We strongly recommend to refer below articles as a prerequisite of this.

Randomized Algorithms | Set 1 (Introduction and Analysis)

Randomized Algorithms | Set 2 (Classification and Applications)

In this post, a Monte Carlo algorithm is discussed.

**Problem Statement : ** Given an unsorted array A[] of n numbers and ε > 0, compute an element whose rank (position in sorted A[]) is in the range [(1 – ε)n/2, (1 + ε)n/2].

For ½ Approximate Median Algorithm &epsilom; is 1/2 => rank should be in the range [n/4, 3n/4]

We can find k’th smallest element in O(n) expected time and O(n) worst case time.

**What if we want in less than O(n) time with low probable error allowed?**

Following steps represent an algorithm that is O((Log n) x (Log Log n)) time and produces incorrect result with probability less than or equal to 2/n^{2}.

- Randomly choose k elements from the array where k=c log n (c is some constant)
- Insert then into a set.
- Sort elements of the set.
- Return median of the set i.e. (k/2)th element from the set

## C

`/* C++ program to find Approximate Median using ` ` ` `1/2 Approximate Algorithm */` `#include<bits/stdc++.h> ` `using` `namespace` `std; ` ` ` `// This function returns the Approximate Median ` `int` `randApproxMedian(` `int` `arr[],` `int` `n) ` `{ ` ` ` `// Declaration for the random number generator ` ` ` `random_device rand_dev; ` ` ` `mt19937 generator(rand_dev()); ` ` ` ` ` `// Random number generated will be in the range [0,n-1] ` ` ` `uniform_int_distribution<` `int` `> distribution(0, n-1); ` ` ` ` ` `if` `(n==0) ` ` ` `return` `0; ` ` ` ` ` `int` `k = 10*log2(n); ` `// Taking c as 10 ` ` ` ` ` `// A set stores unique elements in sorted order ` ` ` `set<` `int` `> s; ` ` ` `for` `(` `int` `i=0; i<k; i++) ` ` ` `{ ` ` ` `// Generating a random index ` ` ` `int` `index = distribution(generator); ` ` ` ` ` `//Inserting into the set ` ` ` `s.insert(arr[index]); ` ` ` `} ` ` ` ` ` `set<` `int` `> ::iterator itr = s.begin(); ` ` ` ` ` `// Report the median of the set at k/2 position ` ` ` `// Move the itr to k/2th position ` ` ` `advance(itr, (s.size()/2) - 1); ` ` ` ` ` `// Return the median ` ` ` `return` `*itr; ` `} ` ` ` `// Driver method to test above method ` `int` `main() ` `{ ` ` ` `int` `arr[] = {1, 3, 2, 4, 5, 6, 8, 7}; ` ` ` `int` `n = ` `sizeof` `(arr)/` `sizeof` `(` `int` `); ` ` ` `printf` `(` `"Approximate Median is %d\n"` `,randApproxMedian(arr,n)); ` ` ` `return` `0 ` `} ` |

## Java

`/* Java program to find Approximate Median using ` ` ` `1/2 Approximate Algorithm */` `import` `java.util.Iterator; ` `import` `java.util.Random; ` `import` `java.util.TreeSet; ` ` ` ` ` `class` `Test ` `{ ` ` ` `static` `int` `arr[] = ` `new` `int` `[]{` `1` `, ` `3` `, ` `2` `, ` `4` `, ` `5` `, ` `6` `, ` `8` `, ` `7` `} ; ` ` ` ` ` `// This function returns the Approximate Median ` ` ` `static` `int` `randApproxMedian(` `int` `n) ` ` ` `{ ` ` ` `// Declaration for the random number ` ` ` `Random r = ` `new` `Random(); ` ` ` ` ` `if` `(n==` `0` `) ` ` ` `return` `0` `; ` ` ` ` ` `double` `k1 = ` `10` `*Math.log(n); ` `// Taking c as 10 ` ` ` ` ` `int` `k = (` `int` `)k1; ` ` ` ` ` `// A treeset stores unique elements in sorted order ` ` ` `TreeSet s = ` `new` `TreeSet<Integer>(); ` ` ` `for` `(` `int` `i=` `0` `; i<k; i++) ` ` ` `{ ` ` ` `// Generating a random index ` ` ` `// Random number generated will be in the range [0,n-1] ` ` ` `int` `index = r.nextInt(n); ` ` ` ` ` `//Inserting into the set ` ` ` `s.add(arr[index]); ` ` ` `} ` ` ` ` ` `Iterator<Integer> itr = s.iterator(); ` ` ` ` ` `int` `temp = s.size()/` `2` `- ` `1` `; ` ` ` ` ` `for` `(` `int` `i = ` `0` `; i < temp; i++) { ` ` ` `itr.next(); ` ` ` `} ` ` ` ` ` `// Return the median ` ` ` `return` `itr.next(); ` ` ` `} ` ` ` ` ` `// Driver method to test the above function ` ` ` `public` `static` `void` `main(String[] args) { ` ` ` ` ` `System.out.println(` `"Approximate Median is "` `+ randApproxMedian(arr.length)); ` ` ` ` ` `} ` `} ` |

Output:

Approximate Median is 4

**Time Complexity:**

We use a set provided by the STL in C++. In STL Set, insertion for each element takes O(log k). So for k insertions, time taken is O (k log k).

Now replacing k with c log n

=>O(c log n (log (clog n))) =>O (log n (log log n))

**How is probability of error less than 2/n ^{2}?**

Algorithm makes an error if the set S has at least k/2 elements are from the Left Quarter or Right Quarter.

It is quite easy to visualize this statement since the median which we report will be (k/2)th element and if we take k/2 elements from the left quarter(or right quarter) the median will be from the left quarter (or the right quarter).

An array can be divided into 4 quarters each of size n/4. So P(selecting left quarter) is 1/4. So what is the probability that at least k/2 elements are from the Left Quarter or Right Quarter? This probability problem is same as below :

Given a coin which gives HEADS with probability 1/4 and TAILS with 3/4. The coin is tossed k times. What is the probability that we get at least k/2 HEADS is less than or equal to?

**Explanation:**

If we put k = c log n for c = 10, we get P <= (1/2)^{2log n}P <= (1/2)^{log n2}P <= n^{-2}

Probability of selecting at least k/2 elements from the left quarter) <= 1/n^{2}

Probability of selecting at least k/2 elements from the left or right quarter) <= 2/n^{2}

Therefore algorithm produces incorrect result with probability less that or equal to 2/n^{2}.

**
References:**www.cse.iitk.ac.in/users/sbaswana/CS648/Lecture-2-CS648.pptx

This article is contributed by **Chirag Agarwal**. If you like GeeksforGeeks and would like to contribute, you can also write an article and mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

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