Data Structures - Divide and Conquer


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.

Divide and Conquer

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.

Useful Video Courses


Azure Data Lake Online Training

42 Lectures 1.5 hours

Ravi Kiran


Data Structure Online Training

141 Lectures 13 hours

Arnab Chakraborty


Oracle Data Guard Online Training

26 Lectures 8.5 hours

Parth Panjabi


Big Data & Hadoop Online Training

65 Lectures 6 hours

Arnab Chakraborty


Python with Data Science

75 Lectures 13 hours

Eduonix Learning Solutions


Mathematics for Data Science and Machine Learning using R

Most Popular

64 Lectures 10.5 hours

Eduonix Learning Solutions