So we can recursively find all 1 3 plurality elements in each of the halves and

# So we can recursively find all 1 3 plurality elements

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one of the halves. So we can recursively find all 1 / 3-plurality elements in each of the halves, and in the end we have at most 4 candidates. We can check whether they are truly a 1 / 3-plurality by just counting how many times they occur in the full array, in O ( n ) time. Algorithm: Here is the complete algorithm. Algorithm FindPlurality ( A [ 0 .. n - 1 ]) : 1. If n = 1 return { A [ 0 ] } . 2. If n 2: 3. Let k : = b n / 2 c . 4. Let C : = /0. (“This will be the set of candidates.”) 5. Let M : = /0. (“This will be the set of 1 / 3-plurality elements.”) 6. Call FindPlurality ( A [ 0 .. k - 1 ]) and add the results to C . 7. Call FindPlurality ( A [ k .. n - 1 ]) and add the results to C . 8. For each x in C : 9. Count the number of occurences of x in A [ 0 .. n - 1 ] . 10. If this number is greater than n / 3, add x to M . 11. Return M . Running time: The running time is O ( n lg n ) . The total number of items returned by each call can be at most 2, since there can be at most two 1 / 3-plurality elements in any array. Therefore C can have size at most 4, which means that line 9 gets executed at most 4 times, each time taking O ( n ) time. Now if T ( n ) is the running time of the algorithm for an array of size n , then we just proved that T ( n ) = 2 T ( n / 2 )+ O ( n ) . By appealing to the master theorem, we can see that this results in a running time of O ( n lg n ) . Proof of correctness: We will be inductively proving that the algorithm returns all 1 / 3-plurality elements in the given array. Let P ( n ) denote the assertion that, for all arrays A of length n , FindPlurality ( A [ 0 .. n - 1 ]) correctly returns all 1 / 3-plurality elements of A . We want to prove n N . P ( n ) . We will prove it by strong induction on n . CS 170, Fall 2014, Sol 2 7 Base case: the base case of the induction is when n = 1, and in that case the single element in the array is a 1 / 3-plurality which is returned by the algorithm (line 1). Thus, we have proven P ( 1 ) . Inductive step: We need to prove P ( 1 ) ∧ ··· ∧ P ( n - 1 ) = P ( n ) . Since we run over all elements of C and check whether they are in fact 1 / 3-plurality elements, there is no risk that FindPlurality will return something that isn’t a 1 / 3-plurality element. We just need to show that each true 1 / 3-plurality element x does get inserted into C . Consider one such element x . If x does not get added to C in line 6, it means that it was not a 1 / 3-plurality in A [ 0 .. k - 1 ] , so it appeared at most k / 3 many times in that part (since k < n and the inductive hypothesis promises that FindPlurality works correctly on an array of size k ). Similarly if it does not get added in line 7, it means that it appeared at most ( n - k ) / 3 many times in A [ k .. n - 1 ] . So overall, the number of times it can appear in A [ 0 .. n - 1 ] is at most k / 3 + ( n - k ) / 3 = n / 3. But this is in contradiction with the assumption that x was a 1 / 3-plurality element. So every 1 / 3-plurality element will be added to C in either line 6 or line 7, and thus will be returned by FindPlurality.  • • • 