Dynamic Programming
1. The Knapsack problem where the objective function is to minimize the profit is ______
A.  Greedy B.  Dynamic 0 / 1 C.  Back tracking D.  Branch & Bound 0/1
2. For 0/1 KNAPSACK problem, the algorithm takes ________ amount of time for memory table, and ______time to determine the optimal load, for N objects and W as the capacity of KNAPSACK.
A.  O(N+W), O(NW) B.  O(NW),O(N+W) C.  O(N),O(NW) D.  O(NW),O(N)
3.

From the following choose the one which belongs to the algorithm paradigm other than to which others from the following belongs to.

A.  Minimum & Maximum problem B.  Knapsack problem C.  Selection problem D.  Merge sort
4. The optimal solution to a problem is a combination of optimal solutions to its subproblems. This is known as
A.  Principleof Duality B.  Principle of Feasibility C.  Principle of Optimality D.  Principle of Dynamicity.
5.

From the following choose the one which belongs to the algorithm paradigm other than  to which others from the following belongs to.

A.  Minimum & Maximum problem B.  Knapsack problem C.  Selection problem D.  Merge sort
6. In Knapsack problem, the best strategy to get the optimal solution, where Pi, Wi is the Profit, Weight associated with each of the Xith object respectively is to
A.  Arrange the values Pi/Wi in ascending order B.  Arrange the values Pi/Xi in ascending order C.  Arrange the values Pi/Wi in descending order D.  Arrange the values Pi/Xi in descending order
7. Find the odd one out.
A.  Merge Sort B.  TVSP Problem C.  KnapSack Problem D.  OBST Problem
8. From the following pick the one which does not belongs to the same paradigm to which others belongs to.
A.  Minimum & Maximum problem B.  Knapsack problem C.  Selection problem D.  Merge sort
9.

The method will choosing when sub problems share sub problems

A.  Divideand conquer B.  Greedy method C.  Dynamic programming D.  Back tracking
10.

which is not feasible solution in the case of job sequence problem

A.  (1,4) B.  (4,3) C.  (2,4) D.  (1,2)