Binary Search Complexity

The video tutorial explains the complexity of binary search, highlighting its efficiency compared to linear search. It describes the binary search process, where an array is divided into halves to find a target element, resulting in a time complexity of log N. The tutorial compares this with linear search, which has a time complexity of O(N), emphasizing the efficiency of binary search. It also discusses best, worst, and average cases for binary search, concluding with a brief mention of the next lecture on implementation.

What is the primary advantage of binary search over linear search?

• It reduces the number of elements to check in each step.

• It works on unsorted arrays.

• It is easier to implement.

• It requires less memory.

How does binary search achieve a logarithmic time complexity?

• By checking each element one by one.

• By dividing the array into halves and ignoring one half each time.

• By sorting the array before searching.

• By using a hash table for faster access.

In a linear search, what is the time complexity when the target is at the last position?

• O(1)

• O(N)

• O(log N)

• O(N^2)

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

• O(N)

• O(1)

• O(log N)

• O(N^2)

In binary search, what affects the worst-case time complexity?

• The sorting algorithm used.

• The number of elements in the array.

• The size of each element.

• The initial position of the target.