Linear Search: Works on unsorted lists, O(n) time complexity | Binary Search: Requires sorted lists, O(log n) time complexity.

Linear Search: Less efficient, checks each element | Binary Search: More efficient, eliminates half of the data with each step.

Linear Search: Minimal, no extra memory needed | Binary Search: Minimal, no significant extra memory needed.

Linear Search: Simple to implement | Binary Search: More complex to implement due to sorting and halving.

Linear Search: Target is the first element | Binary Search: Target is the middle element.

Linear Search: Target is the last element or not present | Binary Search: Target is not present.

Linear Search: Applicable to unsorted lists | Binary Search: Not applicable to unsorted lists.

Linear Search: Performance degrades linearly with data size | Binary Search: Performance degrades logarithmically with data size.

Linear Search: Easier to understand | Binary Search: More complex to understand.

Linear Search: No preprocessing needed | Binary Search: Requires preprocessing (sorting).

1. Sort the list. 2. Find the middle element. 3. Compare with the target. 4. Eliminate half the list. 5. Repeat until found or not found.

1. Ensure the list exists. 2. Populate the list with data. 3. Sort the list in ascending or descending order.

1. Compare the target with the middle element. 2. If target is greater, set low to mid + 1. 3. If target is smaller, set high to mid - 1.

1. Continue dividing and comparing until low > high. 2. If low > high, the target is not in the list. 3. Return a 'not found' indicator (e.g., -1).

1. Get the sorted list. 2. Define the target value. 3. Initialize low to 0 and high to len(list) - 1.

1. Sum low and high index. 2. Divide sum by 2. 3. Take the integer part of the result.

1. Check if target equals middle element. 2. If equal, the target is found. 3. If not equal, proceed to eliminate half of the list.

1. If target > middle element, set low = mid + 1. 2. If target < middle element, set high = mid - 1.

1. Check if low <= high. 2. If true, repeat steps to find the middle element and compare. 3. If false, target is not found.

1. Return the index of the middle element. 2. Terminate the search. 3. Optionally, perform additional actions (e.g., print a message).

Searching for a word in a dictionary, finding a contact in a sorted phone book, or locating data in a sorted database index.

Finding a specific record by ID in a sorted index of a database table.

Identifying the commit where a bug was introduced using a process similar to binary search (bisecting).

Efficiently locating a specific number in a sorted array of integers or floating-point numbers.

Quickly finding a file by name in a sorted directory structure.

Efficiently locating a configuration setting in a sorted configuration file.

Quickly locating a page number in a sorted index of a book.

Efficiently finding a value that falls within a specified range in a sorted dataset.

Quickly locating an item by its unique identifier in a sorted inventory list.

Efficiently finding a log entry by timestamp in a sorted log file.