Hard Problems on Heap Data Structure
- Design an efficient data structure for given operations
- Merge k sorted arrays | Set 1
- Sort numbers stored on different machines
- Smallest Derangement of Sequence
- Largest Derangement of a Sequence
- Maximum difference between two subsets of m elements
- Convert BST to Min Heap
- Merge two binary Max Heaps
- K-th Largest Sum Contiguous Subarray
- Minimum product of k integers in an array of positive Integers
- Leaf starting point in a Binary Heap data structure
- Rearrange characters in a string such that no two adjacent are same
- Sum of all elements between k1’th and k2’th smallest elements
- Minimum sum of two numbers formed from digits of an array
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Heap Data Structure
A Heap is a complete binary tree data structure that satisfies the heap property: for every node, the value of its children is less than or equal to its own value. Heaps are usually used to implement priority queues, where the smallest (or largest) element is always at the root of the tree.
Table of Content
- What is Heap Data Structure?
- Types of Heaps
- Heap Operations
- Heap Data Structure Applications
- Basics of Heap Data Structure
- Other Types of Heap Data Structure
- Easy Problems on Heap Data Structure
- Medium Problems on Heap Data Structure
- Hard Problems on Heap Data Structure