In this DSA tutorial, you will learn what is divide and conquer Algorithm and how to use it.
In divide and conquer approach, the problem at 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.
Divide/Break
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.
Conquer/Solve
This step receives a lot of smaller sub-problems to be solved. Generally, at this level, the problems are considered ‘solved’ on their own.
Merge/Combine
When the smaller sub-problems are solved, this stage recursively combines them until they formulate a solution to the original problem. This algorithmic approach works recursively and conquers & merge steps works so close that they appear as one.
Examples
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.
Data Structures Algorithms Books: Algorithms Plus Data Structures
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