Home | | Design and Analysis of Algorithms | Divide and Conquer

Chapter: Introduction to the Design and Analysis of Algorithms : Divide and Conquer

Divide and Conquer

Divide-and-conquer is probably the best-known general algorithm design technique. Though its fame may have something to do with its catchy name, it is well deserved: quite a few very efficient algorithms are specific implementations of this general strategy.

Divide-and-Conquer

 

Whatever man prays for, he prays for a miracle. Every prayer reduces itself to this—Great God, grant that twice two be not four.

 

—Ivan Turgenev (1818–1883), Russian novelist and short-story writer

 

Divide-and-conquer is probably the best-known general algorithm design technique. Though its fame may have something to do with its catchy name, it is well deserved: quite a few very efficient algorithms are specific implementations of this general strategy. Divide-and-conquer algorithms work according to the following general plan:

 

            A problem is divided into several subproblems of the same type, ideally of about equal size.

 

            The subproblems are solved (typically recursively, though sometimes a dif-ferent algorithm is employed, especially when subproblems become small enough).

 

            If necessary, the solutions to the subproblems are combined to get a solution to the original problem.

 

The divide-and-conquer technique is diagrammed in Figure 5.1, which depicts the case of dividing a problem into two smaller subproblems, by far the most widely occurring case (at least for divide-and-conquer algorithms designed to be executed on a single-processor computer).

 

As an example, let us consider the problem of computing the sum of n numbers a0, . . . , an1. If n > 1, we can divide the problem into two instances of the same problem: to compute the sum of the first n/2 numbers and to compute the sum of the remaining n/2 numbers. (Of course, if n = 1, we simply return a0 as the answer.) Once each of these two sums is computed by applying the same method recursively, we can add their values to get the sum in question:

 

a0 + . . . + an1 = (a0 + . . . + a n/21) + (a n/2  + . . . + an1).

 

Is this an efficient way to compute the sum of n numbers? A moment of reflection (why could it be more efficient than the brute-force summation?), a


small example of summing, say, four numbers by this algorithm, a formal analysis (which follows), and common sense (we do not normally compute sums this way, do we?) all lead to a negative answer to this question.1

 

Thus, not every divide-and-conquer algorithm is necessarily more efficient than even a brute-force solution. But often our prayers to the Goddess of Algorithmics—see the chapter’s epigraph—are answered, and the time spent on executing the divide-and-conquer plan turns out to be significantly smaller than solving a problem by a different method. In fact, the divide-and-conquer approach yields some of the most important and efficient algorithms in computer science. We discuss a few classic examples of such algorithms in this chapter. Though we consider only sequential algorithms here, it is worth keeping in mind that the divide-and-conquer technique is ideally suited for parallel computations, in which each subproblem can be solved simultaneously by its own processor.

 

As mentioned above, in the most typical case of divide-and-conquer a prob-lem’s instance of size n is divided into two instances of size n/2. More generally, an instance of size n can be divided into b instances of size n/b, with a of them needing to be solved. (Here, a and b are constants; a 1 and b > 1.) Assuming that size n is a power of b to simplify our analysis, we get the following recurrence for the running time T (n):


where f (n) is a function that accounts for the time spent on dividing an instance of size n into instances of size n/b and combining their solutions. (For the sum example above, a = b = 2 and f (n) = 1.) Recurrence (5.1) is called the general divide-and-conquer recurrence. Obviously, the order of growth of its solution T (n) depends on the values of the constants a and b and the order of growth of the function f (n). The efficiency analysis of many divide-and-conquer algorithms is greatly simplified by the following theorem (see Appendix B).


For example, the recurrence for the number of additions A(n) made by the divide-and-conquer sum-computation algorithm (see above) on inputs of size n = 2k is


Note that we were able to find the solution’s efficiency class without going through the drudgery of solving the recurrence. But, of course, this approach can only es-tablish a solution’s order of growth to within an unknown multiplicative constant, whereas solving a recurrence equation with a specific initial condition yields an exact answer (at least for n’s that are powers of b).

 

It is also worth pointing out that if a = 1, recurrence (5.1) covers decrease-by-a-constant-factor algorithms discussed in the previous chapter. In fact, some people consider such algorithms as binary search degenerate cases of divide-and-conquer, where just one of two subproblems of half the size needs to be solved. It is better not to do this and consider decrease-by-a-constant-factor and divide-and-conquer as different design paradigms.

 

Study Material, Lecturing Notes, Assignment, Reference, Wiki description explanation, brief detail
Introduction to the Design and Analysis of Algorithms : Divide and Conquer : Divide and Conquer |


Privacy Policy, Terms and Conditions, DMCA Policy and Compliant

Copyright © 2018-2023 BrainKart.com; All Rights Reserved. Developed by Therithal info, Chennai.