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Randomized Algorithms

Randomized Algorithms. CSc 4520/6520 Design & Analysis of Algorithms Fall 2013 Slides adopted from Dmitri Kaznachey, George Mason University and Maciej Ciesielski, University of Massachusetts. The Hiring Problem. The goal is to hire a new assistant through an employment agency.

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Randomized Algorithms

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  1. Randomized Algorithms CSc 4520/6520 Design & Analysis of Algorithms Fall 2013 Slides adopted from Dmitri Kaznachey, George Mason University and Maciej Ciesielski, University of Massachusetts

  2. The Hiring Problem • The goal is to hire a new assistant through an employment agency. • The agency sends one candidate each day. • The commitment is to have the best person to do the job. • When the interviewed person is better than the current assistant, he/she is hired in place of the current one. • There is a small cost to pay for the interview. • There is usually a larger cost associated with the fire/hire process.

  3. The Hiring Problem: Algorithm • Hire-Assistant (n) • 1 best = 0 // candidate 0 is least qualified • 2 for i = 1 to n • <interview candidate i> • 4 ifi is better than best • 5 best = i • 6 <hire candidate i> • Assume interviewing has cost ci, whereas more expensive hiring has cost ch. Let m be the number of people hired. Then the cost of the above algorithm is: • O(nci + mch) • The quantity m varies with each run and determines the overall cost of the algorithm. It is estimated using probabilistic analysis.

  4. Indicator Random Variables Assume sample space S and an event A. The indicator random variable I{A} is defined as 1, if A occurs I{A} = 0, otherwise Given a sample space S and an event A, denote XA a random variable associated with an event being A, i.e. XA = I{A}. The the expected value of XA is: E[XA] = Pr{A} Proof. E[XA] = E[I{A}] = 1Pr{A} + 0Pr{A} = Pr{A}

  5. The Hiring Problem: Analysis Let Xi be the indicator random variable associated with the event that the candidate i is hired: Xi = I{candidate i is hired} Let X be the random variable whose value equals the number of time we hire a new candidate: X = X1 + ... + Xn Note that E[Xi] = Pr{Xi} = Pr{candidate i is hired}. We now need to compute Pr{candidate i is hired}.

  6. The Hiring Problem: Analysis (cont.) Candidate i is hired (line 5) when it is better than any of the previous (i-1) candidates. Since all candidates arrive in random order, each of them have the same probability of being the best so far. Therefore: E[Xi] = Pr{Xi} = 1/i We can now compute E[X]: E[X] = E[X1 + ... + Xn] = 1 + ½ + ... + 1/n = ln(n) + O(1) Hence, when candidates are presented in random order, the algorithm Hire-Assistant has a total hiring cost: O(ch ln(n))

  7. Randomized Algorithms In a randomized algorithm the distribution of inputs is imposed. In particular, in the randomized version of the Hire-Assistant algorithm we randomly permute the candidates: Randomized-Hire-Assistant (n) 1 <randomly permute the list of candidates> 2 best = 0 // candidate 0 is least qualified 3 for i = 1 to n 4 <interview candidate i> 5 ifi is better than best 6 best = i 7 <hire candidate i> According to the earlier computations, the expected cost of the above algorithm is O(nci+chln(n)).

  8. Randomized Version Want to make running time independent of input ordering. Randomized-Quicksort(A, p, r) if p < r then q := Randomized-Partition(A, p, r); Randomized-Quicksort(A, p, q – 1); Randomized-Quicksort(A, q + 1, r) fi Randomized-Partition(A, p, r) i := Random(p, r); A[r]  A[i]; Partition(A, p, r)

  9. 7 4 9 6 2 2 4 6 7 9 4 2 2 4 7 9 7 9 2 2 9 9 Quick-Sort

  10. Quick-sort is a randomized sorting algorithm based on the divide-and-conquer paradigm: Divide: pick a random element x (called pivot) and partition S into L elements less than x E elements equal x G elements greater than x Recur: sort L and G Conquer: join L, Eand G Quick-Sort x x L G E x

