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    consider the strings “pqrstpqrs” and “pratpbrqrps”. what is the length of the longest common subsequence?

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    Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is the length of the longest common subsequence?

    Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is the length of the longest common subsequence? 9 8 7 6. Data Structures and Algorithms Objective type Questions and Answers.

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    Que. Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is the length of the longest common subsequence?

    a. 9 b. 8 c. 7 d. 6 Answer:7

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    Longest Common Subsequence Questions and Answers

    This set of Data Structure Multiple Choice Questions & Answers (MCQs) focuses on “Longest Common Subsequence”. 1. Which of the following methods can be used to solve the longest common subsequence problem? a) Recursion b) Dynamic programming c) Both recursion and dynamic programming d) Greedy algorithm 2. Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is ... Read more

    Data Structure Questions and Answers – Longest Common Subsequence

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    This set of Data Structure Multiple Choice Questions & Answers (MCQs) focuses on “Longest Common Subsequence”.

    1. Which of the following methods can be used to solve the longest common subsequence problem?

    a) Recursion

    b) Dynamic programming

    c) Both recursion and dynamic programming

    d) Greedy algorithm View Answer

    2. Consider the strings “PQRSTPQRS” and “PRATPBRQRPS”. What is the length of the longest common subsequence?

    a) 9 b) 8 c) 7 d) 6 View Answer

    3. Which of the following problems can be solved using the longest subsequence problem?

    a) Longest increasing subsequence

    b) Longest palindromic subsequence

    c) Longest bitonic subsequence

    d) Longest decreasing subsequence

    View Answer

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    4. Longest common subsequence is an example of ____________

    a) Greedy algorithm

    b) 2D dynamic programming

    c) 1D dynamic programming

    d) Divide and conquer

    View Answer

    5. What is the time complexity of the brute force algorithm used to find the longest common subsequence?

    a) O(n) b) O(n2) c) O(n3) d) O(2n) View Answer

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    6. Consider the following dynamic programming implementation of the longest common subsequence problem:

    #include#include

    int max_num(int a, int b)

    { if(a > b) return a; return b; }

    int lcs(char *str1, char *str2)

    { int i,j,len1,len2;

    len1 = strlen(str1);

    len2 = strlen(str2);

    int arr[len1 + 1][len2 + 1];

    for(i = 0; i <= len1; i++)

    arr[i][0] = 0;

    for(i = 0; i <= len2; i++)

    arr[0][i] = 0;

    for(i = 1; i <= len1; i++)

    {

    for(j = 1; j <= len2; j++)

    {

    if(str1[i-1] == str2[j - 1])

    ______________; else

    arr[i][j] = max_num(arr[i - 1][j], arr[i][j - 1]);

    } }

    return arr[len1][len2];

    } int main() {

    char str1[] = " abcedfg", str2[] = "bcdfh";

    int ans = lcs(str1,str2);

    printf("%d",ans); return 0; }

    Which of the following lines completes the above code?

    a) arr[i][j] = 1 + arr[i][j].

    b) arr[i][j] = 1 + arr[i – 1][j – 1].

    c) arr[i][j] = arr[i – 1][j – 1].

    d) arr[i][j] = arr[i][j].

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    7. What is the time complexity of the following dynamic programming implementation of the longest common subsequence problem where length of one string is “m” and the length of the other string is “n”?

    #include

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    #include

    int max_num(int a, int b)

    { if(a > b) return a; return b; }

    int lcs(char *str1, char *str2)

    { int i,j,len1,len2;

    len1 = strlen(str1);

    len2 = strlen(str2);

    int arr[len1 + 1][len2 + 1];

    for(i = 0; i <= len1; i++)

    arr[i][0] = 0;

    for(i = 0; i <= len2; i++)

    arr[0][i] = 0;

    for(i = 1; i <= len1; i++)

    {

    for(j = 1; j <= len2; j++)

    {

    if(str1[i-1] == str2[j - 1])

    arr[i][j] = 1 + arr[i - 1][j - 1];

    else

    arr[i][j] = max_num(arr[i - 1][j], arr[i][j - 1]);

    } }

    return arr[len1][len2];

    } int main() {

    char str1[] = " abcedfg", str2[] = "bcdfh";

    int ans = lcs(str1,str2);

    printf("%d",ans); return 0; } a) O(n) b) O(m) c) O(m + n) d) O(mn) View Answer

    8. What is the space complexity of the following dynamic programming implementation of the longest common subsequence problem where length of one string is “m” and the length of the other string is “n”?

