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$$\scriptsize{\overset{{\large{\textbf{Mark Distribution in Previous GATE}}}}{\begin{array}{|c|c|c|c|c|c|c|c|}\hline \textbf{Year}&\textbf{2019}&\textbf{2018}&\textbf{2017-1}&\textbf{2017-2}&\textbf{2016-1}&\textbf{2016-2}&\textbf{Minimum}&\textbf{Average}&\textbf{Maximum} \\\hline\textbf{1 Mark Count}&2&0&2&2&3&3&0&2&3 \\\hline\textbf{2 Marks Count}&2&4&2&3&2&3&2&2.7&4 \\\hline\textbf{Total Marks}&6&8&6&8&7&9&\bf{6}&\bf{7.3}&\bf{9}\\\hline \end{array}}}$$

# Recent questions in Algorithms

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1
Referring back to the searching problem (see Exercise 2.1-3), observe that if the sequence $A$ is sorted, we can check the midpoint of the sequence against $v$ and eliminate half of the sequence from further consideration. The binary search algorithm repeats this procedure, ... iterative or recursive, for binary search. Argue that the worst-case running time of binary search is $\Theta (lg\ n)$.
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2
We can express the insertion sort as a recursive procedure as follows.In order to sort $A[1\dots n]$, we recursively sort $A[1 \dots n-1]$ and then insert $A[n]$ into the sorted array $A[1 \dots n-1]$. Write a recurrence for the running time of this recursive version of insertion sort.
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3
Rewrite the MERGE procedure so that it does not use sentinels, instead of stopping once either array $L$ or $R$ has had all its elements copied back to $A$ and then copying the remainder of the other array back into $A$.
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4
Using Figure $2.4$ as a model, illustrate the operation of merge sort on the array $A=\langle 3,41,52,26,38,57,9,49 \rangle$
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5
How can we modify almost any algorithm to have a good best-case running time?
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6
Consider the linear search again (see Exercise 2.1-3). How many elements of the input sequence need to be checked on the average, assuming that the element being searched for is equally likely to be any element in the array? How about in the worst case? What are the average-case and worst-case running times of linear search in ‚-notation? Justify your answers.
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7
Consider sorting $n$ numbers stored in an array $A$ by first finding the smallest element of $A$ and exchanging it with the element in $A[1]$. Then find the second smallest element of $A$, and exchange it with $A[2]$. Continue in this manner for the first ... $n-1$ elements, rather than for all n elements? Give the best-case and worst-case running times of selection sort in $\Theta$-notation
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8
Express the function $n^3/1000 -100n^2-100n+3$ in terms of $\Theta$ notation.
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9
Consider the problem of adding two $n$-bit binary integers, stored in two $n$-element arrays $A$ and $B$.The sum of the two integers should be stored in binary form in an $(n+1)$-element array $C$. State the problem formally and write pseudocode for adding the two integers.
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10
Consider the searching problem: Input: A sequence of $n$ numbers $A = \langle a_1, a_2,\dots a_n \rangle$ and a value $v$ Output: An index $i$ such that $v=A[i]$ or the special value NIL if $v$ does not appear in $A$. Write ... sequence, looking for $v$. Using a loop invariant, prove that your algorithm is correct. Make sure that your loop invariant fulfills the three necessary properties.
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11
Rewrite the INSERTION-SORT procedure to sort into non-increasing instead of non-decreasing order.
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12
Using Figure 2.2 as a model, illustrate the operation of INSERTION-SORT on the array $A =( 31, 41, 59, 26, 41, 58)$
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13
What is the smallest value of $n$ such that an algorithm whose running time is $100n^2$ runs faster than an algorithm whose running time is $2^n$ on the same machine?
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14
Suppose we are comparing implementations of insertion sort and merge sort on the same machine. For inputs of size $n$, insertion sort runs in $8n^2$ steps, while merge sort runs in $64\ n \lg n$ steps. For which values of $n$ does insertion sort beat merge sort?
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15
Give an example of an application that requires algorithmic content at the application level, and discusses the function of the algorithms involved.
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16
Come up with a real-world problem in which only the best solution will do. Then come up with one in which a solution that is “approximately” the best is good enough.
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17
How are the shortest-path and traveling-salesman problems given above similar? How are they different?
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18
Select a data structure that you have seen previously, and discuss its strengths and limitations.
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19
Other than speed, what other measures of efficiency might one use in a real-world setting?
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20
Give a real-world example that requires sorting or a real-world example that requires computing a convex hull.