Which of the following methods can be used to solve the longest common subsequence problem?
Which of the following methods can be used to solve the longest common subsequence problem?
Which of the following methods can be used to solve the longest common subsequence problem? Explanation: Both recursion and dynamic programming can be used to solve the longest subsequence problem.
What is optimal substructure in longest common subsequence LCS problem?
1) Optimal Substructure: And let L(X[0..m-1], Y[0..n-1]) be the length of LCS of the two sequences X and Y. Following is the recursive definition of L(X[0..m-1], Y[0..n-1]). So the LCS problem has optimal substructure property as the main problem can be solved using solutions to subproblems.
What is the time complexity of the most efficient algorithm you know for computing the longest common subsequence of two strings of lengths M and N?
O(n * m)
Since we are using two for loops for both the strings ,therefore the time complexity of finding the longest common subsequence using dynamic programming approach is O(n * m) where n and m are the lengths of the strings.
Which of the following problems should be solved using dynamic programming Mergesort binary search longest common subsequence Quicksort?
Explanation: the longest common subsequence problem has both, optimal substructure and overlapping subproblems. hence, dynamic programming should be used the solve this problem.
Which of the following is the longest common subsequence between the string?
Discussion Forum
| Que. | Which of the following is the longest common subsequence between the strings “hbcfgmnapq” and “cbhgrsfnmq”? |
|---|---|
| b. | cfnq |
| c. | bfmq |
| d. | all of the mentioned |
| Answer:all of the mentioned |
What is the time complexity of longest common subsequence algorithm?
The general algorithms which are followed to solve the Longest Common Subsequence (LCS) problems have both time complexity and space complexity of O(m * n). To reduce this complexity, two ways are proposed in this work.
What is the application of the longest common Subseques?
The longest common subsequence problem is a classic computer science problem, the basis of data comparison programs such as the diff utility, and has applications in computational linguistics and bioinformatics.
When dynamic programming is applied to a problem it takes far less time as compared to other methods?
So, dynamic programming saves the time of recalculation and takes far less time as compared to other methods that don’t take advantage of the overlapping subproblems property.