What is the optimal time complexity of solving Linear Search?

The optimized solution achieves a time complexity of O(n log n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for Linear Search?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in Linear Search?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for Linear Search?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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DSA SectionEasy

Linear Search

Learn how to solve the 'Linear Search' problem. This detailed resource details brute force and optimized approaches.

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Problem Statement

Easy

Write a function linear_search(arr, target) that takes a list of integers arr and an integer target. It scans the list from start to finish and returns the 0-based index of the first occurrence of target. If target is not present in the list, return -1.

Constraints

•0 <= len(arr) <= 1000
•-10^9 <= arr[i], target <= 10^9

Examples

Example 1

Input

arr = [4, 2, 9, 1, 7], target = 9

Output

Explanation

The target 9 is found at index 2 of the list [4, 2, 9, 1, 7].

Example 2

Input

arr = [4, 2, 9, 1, 7], target = 5

Output

-1

Explanation

The target 5 is not present in the list, so we return -1.

Need a Hint?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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