Why is the average-case running time of quicksort $O(n log_2 n)$?












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What is a mathematically rigorous explanation of why quicksort's average-case running time is $O(n log_2 n)$?










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    $begingroup$


    What is a mathematically rigorous explanation of why quicksort's average-case running time is $O(n log_2 n)$?










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      What is a mathematically rigorous explanation of why quicksort's average-case running time is $O(n log_2 n)$?










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      What is a mathematically rigorous explanation of why quicksort's average-case running time is $O(n log_2 n)$?







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      asked Dec 6 '18 at 7:22









      Austin ConlonAustin Conlon

      2621412




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          To do average case analysis, we need to consider all possible permutation of the input array and calculate the time taken by every permutation which is not easy. We can get an idea of average case by considering the case when partition cuts $O(frac{n}{9})$ elements in one set and $O(frac{9n}{10})$ elements in other set. Following this we get
          begin{equation}
          T(n) = T(frac{n}{9}) + T(frac{9n}{10}) + O(n)
          end{equation}

          which is shown to be $O(n log n)$. If you average over all such permutations, you will also get $O(n log n)$.






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            $begingroup$

            To do average case analysis, we need to consider all possible permutation of the input array and calculate the time taken by every permutation which is not easy. We can get an idea of average case by considering the case when partition cuts $O(frac{n}{9})$ elements in one set and $O(frac{9n}{10})$ elements in other set. Following this we get
            begin{equation}
            T(n) = T(frac{n}{9}) + T(frac{9n}{10}) + O(n)
            end{equation}

            which is shown to be $O(n log n)$. If you average over all such permutations, you will also get $O(n log n)$.






            share|cite|improve this answer









            $endgroup$


















              1












              $begingroup$

              To do average case analysis, we need to consider all possible permutation of the input array and calculate the time taken by every permutation which is not easy. We can get an idea of average case by considering the case when partition cuts $O(frac{n}{9})$ elements in one set and $O(frac{9n}{10})$ elements in other set. Following this we get
              begin{equation}
              T(n) = T(frac{n}{9}) + T(frac{9n}{10}) + O(n)
              end{equation}

              which is shown to be $O(n log n)$. If you average over all such permutations, you will also get $O(n log n)$.






              share|cite|improve this answer









              $endgroup$
















                1












                1








                1





                $begingroup$

                To do average case analysis, we need to consider all possible permutation of the input array and calculate the time taken by every permutation which is not easy. We can get an idea of average case by considering the case when partition cuts $O(frac{n}{9})$ elements in one set and $O(frac{9n}{10})$ elements in other set. Following this we get
                begin{equation}
                T(n) = T(frac{n}{9}) + T(frac{9n}{10}) + O(n)
                end{equation}

                which is shown to be $O(n log n)$. If you average over all such permutations, you will also get $O(n log n)$.






                share|cite|improve this answer









                $endgroup$



                To do average case analysis, we need to consider all possible permutation of the input array and calculate the time taken by every permutation which is not easy. We can get an idea of average case by considering the case when partition cuts $O(frac{n}{9})$ elements in one set and $O(frac{9n}{10})$ elements in other set. Following this we get
                begin{equation}
                T(n) = T(frac{n}{9}) + T(frac{9n}{10}) + O(n)
                end{equation}

                which is shown to be $O(n log n)$. If you average over all such permutations, you will also get $O(n log n)$.







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Dec 6 '18 at 7:37









                Ahmad BazziAhmad Bazzi

                8,3622824




                8,3622824






























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