1
GATE CSE 2003
MCQ (Single Correct Answer)
+1
-0.3
Ram and Shyam have been asked to show that a certain problem Π is NP-complete. Ram shows a polynomial time reduction from the 3-SAT problem to Π, and Shyam shows a polynomial time reduction from Π to 3-SAT. Which of the following can be inferred from these reductions ?
A
Π is NP-hard but not NP-complete
B
Π is in NP, but is not NP-complete
C
Π is NP-complete
D
Π is neither NP-hard, nor in NP
2
GATE CSE 2003
MCQ (Single Correct Answer)
+2
-0.6
Let G = (V, E) be a directed graph with n vertices. A path from vi to vj in G is sequence of vertices (vi, vi+1, ……., vj) such that (vk, vk+1) ∈ E for all k in i through j – 1. A simple path is a path in which no vertex appears more than once. Let A be an n x n array initialized as follow
$$$A[j,k] = \left\{ {\matrix{ {1\,if\,(j,\,k)} \cr {1\,otherwise} \cr } } \right.$$$ Consider the following algorithm.
for i = 1 to n
   for j = 1 to n
      for k = 1 to n
         A [j , k] = max (A[j, k] (A[j, i] + A [i, k]); 
Which of the following statements is necessarily true for all j and k after terminal of the above algorithm ?
A
$$A\left[ {j,{\rm{ }}k} \right]{\rm{ }} \le {\rm{ }}n$$
B
If $${\rm{A[j, k] = n }} - 1$$, then G has a Hamiltonian cycle
C
If there exists a path from j to k, A[j, k] contains the longest path lens from j to k
D
If there exists a path from j to k, every simple path from j to k contain most A[j, k] edges
3
GATE CSE 2003
MCQ (Single Correct Answer)
+2
-0.6
Let G=(V,E) be an undirected graph with a subgraph G1=(V1,E1). Weights are assigned to edges of G as follows. $$$w(e) = \begin{cases} 0 \text{, if } e \in E_1 \\1 \text{, otherwise} \end{cases}$$$ A single-source shortest path algorithm is executed on the weighted graph (V,E,w) with an arbitrary vertex v1 of V1 as the source. Which of the following can always be inferred from the path costs computed?
A
The number of edges in the shortest paths from v1 to all vertices of G
B
G1 is connected
C
V1 forms a clique in G
D
G1 is a tree
4
GATE CSE 2003
MCQ (Single Correct Answer)
+1
-0.3
The usual $$\Theta ({n^2})$$ implementation of Insertion Sort to sort an array uses linear search to identify the position where an element is to be inserted into the already sorted part of the array. If, instead, we use binary search to identify the position, the worst case running time will
A
remain $$\Theta ({n^2})$$
B
become $$\Theta (n{(\log \,n)^2})$$
C
become $$\Theta (n\log \,n)$$
D
become $$\Theta (n)$$
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