Divide and Conquer Algorithms
Instructor:
Mr. Faizan Parvez
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About this course
In this course, we’ll start by understanding the concept of divide and conquer, i.e., how a problem can be divided into two or more sub-problems of the same type. We will also look at why it’s required and the different steps involved in it: divide, conquer and combine. The first type of algorithm that we’ll look into is Binary Search. We’ll understand the concept of Binary Search, post which we will understand the algorithm along with a demonstration, its implementation and time & space complexity, and then understand how this algorithm falls under the divide and conquer paradigm. Following this, we will also learn about Quick Sort and Merge Sort algorithms. We will understand their concepts, algorithm, demonstration, implementation, time complexity, space complexity and then understand how these algorithms also fall under the divide and conquer model.
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Course Outline
This module introduces you to the algorithm and its role in coding.
Our course instructor
Mr. Faizan Parvez
IT & Software Expert
Frequently Asked Questions
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Is this course free?
What is Divide and Conquer technique explained with examples?
The Divide and Conquer technique is an approach where we divide the problem to be solved into smaller sub-problems. These sub-problems are then solved independently. This continues where we divide the problem into smaller sub-problems, and at one point, we will then reach a stage where we don’t have to divide the problem anymore. Quicksort and Mergesort are the best examples of the Divide and Conquer approach.
Which algorithm uses Divide and Conquer?
The best examples of the Divide and Conquer algorithms are Quicksort and Mergesort. Compared to other sorting algorithms, these two algorithms have a better performance rate. FFT (Fast Fourier Transform) algorithm follows the Divide and Conquer method to achieve the O(N log N) execution time.
Why is Divide and Conquer faster?
The Divide and Conquer method is faster because it divides the bigger problem into smaller subproblems and then solves it independently. The main problem is divided until it can’t be further, and that is where we stop dividing and combine all these sub-problem solutions to get the required solution for the main problem.
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