  11. Partition Algorithmpartition(S,p) Inputsequence S, position p of pivot Outputsubsequences L,E, G of the elements of S less than, equal to, or greater than the pivot, resp. L,E, G empty sequences x S.remove(p) whileS.isEmpty() y S.remove(S.first()) ify<x L.insertLast(y) else if y=x E.insertLast(y) else{ y > x } G.insertLast(y) return L,E, G • We partition an input sequence as follows: • We remove, in turn, each element y from S and • We insert y into L, Eor G,depending on the result of the comparison with the pivot x • Each insertion and removal is at the beginning or at the end of a sequence, and hence takes O(1) time • Thus, the partition step of quick-sort takes O(n) time

  12. Quick-Sort Tree • An execution of quick-sort is depicted by a binary tree • Each node represents a recursive call of quick-sort and stores • Unsorted sequence before the execution and its pivot • Sorted sequence at the end of the execution • The root is the initial call • The leaves are calls on subsequences of size 0 or 1 7 4 9 6 2 2 4 6 7 9 4 2 2 4 7 9 7 9 2 2 9 9

  13. Execution Example • Pivot selection 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 7 2 9 4  2 4 7 9 3 8 6 1  1 3 8 6 9 4  4 9 3  3 8  8 2  2 9  9 4  4

  14. Execution Example (cont.) • Partition, recursive call, pivot selection 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 2 4 3 1 2 4 7 9 3 8 6 1  1 3 8 6 9 4  4 9 3  3 8  8 2  2 9  9 4  4

  15. Execution Example (cont.) • Partition, recursive call, base case 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 2 4 3 1 2 4 7 3 8 6 1  1 3 8 6 11 9 4  4 9 3  3 8  8 9  9 4  4

  16. Execution Example (cont.) • Recursive call, …, base case, join 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 3 8 6 1  1 3 8 6 11 4 334 3  3 8  8 9  9 44

  17. Execution Example (cont.) • Recursive call, pivot selection 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 7 9 7 1  1 3 8 6 11 4 334 8  8 9  9 9  9 44

  18. Execution Example (cont.) • Partition, …, recursive call, base case 7 2 9 4 3 7 6 11 2 3 4 6 7 8 9 2 4 3 1  1 2 3 4 7 9 7 1  1 3 8 6 11 4 334 8  8 99 9  9 44

  19. Execution Example (cont.) • Join, join 7 2 9 4 3 7 6 1 1 2 3 4 67 7 9 2 4 3 1  1 2 3 4 7 9 7 1779 11 4 334 8  8 99 9  9 44

  20. Worst-case Running Time • The worst case for quick-sort occurs when the pivot is the unique minimum or maximum element • One of L and G has size n - 1 and the other has size 0 • The running time is proportional to the sum n+ (n- 1) + … + 2 + 1 • Thus, the worst-case running time of quick-sort is O(n2) …

  21. Consider a recursive call of quick-sort on a sequence of size s Good call: the sizes of L and G are each less than 3s/4 Bad call: one of L and G has size greater than 3s/4 A call is good with probability 1/2 1/2 of the possible pivots cause good calls: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Expected Running Time 7 2 9 4 3 7 6 1 9 7 2 9 4 3 7 6 1 1 7 2 9 4 3 7 6 2 4 3 1 7 9 7 1  1 Good call Bad call Bad pivots Good pivots Bad pivots

  22. Probabilistic Fact: The expected number of coin tosses required in order to get k heads is 2k For a node of depth i, we expect i/2 ancestors are good calls The size of the input sequence for the current call is at most (3/4)i/2n Expected Running Time, Part 2 • Therefore, we have • For a node of depth 2log4/3n, the expected input size is one • The expected height of the quick-sort tree is O(log n) • The amount or work done at the nodes of the same depth is O(n) • Thus, the expected running time of quick-sort is O(n log n)

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