1. Data Structures and Algorithms (AT70.02) Comp. Sc. and Inf. Mgmt. Asian Institute of Technology Instructor: Prof. Sumanta Guha Slide Sources: CLRS “Intro. To Algorithms” book website (copyright McGraw Hill) adapted and supplemented

2. CLRS “Intro. To Algorithms” Ch. 8: Sorting in Linear Time

3. Theorem 8.1: Any comparison sort algorithm requires  (n log n) comparisons in the worst case. Proof: The decision tree must have n! leaves, one corresponding to each possible output. Moreover, the branching-factor of the tree is two, i.e., the tree is binary, because each comparison gives one of two results. The height of the decision tree is the number of comparisons made in the worst case. Therefore, the minimum possible height of a binary tree with n! leaves is the number of comparisons in the worst case. Min. possible ht. = log (n!) =  (n logn) (using Stirling’s approximation).

4. COUNTING-SORT assumes that each of the input elements is an integer in the range 0 to k, inclusive. A [1..n] is the input array, B [1..n] is the output array, C [0..k] is a temporary working array.

5. The running time of COUNTING-SORT is  (k+n) (why?), which is  (n) if k ≤ n. Intuitively, explain why COUNTING-SORT can beat the lower bound of  (n logn) for comparison sorts. Doesn’t it do comparisons as well…!?

6. A stable sort is one where items with the same value appear in the same order in the output as they appeared in the input. Lemma 8.3: Given n d-digit numbers in which each digit can take up to k possible values, RADIX-SORT correctly sorts these numbers in  (d (n +k)) time. Proof: COUNTING-SORT on each column * no. of columns Is COUNTING-SORT stable?!!

7. BUCKET-SORT assumes each input element A [i ] lies in 0 ≤ A [i ] < 1 (i.e., normalized). A [1..n] is the input array, B [0..n-1] is an auxiliary array of linked lists (=buckets).

8. Analysis of BUCKET-SORT The running time is T(n) =  (n) + ∑ i=0..n-1 O(n i 2 ) where n i is the random variable representing the number of elements in bucket B[i], because insertion sort is quadratic-time. Therefore, E[T(n)] = E[  (n) + ∑ i=0..n-1 O(n i 2 ) ] =  (n) + ∑ i=0..n-1 E[O(n i 2 )] =  (n) + ∑ i=0..n-1 O(E[n i 2 ]) Claim : E[n i 2 ] = 2 – 1/n If the claim is proved: E[T(n)] =  (n) + ∑ i=0..n-1 O(2 – 1/n) =  (n) + n * O(2 – 1/n) =  (n)

9. To prove the claim E[n i 2 ] = 2 – 1/n, let the indicator variable X ij = I{A[j] falls in bucket i} for i = 0, …, n-1 and j = 1, 2, …, n. Therefore, n i = ∑ j=1..n X ij and E[n i 2 ] = E[ (∑ j=1..n X ij ) 2 ] = ∑ j=1..n E[X ij 2 ] + ∑ j=1..n ∑ k=1..n & k≠j E[X ij X ik ] Now, X ij is 1 with probability 1/n and 0 with probability 1 – 1/n. Therefore, E[X ij 2 ] = 1 2 * 1/n + 0 2 * (1 – 1/n) = 1/n When k ≠ j, X ij and X ik and independent, so E[X ij X ik ] = E[X ij ] * E[X ik ] = 1/n * 1/n = 1/n 2 Substituting above: E[n i 2 ] = ∑ j=1..n 1/n + ∑ j=1..n ∑ k=1..n & k≠j 1/n 2 = n * 1/n + n(n-1) 1/n 2 = 1 + (n-1)/n = 2 – 1/n

10. Problems Ex. 8.1-4 You are given a sequence of n elements to sort. The input sequence consists of n/k subsequences, each containing k elements. The elements in a given subsequence are all smaller than the elements in the succeeding subsequence and larger than the elements in the preceding subsequence. Thus, all that is needed to sort the whole sequence of length n is to sort the k elements in each of the n/k subsequences. Show an (n lg k) lower bound on the number of comparisons needed to solve this variant of the sorting problem. (Hint: It is not rigorous to simply combine the lower bounds for the individual subsequences.) Ex. 8.2-1 Ex. 8.2-2 Ex. 8.2-3 Ex. 8.2-4

11. Problems Ex. 8.3-1 Ex. 8.3-2 Ex. 8.4-1 Ex. 8.4-2 Ex. 8.4-3 Let X be a random variable that is equal to the number of heads in two flips of a fair coin. What is E [X 2 ]? What is E 2 [X]? Prob. 8-4 (see next slide) Prob. 8-6

12. MATCH-JUGS(R, B) if |R| = 0 // Sets are empty then return if |R| = 1 // Sets contain just one jug each then let R = {r} and B = {b} output “(r, b)” return else r ← a randomly chosen jug in R compare r to every jug of B B < ← the set of jugs in B that are smaller than r B > ← the set of jugs in B that are larger than r b ← the one jug in B with the same size as r compare b to every jug of R − {r} R < ← the set of jugs in R that are smaller than b R > ← the set of jugs in R that are larger than b output “(r, b)” MATCH-JUGS(R <, B < ) MATCH-JUGS(R >, B > ) Solution to Ex. 8-4 c: