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1
Minimum Spanning Tree
Multiplying all edge weights by a positive number(>1) will always change the cost of minimum spanning tree. True/False
Multiplying all edge weights by a positive number(>1) will always change the cost of minimum spanning tree.True/False
18 answers
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GATE CSE 2016 Set 1 | Question: 39
Let $G$ be a complete undirected graph on $4$ vertices, having $6$ edges with weights being $1, 2, 3, 4, 5,$ and $6$. The maximum possible weight that a minimum weight spanning tree of $G$ can have is __________
Let $G$ be a complete undirected graph on $4$ vertices, having $6$ edges with weights being $1, 2, 3, 4, 5,$ and $6$. The maximum possible weight that a minimum weight s...
4 answers
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GATE CSE 2010 | Question: 42
Consider the following schedule for transactions $T1, T2$ and $T3:$ ... correct serialization of the above? $T1 \to T3 \to T2$ $T2 \to T1 \to T3$ $T2 \to T3 \to T1$ $T3 \to T1 \to T2$
Consider the following schedule for transactions $T1, T2$ and $T3:$$$\begin{array}{|c|c|c|}\hline \textbf{T1} & \textbf{T2} & \textbf{T3} \\\hline \text{Read(X)} & \text...
10 answers
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GATE CSE 2003 | Question: 23
In a min-heap with $n$ elements with the smallest element at the root, the $7^{th}$ smallest element can be found in time $\Theta (n \log n)$ $\Theta (n)$ $\Theta(\log n)$ $\Theta(1)$
In a min-heap with $n$ elements with the smallest element at the root, the $7^{th}$ smallest element can be found in time$\Theta (n \log n)$$\Theta (n)$$\Theta(\log n)$$\...
3 answers
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7th smallest element in a Min-Heap
In a min-heap with n elements 1). The 7th smallest element can be found in time, if duplicates are allowed ? 2). The 7th distinct smallest element can be found in time, If duplicates are allowed ?
In a min-heap with n elements1). The 7th smallest element can be found in time, if duplicates are allowed ?2). The 7th distinct smallest element can be found in time, I...
4 answers
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GATE CSE 2013 | Question: 7
Which one of the following is the tightest upper bound that represents the time complexity of inserting an object into a binary search tree of $n$ nodes? $O(1)$ $O(\log n)$ $O(n)$ $O(n \log n)$
Which one of the following is the tightest upper bound that represents the time complexity of inserting an object into a binary search tree of $n$ nodes?$O(1)$$O(\log n)$...