# ALGORITHM DESIGN AND ANALYSIS

**INTRODUCTION OF ALGORITHM DESIGN AND ANALYSIS**

This part will start you thinking about designing and analyzing algorithms. It is intended to be a gentle introduction to how we specify algorithms, some of the design strategies we will use throughout this book, and many of the fundamental ideas used in algorithm analysis. Later parts of this book will build upon this base.**ALGORITHM DESIGN AND ANALYSIS**

Chapter 1 provides an overview of algorithms and their place in modern computing systems. This chapter deﬁnes what an algorithm is and lists some examples. It also makes a case that we should consider algorithms as a technology, alongside technologies such as fast hardware, graphical user interfaces, object-oriented systems, and networks.

In Chapter 2, we see our ﬁrst algorithms, which solve the problem of sorting a sequence of n numbers. They are written in a pseudocode which, although not directly translatable toanyconventional programming language, conveys the structure of the algorithm clearly enough that you should be able to implement it in the language of your choice.

The sorting algorithms we examine are insertion sort, which uses an incremental approach, and merge sort, which uses a recursive technique known as “divide-and-conquer.” Although the time each requires increases with the value of n, the rate of increase differs between the two algorithms. We determine these running times in Chapter 2, and we develop a useful notation to express them.**ALGORITHM DESIGN AND ANALYSIS**

Chapter 3 precisely deﬁnes this notation, which we call asymptotic notation. It starts by deﬁning several asymptotic notations, which we use for bounding algorithm running times from above and/or below. The rest of Chapter 3 is primarily a presentation of mathematical notation, more to ensure that your use of notation matches that in this book than to teach you new mathematical concepts.**ALGORITHM DESIGN AND ANALYSIS**

Chapter 4 delves further into the divide-and-conquer method introduced in Chapter 2. It provides additional examples of divide-and-conquer algorithms, including Strassen’s surprising method for multiplying two square matrices.**ALGORITHM DESIGN AND ANALYSIS**

Chapter 4 contains methods for solving recurrences, which are useful for describing the running times of recursive algorithms. One powerful technique is the “master method,” which we often use to solve recurrences that arise from divide-andconquer algorithms. Although much of Chapter 4 is devoted to proving the correctness of the master method, you may skip this proof yet still employ the master method.**ALGORITHM DESIGN AND ANALYSIS** ADVANCED ENGINEERING MATHEMATICS

Chapter 5 introduces probabilistic analysis and randomized algorithms. We typically use probabilistic analysis to determine the running time of an algorithm in cases in which, due to the presence of an inherent probability distribution, the running time may differ on different inputs of the same size.**ALGORITHM DESIGN AND ANALYSIS**COMPUTER GRAPHICS AND MULTIMEDIA FOUNDATIONS OF COMPUTER SCIENCE

In some cases, we assume that the inputs conform to a known probability distribution, so that we are averaging the running time over all possible inputs. In other cases, the probability distribution comes not from the inputs but from random choices made during the course ofthealgorithm.

Analgorithm whosebehavior isdetermined notonly byits input but by the values produced by a random-number generator is a randomized algorithm. We can use randomized algorithms to enforce a probability distribution on the inputs—thereby ensuring that no particular input always causes poor performance—or even to bound the error rate of algorithms that are allowed to produce incorrect results on a limited basis.

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