]], also known as , is a simple used to search for a specific in an . This search method traverses the list in a sequential manner from the beginning to the end, checking each element one by one until the desired element is found or until the end of the list is reached.
To perform a Sequential Search, an is used to keep track of the position within the list. Starting at the first element, the search compares each element to the target in a sequential order using a operation. If the element matches the target, the search is successful and returns a value of True. On the other hand, if the end of the list is reached without finding a match, the search returns a Boolean value of False, indicating that the target element is not present in the list.
The of a Sequential Search algorithm is linear, often denoted as , because the average runtime increases linearly with the size of the list. In the , where the target element is located at the end of the list or is not present at all, the algorithm has to traverse the entire list, resulting in the maximum number of comparisons.
In the , the target element is found at the very first position, minimizing the number of comparisons required. This leads to a constant time complexity of O(1). However, it is important to note that the best-case scenario is relatively rare in real-world applications.
The of a Sequential Search falls between the best and worst cases, assuming a roughly even distribution of the target element within the list. While the average runtime is still linear, it is typically closer to the best case than the worst case.
Despite its simplicity, Sequential Search is less efficient than other search algorithms, especially when dealing with larger lists. As the number of elements grows, the time required to search the entire list increases proportionally. Therefore, it is generally not considered suitable for large-scale applications where is crucial.
In terms of efficiency analysis, Sequential Search can be characterized using . The notation O(n) represents the , denoting that the algorithm's runtime grows linearly with the size of the list. The 'n' here represents the number of elements in the list. Thus, as the list size increases, the time complexity also increases at a similar rate.
Keywords
comparison | list | worst-case scenario | sequential [[search | unordered | boolean | efficiency | runtime | algorithm | complexity | best-case scenario | linear time complexity | linear search | o(n) | big o notation | index | average-case scenario | element |
