Bt0080 - Fundamentals Of Algorithms
A set of rules to be followed during calculations or other problem solving operations is what defines algorithm. Computational complexity for instance defines clearly the major concern of algorithm, which is efficiency, which involves how much time and memory required to solve problem. Bt0080 - Fundamentals of algorithms will help you understand what algorithm is all about.
- 1.A process is a ………….. actually being carried out to solve a problem.
- A.
Sequence of activities
- B.
Step-by step procedure
- C.
Flow of information
- D.
Both b and c
- 2.An algorithm must terminate after a finite number of steps is known as
- A.
Definiteness
- B.
Finiteness
- C.
Correctness
- D.
Effectiveness
- 3.When the sequence of execution of instructions is to be the same as the sequence in which instruction are written in program text is known as ………..
- A.
Direct sequencing
- B.
Indirect sequencing
- C.
Direct selection
- D.
Indirect selection
- 4.Each of the floor function and ceiling function is a monotonically increasing function but not ……………..
- A.
Strictly monotonically increasing function
- B.
Monotonically decreasing function
- C.
Strictly monotonically decreasing function
- D.
Mod function
- 5.………………….maps each real number x to the integer, which is the greatest of all integers less than or equal to
- A.
Ceiling function
- B.
Floor Function
- C.
Monotonically increasing function
- D.
Exponentiation Function
- 6.The concept of logarithm is defined indirectly by the definition of ……………
- A.
Exponential
- B.
Floor Function
- C.
Ceiling function
- D.
Monotonically increasing function
- 7.
![- ProProfs]()
- A.
A
- B.
B
- C.
C
- D.
D
- 8.Merge sort is an example of
- A.
Divide and conquer
- B.
Decrease and conquer
- C.
Space and time tradeoff
- D.
Greedy method
- 9.Before executing Merge Sort, the n elements should be placed in a [1:n].
- A.
[1:n]
- B.
[2n:n]
- C.
[1:2n]
- D.
[2:n]
- 10.A feasible solution that either maximizes or minimizes a given objective function is called an …………..
- A.
Optimal solution
- B.
Local solution
- C.
Exact solution
- D.
Correct solution correct solution correct solution
- 11.Knapsack Problem is an example of
- A.
Divide and conquer technique
- B.
Greedy technique
- C.
Decrease and conquer technique
- D.
Time and space tradeoff
- 12.In a graph, when does Dijkstra's algorithm stop?
- A.
When the shortest path to the destination vertex is found
- B.
When all the vertices in the graph are included to the path
- C.
When the vertices together form a cycle
- D.
When the minimum spanning tree is constructed
- 13.The multistage graph problem can also be solved using the ……………….
- A.
Backward approach
- B.
Forward approach
- C.
Top down approach
- D.
Bottoms up approach
- 14.A multistage graph G = (V,E) is a ……………….
- A.
Undirected graph
- B.
Directed graph
- C.
Directed as well as undirected graph
- D.
Type of tree
- 15.The all pairs shortest path problem is to determine a …………….such that A (i,j) is the length of a shortest path from i to j.
- A.
Matrix A
- B.
Array A
- C.
Graph A
- D.
Tree A
- 16.All solutions to the 8-queens problem can be represented as ………………. where xi is the column on which queen i is placed.
- A.
8-tuples (x1…… x8),
- B.
12-tuples (x1…… x12),
- C.
16-tuples (x1…… x16),
- D.
4-tuples (x1…… x4),
- 17.The estimated no. of unbounded nodes is only ………... of the total no of nodes in the 8 queen state space tree.
- A.
3.34%
- B.
2.34%
- C.
1%
- D.
4.34%
- 18.Backtracking algorithms determine problem's solutions by …………………... searching the solution space for the given problem instance
- A.
Systematically
- B.
Logically
- C.
Periodically
- D.
Non- systematically
- 19.In branch-and-bound terminology, a BFS-like state space search will be called ……….
- A.
FIFO
- B.
LIFO
- C.
LILO
- D.
FILO
- 20.A D-search-like state space search will be called ………….
- A.
FIFO
- B.
LIFO
- C.
FILO
- D.
LILO
- 21.To use the branch-and-bound technique to solve any problem, first, it is necessary to conceive of a …………... for the problem
- A.
State space tree
- B.
Binary search tree
- C.
Syntax tree
- D.
Flowchart
- 22.If f(n) is the time for some algorithm, then we write f(n)=Ω(g(n)) to mean that g(n) is a …………. for f(n).
- A.
Lower bound
- B.
Upper bound
- C.
Space complexity
- D.
Average bound
- 23.For many problems it is possible to easily observe that a lower bound ………………..to n exists where n is the number of input to the problem
- A.
Descendent
- B.
Identical
- C.
Differ
- D.
Local
- 24.Each internal node in binary tree represents a …………
- A.
Comparison
- B.
Differentiation
- C.
Multiplication
- D.
Division
- 25.An edge having the same vertex as both its end vertices called a ……………
- A.
Self-loop
- B.
Open loop
- C.
Closed vertex
- D.
Open vertex Open vertex Open vertex
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