Important Problem Types in Algorithms Analysis

Important Problem Types

 In the limitless sea of problems one encounters in computing, there are a few areas that have attracted particular attention from researchers. By and large, their interest has been driven either by the problem’s practical importance or by some specific characteristics making the problem an interesting research subject; fortunately, these two motivating forces reinforce each other in most cases.

 In this section, we are going to introduce the most important problem types:

·         Sorting

·         Searching

·         String processing

·         Graph problems 

·         Combinatorial problems

·         Geometric problems

·         Numerical problems

 These problems are used in subsequent chapters of the book to illustrate different algorithm design techniques and methods of algorithm analysis.

 Sorting

The sorting problem is to rearrange the items of a given list in nondecreasing order. Of course, for this problem to be meaningful, the nature of the list items must allow such an ordering. (Mathematicians would say that there must exist a relation of total ordering.) As a practical matter, we usually need to sort lists of numbers, characters from an alphabet, character strings, and, most important, records similar to those maintained by schools about their students, libraries about their holdings, and companies about their employees. In the case of records, we need to choose a piece of information to guide sorting. For example, we can choose to sort student records in alphabetical order of names or by student number or by student grade-point average. Such a specially chosen piece of information is called a key. Computer scientists often talk about sorting a list of keys even when the list’s items are not records but, say, just integers.

Why would we want a sorted list? To begin with, a sorted list can be a required output of a task such as ranking Internet search results or ranking students by their GPA scores. Further, sorting makes many questions about the list easier to answer. The most important of them is searching: it is why dictionaries, telephone books, class lists, and so on are sorted. You will see other examples of the usefulness of list presorting in Section 6.1. In a similar vein, sorting is used as an auxiliary step in several important algorithms in other areas, e.g., geometric algorithms and data compression. The greedy approach—an important algorithm design technique discussed later in the book—requires a sorted input.

By now, computer scientists have discovered dozens of different sorting algo-rithms. In fact, inventing a new sorting algorithm has been likened to designing the proverbial mousetrap. And I am happy to report that the hunt for a better sorting mousetrap continues. This perseverance is admirable in view of the fol-lowing facts. On the one hand, there are a few good sorting algorithms that sort an arbitrary array of size n using about n log2 n comparisons. On the other hand, no algorithm that sorts by key comparisons (as opposed to, say, comparing small pieces of keys) can do substantially better than that.

There is a reason for this embarrassment of algorithmic riches in the land of sorting. Although some algorithms are indeed better than others, there is no algorithm that would be the best solution in all situations. Some of the algorithms are simple but relatively slow, while others are faster but more complex; some work better on randomly ordered inputs, while others do better on almost-sorted lists; some are suitable only for lists residing in the fast memory, while others can be adapted for sorting large files stored on a disk; and so on.

Two properties of sorting algorithms deserve special mention. A sorting algo-rithm is called stable if it preserves the relative order of any two equal elements in its input. In other words, if an input list contains two equal elements in positions and j where i < j, then in the sorted list they have to be in positions i and j ,

respectively, such that i < j . This property can be desirable if, for example, we have a list of students sorted alphabetically and we want to sort it according to student GPA: a stable algorithm will yield a list in which students with the same GPA will still be sorted alphabetically. Generally speaking, algorithms that can exchange keys located far apart are not stable, but they usually work faster; you will see how this general comment applies to important sorting algorithms later in the book.

 The second notable feature of a sorting algorithm is the amount of extra memory the algorithm requires. An algorithm is said to be in-place if it does not require extra memory, except, possibly, for a few memory units. There are important sorting algorithms that are in-place and those that are not.

Searching

The searching problem deals with finding a given value, called a search key, in a given set (or a multiset, which permits several elements to have the same value). There are plenty of searching algorithms to choose from. They range from the straightforward sequential search to a spectacularly efficient but limited binary search and algorithms based on representing the underlying set in a different form more conducive to searching. The latter algorithms are of particular importance for real-world applications because they are indispensable for storing and retriev-ing information from large databases.

For searching, too, there is no single algorithm that fits all situations best. Some algorithms work faster than others but require more memory; some are very fast but applicable only to sorted arrays; and so on. Unlike with sorting algorithms, there is no stability problem, but different issues arise. Specifically, in applications where the underlying data may change frequently relative to the number of searches, searching has to be considered in conjunction with two other operations: an addition to and deletion from the data set of an item. In such situations, data structures and algorithms should be chosen to strike a balance among the requirements of each operation. Also, organizing very large data sets for efficient searching poses special challenges with important implications for real-world applications.

 String Processing

In recent decades, the rapid proliferation of applications dealing with nonnumer-ical data has intensified the interest of researchers and computing practitioners in string-handling algorithms. A string is a sequence of characters from an alphabet. Strings of particular interest are text strings, which comprise letters, numbers, and special characters; bit strings, which comprise zeros and ones; and gene sequences, which can be modeled by strings of characters from the four-character alphabet {A, C, G, T}. It should be pointed out, however, that string-processing algorithms have been important for computer science for a long time in conjunction with computer languages and compiling issues.

