Chapter 18, Problem 56E

Explanation of Solution

Analogy of bins of complexity measures:

The Big-O notation is also called as order of notation which represents the approximate computing time of the function as a notation in the function.

• Comparative to the size of the problem, the term in a function increases.
• The complexity is nothing but the computing time.
• The highest power in the polynomial represents the Big-O complexity of algorithms.

The order of complexity is thought as bins to sort algorithms.

• Order of complexity is represented as O (1), O (log₂N), O (N), O (N log₂N), O (N²), .....