- Data Structures & Algorithms
- DSA - Home
- DSA - Overview
- DSA - Environment Setup

- Algorithm
- DSA - Algorithms Basics
- DSA - Asymptotic Analysis
- DSA - Greedy Algorithms
- DSA - Divide and Conquer
- DSA - Dynamic Programming

- Data Structures
- DSA - Data Structure Basics
- DSA - Array Data Structure

- Stack & Queue
- DSA - Stack
- DSA - Expression Parsing
- DSA - Queue

- Searching Techniques
- DSA - Linear Search
- DSA - Binary Search
- DSA - Interpolation Search
- DSA - Hash Table

- Sorting Techniques
- DSA - Sorting Algorithms
- DSA - Bubble Sort
- DSA - Insertion Sort
- DSA - Selection Sort
- DSA - Merge Sort
- DSA - Shell Sort
- DSA - Quick Sort

- Graph Data Structure
- DSA - Graph Data Structure
- DSA - Depth First Traversal
- DSA - Breadth First Traversal

- Tree Data Structure
- DSA - Tree Data Structure
- DSA - Tree Traversal
- DSA - Binary Search Tree
- DSA - AVL Tree
- DSA - Spanning Tree
- DSA - Heap

- DSA Useful Resources
- DSA - Questions and Answers
- DSA - Quick Guide
- DSA - Useful Resources
- DSA - Discussion

In divide and conquer approach, the problem in hand, is divided into smaller sub-problems and then each problem is solved independently. When we keep on dividing the subproblems into even smaller sub-problems, we may eventually reach a stage where no more division is possible. Those "atomic" smallest possible sub-problem (fractions) are solved. The solution of all sub-problems is finally merged in order to obtain the solution of an original problem.

Broadly, we can understand **divide-and-conquer** approach in a three-step process.

This step involves breaking the problem into smaller sub-problems. Sub-problems should represent a part of the original problem. This step generally takes a recursive approach to divide the problem until no sub-problem is further divisible. At this stage, sub-problems become atomic in nature but still represent some part of the actual problem.

This step receives a lot of smaller sub-problems to be solved. Generally, at this level, the problems are considered 'solved' on their own.

When the smaller sub-problems are solved, this stage recursively combines them until they formulate a solution of the original problem. This algorithmic approach works recursively and conquer & merge steps works so close that they appear as one.

The following computer algorithms are based on **divide-and-conquer** programming approach −

- Merge Sort
- Quick Sort
- Binary Search
- Strassen's Matrix Multiplication
- Closest pair (points)

There are various ways available to solve any computer problem, but the mentioned are a good example of divide and conquer approach.

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