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.

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).

A search algorithm that repeatedly divides a sorted list in half to find a target value.

A search algorithm that checks each item in a list sequentially until the target value is found.

A measure of the amount of time taken by an algorithm to run as a function of the input size.

Linear time complexity, where the time taken increases linearly with the input size.

Logarithmic time complexity, where the time taken increases logarithmically with the input size.

A list where elements are arranged in a specific order (ascending or descending).

The portion of the data structure that is being examined by a search algorithm.

A step-by-step procedure for solving a problem.

A way of organizing and storing data.

The element located at the midpoint of the current search space.