Let minHeap is an integer array with root at index “ i = 0 ”.We are going to demonstrate how you can simply access the parent, right or left child nodes using the following formulas. Figure 3: Array representation of the Heap in Figure 2 Just like we don’t have any data structure to store a “ tree” in Java and we build a “node” for it, or the way we use “map” to store a “ graph”. You can look at it as, the values of nodes / elements of a min-heap are stored in an array. As a beginner you do not need to confuse an “array” with a “min-heap”. Figure 2: Min heap with left child nodes > right child nodes Representation of Min Heap in JavaThe most commonly used data structure to represent a Min Heap is a simple Array. For example, it is possible that the values for all nodes in the left subtree of the root are greater than the values for every node of the right subtree. Note that there is no necessary relationship between the value of a node and that of its sibling in either the min-heap or the max-heap. Because the root has a value less than or equal to its children, which in turn have values less than or equal to their children, the root stores the minimum of all values in the tree. What is a Min Heap?A min-heap has the property that every node at level ‘n’ stores a value that is less than or equal to that of its children at level ‘n+1’. In this post we’ll take a deep dive to see how heaps are different from Min-Heaps and how we can use Priority Queues to Implement Min Heaps. Many novice programmers can struggle with the concept of Heaps, Min Heaps and Priority Queues. If you’re not familiar with these concepts, we recommend you to understand these as a prerequisite. Whereas, a Binary Heap is a complete binary tree which satisfies either the min-heap or max-heap ordering property. Before we get started, it is assumed that you know about a Binary Tree (in a binary tree, each node stores a key greater than all the keys in its left subtree and less than all the keys in its right subtree).
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