1. Chapter 9: Selection Order Statistics What are an order statistic? min, max median, i th smallest, etc. Selection means finding a particular order statistic Selection by sorting T(n) =  (nlgn) Selection in linear time best case worst case average case

2. Given a set of n elements, i th order statistic = i th smallest element min is 1 st order statistic; max is the n th order statistic parity of a set is whether n is even or odd median is roughly half way between min and max unique for an odd parity set i th smallest with i = (n+1)/2 regardless of parity lower median means i th smallest with i =  (n+1)/2  upper median means i th smallest with i =  (n+1)/2  Min, Max and Median order statistics

3. Find the i th order statistic in set of n (distinct) elements A= (i.e. find x  A that x is larger than exactly i –1 other elements of A) Selection problem can be solve in O(nlgn) by sorting Since min and max can be found in linear time, expect that any order statistic can be found in linear time. Analyze deterministic selection algorithm with O(n) in worst case. Analyze randomized selection by partition: O(n) in average case Selection problem

4. Selection algorithm with worst-case runtime = O(n) Possible to design a deterministic selection algorithm that has a linear worst-case runtime. Making the pivot an input parameter, can guarantee a good split when partition is called Processing before calling partition determines a good choice for pivot.

5. Outline of recursive Select with worst-case runtime = O(n): Step 1: Divide n-element sequence into flour(n/5) groups of at most 5 elements (one may have less than 5) cost =  (n) Step 2: Use insertion sort to find median of each subgroup cost = constant times number of subgroups =  (n) Step 3: Use Select to find the median of the medians cost = T(ceiling(n/5)) Step 4: Partition the input array with pivot = median of medians Calculate the number of elements in the lower sub-array cost =  (n) + constant Step 5: If pivot is not the i th smallest element, bound the runtime by the time to Select from the larger sub-array cost < T(7n/10 + 6)

6. Diagram to help explain cost of Step 5 Dots represent elements of input. Subgroups of 5 occupy columns Arrows point from larger to smaller elements. Medians are white. x marks median of medians. Shaded area shows elements greater than x 3 out of 5 are shaded if subgroup is full and does not contain x

7. At least 3[(1/2)(n/5) – 2] elements larger than x At most {n - 3[(1/2)(n/5) – 2]} = 7n/10+6 elements less than x Worst case described by T(n) = T(ceiling(n/5)) + T(7n/10+6) +  (n) Solve by substitution method

8. CS 450 Spring 2015 [All problems are from Cormen et al, 3 rd Edition] Homework Assignment 8: due 3/13/15 1. ex 9.3-1 p 223 (groups of 7 and 3) 2. ex 9.3-3 p 223 3. ex 9.3-5 p 223 On problems 2 and 3, Write a pseudo code (variation of code in text) Explain how code works Analyze its run time

9. Select-by-Partition(A,p,r,i) 1if p=r then return A[p] (single element is i th smallest by default) 2q  Partition(A,p,r) (get upper and lower sub-arrays) 3k  q – p + 1 (number of elements in lower including pivot) 4if i = k then 5return A[q] (pivot is the i th smallest element) 6else 7if i < k then return Select-by-Partition(A,p,q-1,i) 8else 9return Select-by-Partition(A,q+1,r,i - k) Select by partition pseudocode Note: index of i th order statistic changed in upper sub-array With favorable splits, T(n) = O(n) Why not O(nlg(n)) as in quicksort?

10. Randomized-Select lets us analyze the runtime for the average case Randomized-Select(A,p,r,i) 1if p=r then return A[p] 2q  Randomized-Partition(A,p,r) 3k  q – p + 1 4if i = k then 5return A[q] (pivot is the i th smallest element) 6else 7if i < k then return Randomized-Select(A,p,q-1,i) 8else 9return Randomized-Select(A,q+1,r,i –k) As in Randomized-Quicksort, Randomized-Partition chooses a pivot at random from array elements between p and r

11. Upper bound on the expected value of T(n) for Randomized-Select Call to Randomized-Partition creates upper and lower sub-arrays Include the pivot in lower sub-array A(p..q) Define indicator random variables X k = I{sub-array A[p...q]} has exactly k elements} 1 < k < n All possibilities values of k are equally likely. E[X k ] = 1/n

12. Assume that the desired element always falls in larger partition This assumption ensures an upper bound on E(T(n)) T(n) < {X k T(max(k-1,n-k))} + O(n) Sum contains only one nonzero term T(n) = T(n-1) + O(n) when lower sub-array has 1 element T(n) = T(n-2) + O(n) when lower sub-array has 2 element. T(n) = T(n-2) + O(n) when lower sub-array has n-1 element T(n) = T(n-1) + O(n) when lower sub-array has n element

13. E[T(n)] < { E[X k T(max(k-1,n-k))] } + O(n) (linearity of expected values) E[T(n)] < { E[X k ] E[ T(max(k-1,n-k))] } + O(n) (independence of random variables, exercise 9.2-2) E[T(n)] < (1/n) E[ T(max(k-1,n-k))] + O(n) (using E[X k ] = 1/n)

14. E[T(n)] < (1/n) E[ T(max(k-1,n-k))] + O(n) if k >  n/2 , max(k-1,n-k) = k-1 if k <  n/2 , max(k-1,n-k) = n-k For even n, each term from T(n/2) to T(n-1) occurs exactly twice Similar argument applies for odd n E[T(n)] < (2/n) E[ T(k)] + O(n) (using the redundancy of T’s) E[T(n)] < (2/n) { E[ T(k)] - E[ T(k)] } + O(n) (Get setup to use the arithmetic sum)

15. Apply substitution method: assume E[T(k)] = O(k) Then exist c > 0 such that E[T(k)] < ck E[T(n)] 0 Now use arithmetic sum After much algebra (text p219) E[T(n)] < cn – (cn/4 – c/2 – dn) Find c and n 0

17. simplify (see text p219) E[T(n)] < cn – (cn/4 – c/2 – dn) E[T(n)] 0 n(c/4 –d) > c/2 If c > 4d, n sufficient large does exist If c = 8d, than n > 4