The lecture introduces the concept of search algorithms, focusing on linear search. It explains the need for search in data retrieval, using a bookshelf analogy. The linear search algorithm is detailed, including its implementation and time complexity analysis. The lecture concludes with a brief mention of binary search as the next topic.

Why is searching an important operation in data management?

• To retrieve stored data when needed

• To compress data for storage

• To organize data alphabetically

• To permanently delete data

What is the primary method used in linear search?

• Checking elements in reverse order

• Checking each element sequentially

• Jumping to random elements

• Using a binary tree structure

In the worst-case scenario, what is the time complexity of linear search?

• O(n^2)

• O(log n)

• O(1)

• O(n)

What is the best-case time complexity for linear search?

• O(1)

• O(log n)

• O(n^2)

• O(n)

What is the average-case time complexity of linear search?

• O(n)

• O(n^2)

• O(1)

• O(log n)

What is the space complexity of the linear search algorithm?

• O(n)

• O(n^2)

• O(log n)

• O(1)

What does the linear search algorithm return if the target element is not found?

• The length of the array

• Minus 1

• The first index

• The last index