a) Define Big O notation. How is it used to describe the upper bound of an algorithm's time complexity? (Unit 1)

b) In what scenarios is worst-case analysis more relevant than average-case analysis discuss and vice versa also. (Unit 1)

a) When is it appropriate to sacrifice code readability for the sake of optimization, and when is it not? Explain. (Unit 1)

b) Explain the significance of asymptotic notations (such as Big O, Big Omega, and Big Theta) in algorithm analysis. (Unit 1)

a) Explain the heap sort algorithm step by step with suitable example. (Unit 1)

b) Discuss the key steps involved in the divide and conquer paradigm. (Unit 1)

a) Walk through the dynamic programming solution for the $0/1$ knapsack problem. (Unit 3)

b) What is the significance of overlapping sub-problems in dynamic programming? (Unit 3)

a) Introduce the Floyd-Warshall algorithm for all-pairs shortest path. (Unit 3)

b) Explain the concept of backtracking in algorithmic design. (Unit 4)

a) Describe the 8 Queen's Problem. How does backtracking help to find a solution? (Unit 4)

b) What is the Hamiltonian Cycle problem and how is it approached using backtracking? (Unit 4)

a) Describe the Traveling Salesman Problem (TSP). How is the branch and bound method applied to find an optimal solution? (Unit 4)

b) Provide an example illustrating the application of lower bound theory to a specific algebraic problem. (Unit 4)

a) What is a B-tree and what advantages does it offer over other tree structures? (Unit 5)

b) Explain the difference between P, NP and NP-complete problems. (Unit 5)