Given below are some algorithms, and some algorithm design paradigms.

GROUP 1	GROUP 2
1. Dijkstra's Shortest Path	i. Divide and Conquer
2. Floyd-Warshall algorithm to compute all pairs shortest path	ii. Dynamic Programming
3. Binary search on a sorted array	iii. Greedy design
4. Backtracking search on a graph	iv. Depth-first search
	v. Breadth-first search

Match the above algorithms on the left to the corresponding design paradigm they follow.

Consider two strings A = “qpqrr” and B = “pqprqrp”. Let x be the length of the longest common subsequence (not necessarily contiguous) between A and B and let y be the number of such longest common subsequences between A and B. Then x + 10y = ___.

Four matrices M₁, M₂, M₃ and M₄ of dimensions p $$\times$$ q, q $$\times$$ r, r $$\times$$ s and s $$\times$$ t respectively can be multiplied is several ways with different number of total scalar multiplications. For example, when multiplied as ((M₁ $$\times$$ M₂) $$\times$$ (M₃ $$\times$$ M₄)), the total number of multiplications is pqr + rst + prt. When multiplied as (((M₁ $$\times$$ M₂) $$\times$$ M₃) $$\times$$ M₄), the total number of scalar multiplications is pqr + prs + pst.

If p = 10, q = 100, r = 20, s = 5 and t = 80, then the number of scalar multiplications needed is:

A sub-sequence of a given sequence is just the given sequence with some elements (possibly none or all) left out. We are given two sequences X[m] and Y[n] of lengths m and n, respectively with indexes of X and Y starting from 0.

We wish to find the length of the longest common sub-sequence (LCS) of X[m] and Y[n] as l(m,n), where an incomplete recursive definition for the function I(i,j) to compute the length of the LCS of X[m] and Y[n] is given below:

l(i,j)  = 0, if either i = 0 or j = 0
        = expr1, if i,j > 0 and X[i-1] = Y[j-1]
        = expr2, if i,j > 0 and X[i-1] ≠ Y[j-1]

The value of l(i, j) could be obtained by dynamic programming based on the correct recursive definition of l(i, j) of the form given above, using an array L[M, N], where M = m+1 and N = n + 1, such that L[i, j] = l(i, j).

Which one of the following statements would be TRUE regarding the dynamic programming solution for the recursive definition of l(i, j)?