This preview shows pages 1–3. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Chapter 5 Divide and Conquer Divide-and-conquer refers to a class of algorithmic techniques in which one breaks the input into several parts, solves the problem in each part recursively, and then combines the solutions to these sub-problems into an overall solution. In many cases, it can be a simple and powerful method. Analyzing the running time of a divide-and-conquer algorithm generally involves solving a recurrence relation that bounds the running time recursively in terms of the running time on smaller instances. We begin the chapter with a general discussion of recurrence relations, illustrating how they arise in the analysis and describing methods for working out upper bounds from them. We then illustrate the use of divide-and-conquer with applications to a number of different domains: computing a distance function on different rankings of a set of objects; finding the closest pair of points in the plane; multiplying two integers; and smoothing a noisy signal. Divide-and-conquer will also come up in subsequent chapters, since it is a method that often works well when combined with other algorithm design techniques; for example, we will see it combined with dynamic programming to produce a space-eﬃcient solution to sequence alignment, and combined with randomization to yield a simple and eﬃcient algorithm for computing the median of a set of numbers. One thing to note about many settings in which divide-and-conquer is applied, including these, is that the natural brute-force algorithm may already be polynomial-time, and the divide-and-conquer strategy is serving to reduce the running time to a lower polynomial. This is in contrast to most of the problems in the previous chapter, for example, where brute-force was exponential and the goal in designing a more sophisticated algorithm was to achieve any kind of polynomial running time. For example, we discussed in Chapter 2 that the natural brute-force algorithm for finding the closest pair among n points in the plane would simply measure all Θ( n 2 ) distances, for a (polynomial) running time of Θ( n 2 ). Using divide-and-conquer, we will improve the running time to O ( n log n ). At a high level, then, the overall theme of this chapter is the same as what we’ve been seeing earlier: that 197 198 CHAPTER 5. DIVIDE AND CONQUER improving on brute-force search is a fundamental conceptual hurdle in solving a problem eﬃciently, and the design of sophisticated algorithms can achieve this. The difference is simply that the distinction between brute-force search and an improved solution here will not always be the distinction between exponential and polynomial. 5.1 A First Recurrence: The Mergesort Algorithm To motivate the general approach to analyzing divide-and-conquer algorithms, we begin with the Mergesort algorithm. We discussed the Mergesort algorithm brieﬂy in Chapter 2, when we surveyed common running times for algorithms. Mergesort sorts a given list of numbers by first dividing them into two equal halves, sorting each half separately by recursion, and...
View Full Document
This note was uploaded on 02/18/2010 for the course CSB 1208204834 taught by Professor N/a during the Spring '10 term at École Normale Supérieure.
- Spring '10