • Precondition: left(i) and right(i) are both valid max heaps
  • In other words, we have one value that is out of place. We need to fix the heap!


3.1. Max-Heapify Example:

Image source.

  • 4 is out of place. The subtrees at 7 and 8 are both valid max heaps.
  • Swap 4 with its largest child (the right child - 8).
  • Is 4 in the correct position after this swap? No.
  • Swap 4 with its largest child (the left child -6).
  • Is 4 in the correct position after this swap? Yes!


3.2. Max-Heapify PseudoCode

Max-Heapify(A, i)

l = left(i) //get the index of the left child of A[i] and store as l

r = right(i) //get the index of the right child of A[i] and store r

//Check if l is off the end of the array (heap) AND compare A[i] to its left child

if (l <= Heap_size(A) AND A[l] > A[i])

    index_of_max = l //update index_of_max if left is bigger

//Check if r is off the end of the array (heap) AND compare A[r] to current max value

if (r <= Heap_size(A) AND A[r] > A[index_of_max]

index_of_max = r //update index_of_max if right is bigger


if (i != index_of_max) //if A[i] was not bigger than its two children

    A[i]<--->A[index_of_max] //swap, so now A[i] stored at A[index_of_max]

    Max-Heapify(A, index_of_max) //recursively move through tree until restore heap property


3.3. Run-Time of Max-Heapify: O(height of the tree) = O(log n)


4. Build-Heap

  • This algorithm takes an un-ordered array and turns it into a valid Heap
  • The build-heap algorithm starts around the middle of the array.
  • It swaps nodes where the parents are smaller than the children (bubbles the values down) until the nodes have reached their correct locations.
    • It uses max-heapify as a helper algorithm to achieve this bubbling down process.
  • The build heap algorithm creates larger and larger valid subheaps until the entire heap is valid.


4.1. Build Heap Example

image showing the progression of build max heap algorithm

image source


4.2. Build-Heap Pseudocode

Build-Heap(A)

n = Heap_size(A)

for (i = floor(n/2) down to 1) //start at floor(n/2); we can ignore leaf nodes

    Max-Heapify(A, i) //call Max-Heapify helper function


4.3. Run-Time of Build-Heap: O(n) (surprise!)

The intuition:
  • The algorithm ignores any leaf nodes (starts at index floor(n/2)).
  • Works bottom up.
    • Every subsequent level requires potentially more swaps,
    • But for only half the number of nodes as the prior level (fewer nodes at each level as you go up the tree)


File:Binary heap bottomup vs topdown.svg

Image source.


5. Build Heap Practice

Trace the build-heap algorithm on the given un-ordered arrays:
  • Call BuildHeap on A= [11, 5, 10, 12, 9, 8, 2, 6, 2, 15]
  • Call BuildHeap on A2 = [12, 7, 15, 9, 2, 4, 6, 8]

Wrap Up

  • Answer the review questions for this lesson on Canvas

~ Have a Great Day! ~