Why is Binary Search a divide and conquer algorithm?

Question

I was asked if a Binary Search is a divide and conquer algorithm at an exam. My answer was yes, because you divided the problem into smaller subproblems, until you reached y

Answer 1

I think it is Decrease and Conquer.

Here is a quote from wikipedia.

"The name decrease and conquer has been proposed instead for the single-subproblem class"

http://en.wikipedia.org/wiki/Divide_and_conquer_algorithms#Decrease_and_conquer

According to my understanding, "Conquer" part is at the end when you find the target element of the Binary search. The "Decrease" part is reducing the search space.

Answer 2

Divide and Conquer algorithm is based on 3 step as follows:

1. Divide
2. Conquer
3. Combine

Binary Search problem can be defined as finding x in the sorted array A[n]. According to this information:

1. Divide: compare x with middle
2. Conquer: Recurse in one sub array. (Finding x in this array)
3. Combine: it is not necessary.

Answer 3

Apparently some people consider binary search a divide-and-conquer algorithm, and some are not. I quickly googled three references (all seem related to academia) that call it a D&C algorithm: http://www.cs.berkeley.edu/~vazirani/algorithms/chap2.pdf http://homepages.ius.edu/rwisman/C455/html/notes/Chapter2/DivConq.htm http://www.csc.liv.ac.uk/~ped/teachadmin/algor/d_and_c.html

I think it's common agreement that a D&C algorithm should have at least the first two phases of these three:

• divide, i.e. decide how the whole problem is separated into sub-problems;
• conquer, i.e. solve each of the sub-problems independently;
• [optionally] combine, i.e. merge the results of independent computations together.

The second phase - conquer - should recursively apply the same technique to solve the subproblem by dividing into even smaller sub-sub-problems, and etc. In practice, however, often some threshold is used to limit the recursive approach, as for small size problems a different approach might be faster. For example, quick sort implementations often use e.g. bubble sort when the size of an array portion to sort becomes small.

The third phase might be a no-op, and in my opinion it does not disqualify an algorithm as D&C. A common example is recursive decomposition of a for-loop with all iterations working purely with independent data items (i.e. no reduction of any form). It might look useless at glance, but in fact it's very powerful way to e.g. execute the loop in parallel, and utilized by such frameworks as Cilk and Intel's TBB.

Returning to the original question: let's consider some code that implements the algorithm (I use C++; sorry if this is not the language you are comfortable with):

int search( int value, int* a, int begin, int end ) {
  // end is one past the last element, i.e. [begin, end) is a half-open interval.
  if (begin < end)
  {
    int m = (begin+end)/2;
    if (value==a[m])
      return m;
    else if (value<a[m])
      return search(value, a, begin, m);
    else
      return search(value, a, m+1, end);
  }
  else // begin>=end, i.e. no valid array to search
    return -1;
}

Here the divide part is int m = (begin+end)/2; and all the rest is the conquer part. The algorithm is explicitly written in a recursive D&C form, even though only one of the branches is taken. However, it can also be written in a loop form:

int search( int value, int* a, int size ) {
  int begin=0, end=size;
  while( begin<end ) {
    int m = (begin+end)/2;
    if (value==a[m])
      return m;
    else if (value<a[m])
      end = m;
    else
      begin = m+1;
  }
  return -1;
}

I think it's quite a common way to implement binary search with a loop; I deliberately used the same variable names as in the recursive example, so that commonality is easier to see. Therefore we might say that, again, calculating the midpoint is the divide part, and the rest of the loop body is the conquer part.

But of course if your examiners think differently, it might be hard to convince them it's D&C.

Update: just had a thought that if I were to develop a generic skeleton implementation of a D&C algorithm, I would certainly use binary search as one of API suitability tests to check whether the API is sufficiently powerful while also concise. Of course it does not prove anything :)

Answer 4

The book

Data Structures and Algorithm Analysis in Java, 2nd edtition, Mark Allen Weiss

Says that a D&C algorithm should have two disjoint recursive calls. I.e like QuickSort. Binary Search does not have this, even if it can be implemented recursively, so I guess this is the answer.

Answer 5

Binary Search and Ternary Search Algorithms are based on Decrease and Conquer technique. Because, you do not divide the problem, you actually decrease the problem by dividing by 2(3 in ternary search).

Merge Sort and Quick Sort Algorithms can be given as examples of Divide and Conquer technique. You divide the problem into two subproblems and use the algorithm for these subproblems again to sort an array. But, you discard the half of array in binary search. It means you DECREASE the size of array, not divide.

Answer 6

The divide part is of course dividing the set into halves.

The conquer part is determining whether and on what position in the processed part there is a searched element.