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Divide and Conquer algorithms are an algorithmic strategy that can be used to solve a variety of problems in computer science.

In this article, we will discuss how Divide and Conquer algorithms work and explore some examples of their applications.

# Divide and Conquer

Divide and Conquer is an algorithmic technique used to solve problems by breaking them down into smaller, more manageable sub-problems.

This technique is often used to solve problems that are too complex or too large to solve in one step.

The idea behind Divide and Conquer is to divide the problem into smaller, easier-to-solve sub-problems, then combine the solutions to the sub-problems to get the solution for the original problem.

This technique is often used with recursion, where the sub-problems are themselves divided into smaller sub-problems until an answer can be found.

# How Do Divide and Conquer Algorithms Work?

Divide and conquer algorithms work by breaking down a problem into smaller, more manageable subproblems.

**Step 1: Divide –** Start by dividing the problem into smaller and more manageable subproblems.

**Step 2: Conquer –** Solve each of the smaller subproblems.

**Step 3: Combine –** Take the solutions of the subproblems and combine them to get the solution to the original problem.

Now, let’s sort an unsorted array with the help of the Divide and Conquer Algorithm (i.e., Merge Sort)

**Problem:** Sort an array of alphabets in ascending order.

Let the given unsorted array be:

**Step 1: Divide:** Split the array into two halves.

Recursively divide each subarray into two halves again until you have individual elements.

**Step 2: Conquer: **Sort each half individually, using the same divide and conquer approach.

**Step 3: Combine:** Merge the two sorted halves back into one sorted array.

# Time Complexity Of Divide and Conquer Algorithm

The time complexity of the divide and conquer algorithm is typically expressed in terms of the number of subproblems, N, that must be solved.

The time complexity of the divide and conquer algorithm is typically of the form `T(N) = aT(N/b) + f(N)`

, where `a`

and `b`

are constants, and `f(N)`

is a function of `N`

. This can be further simplified to `T(N) = O(NlogN)`

if `a = b and f(N) = O(N)`

.

Let’s take a look at an example. Suppose we want to find the maximum element in an array of numbers. We can use the divide-and-conquer approach to solve this problem.

First, we will divide the array into two halves. Then, we will recursively find the maximum element in each half and compare them to find the overall maximum element. This process can be represented as follows:

`T(N) = 2T(N/2) + O(1)`

We can solve for the time complexity of this algorithm by using the master theorem. This gives us `T(N) = O(NlogN)`

.

# Applications of the Divide-and-Conquer Algorithm

Divide-and-conquer algorithms are used in a wide variety of applications. They are an efficient way to solve problems that can be broken down into smaller subproblems. Examples of these applications include:

**1. Sorting: **Sorting algorithms such as QuickSort, MergeSort, and HeapSort use the divide-and-conquer approach. These algorithms divide the data into smaller pieces and then sort each piece separately. After sorting the individual pieces, the algorithm combines the pieces together to form the sorted list.

**2. Graph Algorithms:** Graph algorithms such as the shortest path algorithm, the minimum spanning tree algorithm, and the maximum flow algorithm can be solved using the divide-and-conquer approach. These algorithms break the problem into smaller subproblems and then solve each subproblem separately.

**3. String Matching:** String matching algorithms such as the Knuth-Morris-Pratt algorithm and the Boyer-Moore algorithm use the divide-and-conquer approach to find a pattern in a string of text. The algorithm divides the text into smaller pieces and then checks each piece for the pattern.

**4. Matrix Multiplication:** Matrix multiplication algorithms such as Strassen’s Matrix Multiplication use the divide-and-conquer approach to divide the matrix into smaller submatrices and then recursively perform the multiplication.

**5. Closest Pair Problem: **The closest pair problem is a geometric problem which requires finding the two closest points in a set of points. This problem can be solved using the divide-and-conquer approach by dividing the set of points into two halves and then recursively calculating the closest pair in each half.

# Advantages of Divide and Conquer Algorithm

The following are the advantages of using the Divide and Conquer algorithm:

**Efficiency:**Divide and conquer algorithms are generally more efficient than other algorithmic techniques, such as brute force and greedy algorithms. By dividing the problem into smaller subproblems, the overall time complexity is reduced significantly. This makes it particularly suitable for solving problems that have large inputs.

**Flexibility:**Divide and conquer algorithms can be applied to a variety of different problems, making them very versatile. Many of the standard algorithms used in computer science today are based on divide-and-conquer techniques.

**Parallelizability:**Divide and conquer algorithms can be easily parallelized, meaning that they can be implemented on multiple processors simultaneously. This makes them well-suited for large-scale problems that require significant computational power.

**Reusability:**Divide and conquer algorithms are often used as building blocks for more complex algorithms. This allows the same algorithm to be used multiple times, saving time and effort.

**Memory Usage:**Since divide and conquer algorithms divide the problem into smaller subproblems, the amount of memory needed is less than with other algorithms. This makes them well-suited for problems that require large inputs.