In this article, we will study what is a divide and conquer algorithm and will also go through the examples and their python code with output. And lastly, we will learn the advantages, disadvantages, and applications of the divide and conquer algorithm. So, let's get started!
What is a Divide and Conquer Algorithm?
Many useful algorithms are recursive in structure, i.e., to solve the given problem, they call themselves recursively one or more times to deal with closely related subproblems. These algorithms typically follow a divideandconquer algorithm. The divide and conquer algorithm involves three steps at each level of recursion.
 Divide: Break the problem into several subproblems that are similar to the original problem but smaller,
 Conquer: Solve the subproblem recursively, and if the subproblem sizes are small enough, just straightforwardly solve the subproblems.
 Combine: Combine this solution to create a solution to the original problem
Algorithm Of Divide and Conquer
Consider an arbitrary problem, and let adhoc be a simple algorithm capable of solving the problem. We call adhoc a basic subalgorithm such that it can be efficient in solving small instances, but its performance on large instances is of no concern.
DivideAndConquer(a, m, n) { if(subProblem(a, m, n)) return(solution(a, m, n)) else p = divide(a, m, n) b = DivideAndConquer(a, m, mid) c = DivideAndConquer(a, mid+1, n) d = combine(b, c) return(d) }
Time Complexity
The time complexity for the divide and conquer algorithm is calculated using the master theorem.
T(n) = aT(n/b) + f(n),
where 'n' is the input size, 'a' is the number of subproblems in the recursion, and ‘n/b’ is the size of each subproblem where all subproblems are assumed to have the same size. We can say that f(n) is the work done outside the recursive call.
Divide and Conquer Algorithm Examples
Divide and conquer approach is widely used to solve many problem statements like merge Sort, quick sort, finding closest pair of points, etc. Below we have mentioned 2 such examples which are most important for any programmer to learn.
Example 1  Tower of Hanoi problem
Problem Statement: In the Tower of Hanoi problem, you are given N blocks in decreasing order of size on Tower A and you want to move all these blocks to Tower C with the help of Tower B. Remember that you can move only one block at a time and block can never be on top of a smaller block.
Suppose we take some blocks let, N = 3, the process of moving the blocks from Tower A to Tower C would be as given below:
First, move the smallest block to Tower C. This step shows the division of the problem by dividing the number of blocks from N=3 to N=2. Therefore, now we can assume our subproblem to be moving 2 blocks from Tower A to Tower C.
Then move the middle block to Tower B as given in the figure below. Similar to the above step, we further divided the subproblem from N=2 to N=1 at the source point.
In this step, we start solving the subproblems by replacing the smallest block over the middle block in Tower B as given below:
Later, move the biggest block to Tower C from Tower A and move the smallest block to empty Tower A from Tower B.
Lastly, we combine the solution and get the final result by moving the middle block over the biggest block from Tower B to Tower C and moving the smallest block from Tower A to Tower C as shown in the figure below:
Here's the illustration of the solution for Tower of Hanoi
Python Code For A Tower Of Hanoi
def towerOfHanoi(N , source, destination, auxiliary): if N==1: print("Move disk 1 from source",source,"to destination",destination) return towerOfHanoi(N1, source, auxiliary, destination) print("Move disk",N,"from source",source,"to destination",destination) towerOfHanoi(N1, auxiliary, destination, source) # Driver code N = 3 towerOfHanoi(N,'A','B','C') # A, C, B are the name of rods
Output
Move disk 1 from source A to destination B
Move disk 2 from source A to destination C
Move disk 1 from source B to destination C
Move disk 3 from source A to destination B
Move disk 1 from source C to destination A
Move disk 2 from source C to destination B
Move disk 1 from source A to destination B
Example 2: Find the minimum and maximum elements in an array.
Problem Statement: In this problem, we are given an array of elements and we have to find the minimum and maximum element from the given array. We will solve the given problem by the divide and conquer algorithm. Here, we divide the elements as the first step of the divide and conquer algorithm, find the minimum and maximum elements from the array as conquering the solution and finalize the answer at the end by combining the results.
Python Code For Finding Minimum And Maximum Element In Array
def divideAndConquer_Max(arr, ind, len): maximum = 1; if (ind >= len  2): if (arr[ind] > arr[ind + 1]): return arr[ind]; else: return arr[ind + 1]; maximum = divideAndConquer_Max(arr, ind + 1, len); if (arr[ind] > maximum): return arr[ind]; else: return maximum; def divideAndConquer_Min(arr, ind, len): minimum = 0; if (ind >= len  2): if (arr[ind] < arr[ind + 1]): return arr[ind]; else: return arr[ind + 1]; minimum = divideAndConquer_Min(arr, ind + 1, len); if (arr[ind] < minimum): return arr[ind]; else: return minimum; if __name__ == '__main__': minimum, maximum = 0, 1; # array initialization arr = [6, 4, 8, 90, 12, 56, 7, 1, 63]; maximum = divideAndConquer_Max(arr, 0, 9); minimum = divideAndConquer_Min(arr, 0, 9); print("The minimum number in the array is: ", minimum); print("The maximum number in the array is: ", maximum);
Output
The minimum number in the array is: 1
The maximum number in the array is: 90
Divide and Conquer vs Dynamic Programming Algorithm
Divide and Conquer

Dynamic Programming

It includes 3 steps to reach the solution: Divide: Dividing the original problem into a smaller subproblem Conquer: Solving subproblem recursively Combine: Combine the solution of the subproblem to find a final solution 
It includes 4 steps to reach the solution: Characterizing the structure of an optimal solution Define the value of an optimal solution recursively Using the bottomup algorithm, calculate the value of an optimal solution Using computed information, construct an optimal solution 
It is recursive in nature 
It is nonrecursive in nature 
It recursively works on subproblems and hence consumes more time 
It solves the subproblem only once, and therefore it consumes less time 
It is a topdown algorithm 
It is a bottomup algorithm 
Subproblems are independent in nature 
Subproblems are interdependent in nature 
Example: Linear search and Merge sort 
Example: Matrix Multiplication

Advantages of Divide and Conquer Algorithm
 The divide and conquer algorithm is easy and effective to solve difficult problems by breaking them into subproblems
 The divide and conquer algorithm helps to solve the subproblem independently
 The divide and conquer algorithm makes effective and efficient use of memory caches
 The divide and conquer algorithm is often used in sorting algorithms like merge sort, quick sort, etc
 It is also used in searching algorithms like a linear search and binary search
 The round of control in such an algorithm is very efficient and therefore, it is better to use the divide and conquer algorithm while dealing with floating numbers.
 Divide and conquer algorithms does not require any modifications
Disadvantages of Divide and Conquer Algorithm
 In the divide and conquer algorithm, the recursion of the subproblem is slow
 For some specific problems, the divide and conquer method becomes complicated in comparison to the iterative method
Applications
 The divide and conquer algorithm is used to find min and max
 The divide and conquer algorithm is used in the binary search algorithm
 It is used to find the power of the element
 Divide and conquer is the major algorithm in merge sort and quick sort
 Merge sort is used in selection procedures
 It is used Strassen's algorithm for matrix multiplication
 It is used in karatsuba Algorithm
 Merge sort is used in the cooleyTukey Algorithm
Conclusion
As we studied, the divide and conquer algorithm has many benefits that make it a very useful and important problemsolving algorithm. It helps to solve difficult problems in 3 simple steps using the topdown algorithm recursively. This basic idea of the divide and conquer algorithm can be related to everyday life with lots of practical applications and hence it is important to learn and understand the divide and conquer algorithm.