7 Search Algorithms You Need To Know
All the data in the world is worth nothing if you can’t find what you are looking for.
Here are 7 Search Algorithms you need to know!
1. Linear Search: This is the simplest searching algorithm.
In a linear order, you search elements one by one. This method is common for small datasets or when the data is unordered.
2. Binary Search: Use this algorithm on sorted arrays.
It involves repeatedly dividing the search interval in half. It's much more efficient than a linear search for larger datasets.
3. Depth-First Search (DFS): DFS traverses graphs or trees.
It explores as far as possible along a branch before backtracking. It's often used to solve problems like:
- Finding paths
- Connected components
- Traversing tree structures.
4. Breadth-First Search (BFS): Like DFS, BFS traverses graphs or trees.
It explores all the nodes at the current depth level before moving on to the next level. BFS is commonly used to find the shortest path in unweighted graphs.
5. Interpolation Search: Similar to binary search, this algorithm works on sorted datasets.
It calculates the probable position of the target element based on its value. It's especially useful when the dataset is uniformly distributed.
6. Jump Search: Jump search is an improvement over linear search.
You divide the array into blocks and determine a step size. The search jumps ahead in blocks until it finds the target element or passes it, and then it performs a linear search within that block.
7. Exponential Search: Use this algorithm on sorted arrays.
It involves finding a range in which the target element exists and performing a binary search. It's useful for quickly narrowing down the search space.
These search algorithms have various use cases and performance characteristics, making them suitable for different scenarios.
Understanding their strengths and weaknesses will help you choose the most appropriate algorithm for a problem.
What's the search algorithm you struggled with?
Mine was Exponential Search.
