Search Algorithms

Introduction to Search Algorithms

Search algorithms are a collection of methods used to find specific data in a collection.
These algorithms scrutinize the collection systematically to find the requested data.
Different search algorithms are suited to different types of data sets and structures.

The Linear Search works by starting at the first item in a list and checking each item in turn until it finds what it’s looking for.
This method works well for small lists or unsorted data.
It’s easy to understand and implement but not optimal for larger data sets due to its low efficiency.

The Binary Search works on sorted data sets by dividing the set in half repeatedly until it locates the desired data.
At each step, the algorithm compares the middle element with the target element. If the target value matches the middle element, its position in the list is returned. If the target value is less or more than the middle element, the search continues the on lower or upper half of the list respectively.
This algorithm is much more efficient than a linear search for larger data sets but requires the list to be sorted first.

Hashing is an advanced search technique that uses a function to convert keys (data you are looking for) into addresses (where the data is stored).
This method is highly efficient with almost constant search time, regardless of the number of items in the collection.
However, designing an efficient hash function is challenging. Poorly designed hash functions can lead to clustering, a situation in which many keys map to the same address leading to slower search times.

Interpolation Search is an algorithm for searching for a key in an array that has been ordered by numerical values assigned to the keys (probabilistic search).
This method estimates where a key could be, based on the key values at the ends of the array, so it can be faster for data sets where the data is uniformly distributed.

To determine the most suitable search algorithm consider factors like: the size of the data set, whether the data set is sorted, how uniformly distributed the keys are and how often searches will be performed.
Different search algorithms have different time complexities, a mathematical representation of the amount of time an algorithm takes to run. This can help to evaluate which algorithm would be the most efficient to use.
Always keep readability and maintainability of the code in mind as overly complex algorithms can become difficult to understand and manage.