Asymptotic Notations Quiz

Theta Notation

• Represents tight bound on the time complexity of an algorithm.
• Indicates the algorithm's runtime grows at the same rate as the input size.

Big O Notation

• Provides upper bound on the time complexity.
• Shows worst-case runtime of an algorithm.

Big Omega Notation

• Represents lower bound on the time complexity.
• Shows best-case runtime of an algorithm.

Little O Notation

• Used when an algorithm's runtime grows strictly slower than the function specified.

Little Omega Notation

• Indicates an algorithm's runtime grows strictly faster than the function specified.

Asymptotic Notations

• Provide mathematical framework to analyze algorithm performance.
• Help compare efficiency of different algorithms.

Big O Notation (Time Complexity)

• Describes how runtime of an algorithm grows with input size.
• Used to categorize algorithms based on their growth rates.

Big Omega Notation (Algorithm Analysis)

• Represents minimum amount of time an algorithm will take to execute.
• Useful for understanding algorithm's efficiency in best-case scenarios.

Theta Notation (Algorithm Analysis)

• Represents exact rate of growth of an algorithm's runtime.
• Gives a precise understanding of algorithm's performance.

Time Complexity (Big O Notation)

• Represented using mathematical expressions like O(1) (constant time), O(n) (linear time), O(log n) (logarithmic time), O(n^2) (quadratic time), etc.
• Provides a generalized way to compare algorithm efficiency.