# Compressed segment trees and merging sets in O(N*logN)

## Introduction:

Efficiently managing data structures lies at the heart of algorithmic problem-solving. Among the myriad challenges that arise in this domain, performing set operations and range queries on large datasets is a common task. One powerful approach to tackle these challenges is through the use of compressed segment trees.

## Understanding Segment Trees:

Before diving into the concept of compressed segment trees, let's establish a foundation by understanding regular segment trees. A segment tree is a binary tree data structure that represents an array. Each node of the tree represents a range of elements in the array, and the leaves represent individual elements.

Segment trees are widely used for range queries and updates on arrays. They enable operations like finding the sum, minimum, maximum, or any associative operation within a given range of array elements in O(logN) time complexity. This efficiency comes from the property that each element in the array is stored in multiple nodes of the segment tree.

## The Challenge of Merging Sets:

Consider a scenario where you have multiple sets, and you need to efficiently merge them while also being able to query the number of elements in a given range across all sets. This situation arises in various problems, such as maintaining dynamic frequency counts, finding distinct elements in a range, or calculating the union of sets.

A straightforward approach would be to merge the sets naively, resulting in a merged set that can be queried using basic data structures. However, this approach would yield poor time complexity, often O(N^2), which is far from efficient for large datasets.

## Enter Compressed Segment Trees:

Compressed segment trees are an innovative extension of regular segment trees that excel in handling set merging and range queries. They achieve this efficiency by avoiding unnecessary memory usage and focusing on essential nodes.

The core idea behind compressed segment trees is to exploit the fact that the sets we are merging have a lot of common elements. Instead of building a segment tree for each individual set, we build a single compressed segment tree that covers all the sets. This shared tree eliminates the redundant storage of common elements and optimizes memory usage.

In a compressed segment tree, each node still represents a range of elements. However, unlike regular segment trees, not every element has its own node. Instead, only the unique elements across all sets are stored in the nodes. This compression significantly reduces the memory footprint.

## Building the Compressed Segment Tree:

The process of building a compressed segment tree involves the following steps:

Collecting Unique Elements: Gather all unique elements from the sets and sort them. This sorted list will serve as the basis for constructing the tree.

Mapping Elements to Indices: Map each unique element to an index in the sorted list. This index will be used to locate the element within the tree.

Constructing the Tree: Recursively divide the sorted list into two halves and create nodes for the ranges they cover. Each node stores the number of occurrences of its corresponding element in the sets.

Propagating Information: As the tree is built, propagate information upwards. For example, in a sum query, each node stores the sum of its children. This information helps answer range queries efficiently.

## Merging Sets and Range Queries:

With the compressed segment tree constructed, merging sets and performing range queries becomes a straightforward process:

Merging Sets: To merge two sets, update the corresponding nodes in the tree by incrementing their values based on the elements in the new set.

Range Queries: For range queries, traverse the tree to locate the nodes representing the elements within the desired range and calculate the relevant information, such as sum or count.

## Time Complexity Analysis:

The beauty of compressed segment trees lies in their time complexity. Constructing the compressed segment tree takes O(N*logN) time due to the sorting step. However, once constructed, both merging sets and answering range queries take O(logN) time, making these operations extremely efficient even for large datasets.

Compressed Segment Trees offer several advantages:

Efficiency: The most prominent advantage is their superior time complexity for set merging. This is particularly beneficial when dealing with large datasets, where traditional methods would exhibit poor performance.

Versatility: While Compressed Segment Trees are especially useful for set merging, they can also be adapted for other range-based operations, making them a versatile data structure.

Optimization: The tree's compression technique minimizes memory usage and allows for faster traversal, resulting in optimized performance for various applications.

## Merging Sets using Compressed Segment Trees

One of the interesting applications of compressed segment trees is efficiently merging sets. Suppose you have two sets A and B, and you want to merge them to form a new set C. This operation involves taking the union of elements from both sets without duplication. Traditional methods of merging sets could take O(N) time complexity, but with compressed segment trees, this can be achieved in O(N*logN) time.

Explanation:

• The program starts by including necessary libraries and defining constants. It also declares data structures to store the merged array of sets A and B, a hash map for value compression, and an array for the compressed segment tree.
• The build function constructs the compressed segment tree recursively. It divides the range into smaller segments and calculates the minimum value within each segment. This allows for efficient querying of minimum values within a given range.
• The query function is used to perform range queries on the compressed segment tree. It recursively traverses the tree, checking for intersections between the query range and the current tree node's range.
• If there is no intersection, it returns a default maximum value (INT_MAX). If the query range is entirely contained within the current node's range, it directly returns the stored minimum value. Otherwise, it recursively queries the left and right child nodes and returns the minimum of their results.
• In the main function, two example sets A and B are defined. These sets are merged and sorted, and duplicates are removed to create the mergedArray. The values in mergedArray are then compressed using the compressed hash map, where each value is mapped to its compressed index in the sorted array.
• The build function is called to construct the compressed segment tree using the compressed indices of mergedArray.
• Two example queries are performed using the query The first query finds the 6th smallest element in the merged array, while the second query counts the number of elements less than 9. The results are printed to the console.

Program Output:

## Conclusion:

In conclusion, the utilization of Compressed Segment Trees presents an innovative solution to the space complexity limitations often encountered when dealing with large datasets and complex operations. By efficiently merging sets using this advanced data structure, we've demonstrated how to achieve an optimal time complexity of O(N*logN), which is essential for addressing real-world problems that demand quick and accurate computations.

The concept of Compressed Segment Trees not only enables us to streamline memory usage but also empowers us to solve intricate problems with elegance and efficiency.

As we've seen in the practical implementation, the technique can be applied to merge sets while facilitating operations such as finding the k-th smallest element or counting the number of elements smaller than a given value. This exemplifies the power of combining algorithmic prowess with clever data structure design.

In a world where data continues to grow exponentially, the ability to harness the capabilities of Compressed Segment Trees showcases the dynamic nature of computer science. As we continue to explore novel approaches like this, we unlock new avenues for optimization and innovation, ensuring that we are well-equipped to tackle the challenges of today and tomorrow.