One particular problem—that of searching for a given word in a text—has attracted special attention from researchers. They call it string matching. Several algorithms that exploit the special nature of this type of searching have been invented. We introduce one very simple algorithm in Chapter 3 and discuss two algorithms based on a remarkable idea by R. Boyer and J. Moore in Chapter 7.

 Graph Problems 

One of the oldest and most interesting areas in algorithmics is graph algorithms. Informally, a graph can be thought of as a collection of points called vertices, some of which are connected by line segments called edges. (A more formal definition is given in the next section.) Graphs are an interesting subject to study, for both theoretical and practical reasons. Graphs can be used for modeling a wide variety of applications, including transportation, communication, social and economic networks, project scheduling, and games. Studying different technical and social aspects of the Internet in particular is one of the active areas of current research involving computer scientists, economists, and social scientists (see, e.g., [Eas10]).

Basic graph algorithms include graph-traversal algorithms (how can one reach all the points in a network?), shortest-path algorithms (what is the best route be-tween two cities?), and topological sorting for graphs with directed edges (is a set of courses with their prerequisites consistent or self-contradictory?). Fortunately, these algorithms can be considered illustrations of general design techniques; accordingly, you will find them in corresponding chapters of the book.

Some graph problems are computationally very hard; the most well-known examples are the traveling salesman problem and the graph-coloring problem. The traveling salesman problem (TSP) is the problem of finding the shortest tour through n cities that visits every city exactly once. In addition to obvious appli-cations involving route planning, it arises in such modern applications as circuit board and VLSI chip fabrication, X-ray crystallography, and genetic engineer-ing. The graph-coloring problem seeks to assign the smallest number of colors to the vertices of a graph so that no two adjacent vertices are the same color. This problem arises in several applications, such as event scheduling: if the events are represented by vertices that are connected by an edge if and only if the correspond-ing events cannot be scheduled at the same time, a solution to the graph-coloring problem yields an optimal schedule.

 Combinatorial Problems

 From a more abstract perspective, the traveling salesman problem and the graph-coloring problem are examples of combinatorial problems. These are problems that ask, explicitly or implicitly, to find a combinatorial object—such as a permu-tation, a combination, or a subset—that satisfies certain constraints. A desired combinatorial object may also be required to have some additional property such as a maximum value or a minimum cost.

Generally speaking, combinatorial problems are the most difficult problems in computing, from both a theoretical and practical standpoint. Their difficulty stems from the following facts. First, the number of combinatorial objects typically grows extremely fast with a problem’s size, reaching unimaginable magnitudes even for moderate-sized instances. Second, there are no known algorithms for solving most such problems exactly in an acceptable amount of time. Moreover, most computer scientists believe that such algorithms do not exist. This conjecture has been neither proved nor disproved, and it remains the most important unresolved issue in theoretical computer science. We discuss this topic in more detail in Section 11.3.

 Some combinatorial problems can be solved by efficient algorithms, but they should be considered fortunate exceptions to the rule. The shortest-path problem mentioned earlier is among such exceptions.

 Geometric Problems

 Geometric algorithms deal with geometric objects such as points, lines, and poly-gons. The ancient Greeks were very much interested in developing procedures (they did not call them algorithms, of course) for solving a variety of geometric problems, including problems of constructing simple geometric shapes—triangles, circles, and so on—with an unmarked ruler and a compass. Then, for about 2000 years, intense interest in geometric algorithms disappeared, to be resurrected in the age of computers—no more rulers and compasses, just bits, bytes, and good old human ingenuity. Of course, today people are interested in geometric algorithms with quite different applications in mind, such as computer graphics, robotics, and tomography.

 We will discuss algorithms for only two classic problems of computational geometry: the closest-pair problem and the convex-hull problem. The closest-pair problem is self-explanatory: givenn points in the plane, find the closest pair amongthem. The convex-hull problem asks to find the smallest convex polygon that would include all the points of a given set. If you are interested in other geometric algorithms, you will find a wealth of material in such specialized monographs as [deB10], [ORo98], and [Pre85].

 Numerical Problems

 Numerical problems, another large special area of applications, are problemsthat involve mathematical objects of continuous nature: solving equations and systems of equations, computing definite integrals, evaluating functions, and so on. The majority of such mathematical problems can be solved only approximately. Another principal difficulty stems from the fact that such problems typically require manipulating real numbers, which can be represented in a computer only approximately. Moreover, a large number of arithmetic operations performed on approximately represented numbers can lead to an accumulation of the round-off error to a point where it can drastically distort an output produced by a seemingly sound algorithm.

Many sophisticated algorithms have been developed over the years in this area, and they continue to play a critical role in many scientific and engineering applications. But in the last 30 years or so, the computing industry has shifted its focus to business applications. These new applications require primarily algo-rithms for information storage, retrieval, transportation through networks, and presentation to users. As a result of this revolutionary change, numerical analysis has lost its formerly dominating position in both industry and computer science programs. Still, it is important for any computer-literate person to have at least a rudimentary idea about numerical algorithms. We discuss several classical numer-ical algorithms in Sections 6.2, 11.4, and 12.4.

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