Median Finding and Quick Sort

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					Median Finding and Quick
Sort


       Suvarna Angal
Project Requirements
   Implement the median-finding algorithms –
    Random and Linear Median Finding Algorithms.
   The user is able to select the “k”, i.e., the rank of
    the number desired as output (k = n/2 is the
    median).
   The user is also able to select groups of 3 or 5 in
    the linear-time median finding algorithm.
Project Requirements
   The user can compare the performance of
    Random and Linear Median Finding
    Algorithms.
   Implement quick sort using both
    algorithms and compare the performances
    of these different versions.
Randomized Median Finding
 QSel(S,k)
 m = a random element of S is the pivot.
 S1 = all numbers in S < m
 S2 = all numbers in S > m
 if |S1| >= k return Qsel(S1,k);
 else if |S| - |S2| >= k return m;
 else return Qsel(S2,k-|S|+|S2|);
Median Finding
   The difference with this approach is about
    the pivot selection.
   To find the pivot element, we divide S into
    n/5 groups of 5 elements each.
   Each group is then sorted and its median is
    selected.
   Then invoke a recursive call to the same
    function to find median of medians.
Median Finding
   Then this median of medians becomes the
    pivot element for the QSel function.

   This will find the k-th smallest element in a
    sequence S of n elements in worst-case
    time O(n).
Implementation
   The project is implemented in java.
   The User Interface is done using Java Swing-
    MedianClient.java
   This takes a list of numbers, k and number of
    elements in a group as input.
   4 classes – MedianQuickSort.java,
    MedianRandomQuickSort.java, Median.java, and
    MedianRandom.java are written.
User Interface
Analysis
   Used simple counter to measure
    performance.
   Checked performance for varying input
    sizes like 100, 1000 and 10000 for all 4
    algortihms.
   Also changed the group size 3 or 5 for the
    Linear Median Finding Algorithm.
Thank You!
  Demo.