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Balanced binary tree implementation of priority queues

Kotlin 100.00%
balanced-trees data-structures priority-queue min-priority-queue

kotiln-priority-queues's Introduction

Priority Queues

Balanced binary tree implementation of priority queues in Kotlin

Time Complexities

  1. Insertion: O(log n)
  2. Removal: O(log n)
  3. Returning max or min: O(1)

Implementation

  1. Min Priority Queues:
    Removing and inserting to the priority queue uses helper functions heapifyDown() and heapifyUp() to maintain heap properties.

    class MinPriorityQueue<T: Comparable<T>> : PriorityQueue<T>() {
        fun removeMin(): T? {
            if (isEmpty()) return null
            if (size == 1) return heapArray.removeAt(0)
    
            val minElement = heapArray[0]
    
            heapArray[0] = heapArray.removeAt(size - 1)
            heapifyDown()
    
            return minElement
        }
    
        fun insertItem(item: T) {
            heapArray.add(item)
            heapifyUp(size - 1)
        }
    
        fun min(): T? {
            return heapArray.firstOrNull()
        }
    }
  2. Max Priority Queues:
    They also maintain heap properties using the helper functions.

    class MaxPriorityQueue<T: Comparable<T>> : PriorityQueue<T>() {
        fun removeMax(): T? {
            if (isEmpty()) return null
            if (size == 1) return heapArray.removeAt(0)
    
            val maxElement = heapArray[0]
            heapArray[0] = heapArray.removeAt(size - 1)
            heapifyDown()
    
            return maxElement
        }
    
        fun insertItem(item: T) {
            heapArray.add(item)
            heapifyUp(size - 1)
        }
    
        fun max(): T? {
            return heapArray.firstOrNull()
        }
    }

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