1. Median, order statistics

2. Problem Find the i-th smallest of n elements.  i=1: minimum  i=n: maximum  i= or i= : median Sol: sort and index the i-th element. Ο(nlgn): worst case.

3. Randomized algorithm: (9.2) Divide & Conquer ≤A[q]≥A[q] k pq r

4. Analysis: Lucky case: T(n) ≤ T( ) + Θ(n) = Θ(n). By case 3 of master method (chap 4) Unlucky case: T(n) = T(n-1) + Θ(n) = Θ(n 2 ).

5. Analysis:

6. Average case: For upper bound, assume the i-th element always falls in larger side of partition.

7. Solve By substitution method: assume T(n) ≤ cn. We can pick c large enough so that c(n/4+1/2) dominates the Ο(n) term.

8. Worst case linear-time order statistics (9.3) Theoretical interest Idea: generate a good partitioning element x.

9. Select (i) { Divide n elements into groups of 5 elements. Find the median of each group of 5. Use Select recursively to find the median x of the medians. Partition the n elements around x Let k = rank of x. 5. If i=k then return x. If i<k then use Select recursively to find the i-th smallest in the 1st part else use Select recursively to find (i-k)th smallest in the last part. }

11. Analysis: At least ½ of 5-element medians ≤ x, which is at least medians. At least elements ≤ x. For n ≥ 50, Similarly, at least n/4 elements ≥ x. Thus after partitioning around x, step 5 is called on ≤ 3n/4 elements.

12. T(n) ≤ T(n/5) + T(3n/4) + Θ(n). By substitution: T(n) ≤ cn

13. Application of linear-time median algorithm: i-th order statistic: {  Find median x  Partition input around x  If i≤ then recursively find the i-th element in the first half. else recursively find (i- )th element in the 2nd half. } T(n) = T(n/2) + Θ(n) = Θ(n).

14. Worst-case Θ(nlgn) quicksort: { Find median x and partition; Recursively sort 2 halves; } T(n) = 2T(n/2) + Θ(n) = Θ(nlgn).