    #include#include

    int max_num(int a, int b)

    { if(a > b) return a; return b; }

    int lcs(char *str1, char *str2)

    { int i,j,len1,len2;

    len1 = strlen(str1);

    len2 = strlen(str2);

    int arr[len1 + 1][len2 + 1];

    for(i = 0; i <= len1; i++)

    arr[i][0] = 0;

    for(i = 0; i <= len2; i++)

    arr[0][i] = 0;

    for(i = 1; i <= len1; i++)

    {

    for(j = 1; j <= len2; j++)

    {

    if(str1[i-1] == str2[j - 1])

    arr[i][j] = 1 + arr[i - 1][j - 1];

    else

    arr[i][j] = max_num(arr[i - 1][j], arr[i][j - 1]);

    } }

    return arr[len1][len2];

    } int main() {

    char str1[] = " abcedfg", str2[] = "bcdfh";

    int ans = lcs(str1,str2);

    printf("%d",ans); return 0; } a) O(n) b) O(m) c) O(m + n) d) O(mn) View Answer

    9. What is the output of the following code?

    #include#include

    int max_num(int a, int b)

    { if(a > b) return a; return b; }

    int lcs(char *str1, char *str2)

    { int i,j,len1,len2;

    len1 = strlen(str1);

    len2 = strlen(str2);

    int arr[len1 + 1][len2 + 1];

    for(i = 0; i <= len1; i++)

    arr[i][0] = 0;

    for(i = 0; i <= len2; i++)

    arr[0][i] = 0;

    for(i = 1; i <= len1; i++)

    {

    for(j = 1; j <= len2; j++)

    {

    if(str1[i-1] == str2[j - 1])

    स्रोत : www.sanfoundry.com

    PT 2 Flashcards

    Study with Quizlet and memorize flashcards containing terms like Which of the following methods can be used to solve the longest common subsequence problem? a) Recursion b) Dynamic programming c) Both recursion and dynamic programming d) Greedy algorithm, Consider the strings "PQRSTPQRS" and "PRATPBRQRPS". What is the length of the longest common subsequence? a) 9 b) 8 c) 7 d) 6, Which of the following problems can be solved using the longest subsequence problem? a) Longest increasing subsequence b) Longest palindromic subsequence c) Longest bitonic subsequence d) Longest decreasing subsequence and more.

    PT 2

    Term 1 / 60

    Which of the following methods can be used to solve the longest common subsequence problem?

    a) Recursion

    b) Dynamic programming

    c) Both recursion and dynamic programming

    d) Greedy algorithm

    Click the card to flip 👆

    Definition 1 / 60 c

    Click the card to flip 👆

    Created by Viet_Tung_N

    Terms in this set (60)

    Which of the following methods can be used to solve the longest common subsequence problem?

    a) Recursion

    b) Dynamic programming

    c) Both recursion and dynamic programming

    d) Greedy algorithm c

    Consider the strings "PQRSTPQRS" and "PRATPBRQRPS". What is the length of the longest common subsequence?

    a) 9 b) 8 c) 7 d) 6 c

    Which of the following problems can be solved using the longest subsequence problem?

    a) Longest increasing subsequence

    b) Longest palindromic subsequence

    c) Longest bitonic subsequence

    d) Longest decreasing subsequence

    b

    Longest common subsequence is an example of ____________

    a) Greedy algorithm

    b) 2D dynamic programming

    c) 1D dynamic programming

    d) Divide and conquer

    b

    What is the time complexity of the brute force algorithm used to find the longest common subsequence?

    a) O(n) b) O(n^2) c) O(n^3) d) O(2^n) d

    Which of the following is the longest common subsequence between the strings "hbcfgmnapq" and "cbhgrsfnmq" ?

    a) hgmq b) cfnq c) bfmq d) fgmna d

    Which of the following is/are property/properties of a dynamic programming problem?

    a) Optimal substructure

    b) Overlapping subproblems

    c) Greedy approach

    d) Both optimal substructure and overlapping subproblems

    d

    If an optimal solution can be created for a problem by constructing optimal solutions for its subproblems, the problem possesses ____________ property.

    a) Overlapping subproblems

    b) Optimal substructure

    c) Memoization d) Greedy b

    If a problem can be broken into subproblems which are reused several times, the problem possesses ____________ property.

    a) Overlapping subproblems

    b) Optimal substructure

    c) Memoization d) Greedy a

    If a problem can be solved by combining optimal solutions to non-overlapping problems, the strategy is called _____________

    a) Dynamic programming

    b) Greedy

    c) Divide and conquer

    d) Recursion c

    When dynamic programming is applied to a problem, it takes far less time as compared to other methods that don't take advantage of overlapping subproblems.

    a) True b) False a

    A greedy algorithm can be used to solve all the dynamic programming problems.

    a) True b) False b

    In dynamic programming, the technique of storing the previously calculated values is called ___________

    a) Saving value property

    b) Storing value property

    c) Memoization d) Mapping c

    When a top-down approach of dynamic programming is applied to a problem, it usually _____________

    a) Decreases both, the time complexity and the space complexity

    b) Decreases the time complexity and increases the space complexity

    c) Increases the time complexity and decreases the space complexity

    d) Increases both, the time complexity and the space complexity

    b

    Which of the following problems is NOT solved using dynamic programming?

    a) 0/1 knapsack problem

    b) Matrix chain multiplication problem

    c) Edit distance problem

    d) Fractional knapsack problem

    d

    Which of the following problems should be solved using dynamic programming?

    a) Mergesort b) Binary search

    c) Longest common subsequence

    d) Quicksort c

    Which of the following methods can be used to solve the matrix chain multiplication problem?

    a) Dynamic programming

    b) Brute force c) Recursion

    d) Dynamic Programming, Brute force, Recursion

    d

    Which of the following is the recurrence relation for the matrix chain multiplication problem where mat[i-1] * mat[i] gives the dimension of the ith matrix?

    a) dp[i,j] = 1 if i=j

    dp[i,j] = min{dp[i,k] + dp[k+1,j]}

    b) dp[i,j] = 0 if i=j

    dp[i,j] = min{dp[i,k] + dp[k+1,j]}

    c) dp[i,j] = 1 if i=j

    dp[i,j] = min{dp[i,k] + dp[k+1,j]} + mat[i-1]mat[k]mat[j].

    d) dp[i,j] = 0 if i=j

    dp[i,j] = min{dp[i,k] + dp[k+1,j]} + mat[i-1]mat[k]mat[j].

    d

    Consider the two matrices P and Q which are 10 x 20 and 20 x 30 matrices respectively. What is the number of multiplications required to multiply the two matrices?

    a) 10*20 b) 20*30 c) 10*30 d) 102030 d

    Consider the matrices P, Q and R which are 10 x 20, 20 x 30 and 30 x 40 matrices respectively. What is the minimum number of multiplications required to multiply the three matrices?

    a) 18000 b) 12000 c) 24000 d) 32000 a

    Consider the matrices P, Q, R and S which are 20 x 15, 15 x 30, 30 x 5 and 5 x 40 matrices respectively. What is the minimum number of multiplications required to multiply the four matrices?

    a) 6050 b) 7500 c) 7750 d) 12000 c

    Consider the brute force implementation in which we find all the possible ways of multiplying the given set of n matrices. What is the time complexity of this implementation?

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    Mohammed 6 day ago
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