In this chapter we provide an intuitive introduction to the topic of approximability and parallel computation. The method of approximation is one of the well established ways of coping with computationally hard optimization problems. Many important problems are known to be NP-hard, therefore assuming the plausible hypothesis that P≠NP, it would be impossible to obtain polynomial time algorithms to solve these problems.

In Chapter 2, we will give a formal definition of optimization problem, and a formal introduction to the topics of PRAM computation and approximability. For the purpose of this chapter, in an optimization problem the goal is to find a solution that maximizes or minimizes an objective function subjected to some constrains. Let us recall that in general to study the NPcompleteness of an optimization problem, we consider its decision version. The decision version of many optimization problems is NP-complete, while the optimization version is NP-hard (see for example the book by Garey and Johnson [GJ79]). To refresh the above concepts, let us consider the Maximum Cut problem (MAXCUT).

Given a graph G with a set V of n vertices and a set E of edges, the MAXCUT problem asks for a partition of V into two disjoint sets V1 and V2 that maximizes the number of edges crossing between V1 and V2. From now on through all the manuscript, all graphs have a finite number of vertices n. The foregoing statement of the MAXCUT problem is the optimization version and it is known to be NP-hard [GJ79].

In Chapter 2, we presented several complexity classes based on the approximability degree of the problems they contain: APX, PTAS, FPTAS and their parallel counterparts NCX, NCAS, FNCAS. These classes, defined in terms of degree of approximability, are known as the “computationally defined” approximation classes. We have seen that in order to show that a problem belongs to one of these classes, one can present an approximation algorithm or a reduction to a problem with known approximability properties. In this chapter we show that in some cases the approximation properties of a problem can be obtained directly from its syntactical definition. These results are based on Fagin's characterization of the class NP in terms of existential second-order logic [Fag75], which constitutes one of the most interesting connections between logic and complexity. Papadimitriou and Yannakakis discovered that Fagin's characterization can be adapted to deal with optimization problems. They defined classes of approximation problems according to their syntactical characterization [PY91]. The importance of this approach comes from the fact that approximation properties of the optimization problems can be derived from their characterization in terms of logical quantifiers. Papadimitriou and Yannakakis defined the complexity classes MaxSNP and MaxNP which contain optimization versions of many important NP problems. They showed that many MaxSNP problems are in fact complete for the class. The paradigmatic MaxSNP-complete problem is Maximum 3SAT. Recall that the Maximum 3SAT problem consists in, given a boolean formula F written in conjunctive normal form with three literals in each clause, finding a truth assignment that satisfies the maximum number of clauses.

In the previous chapter, we gave a brief introduction to the topic of parallel approximability. We keep the discussion at an intuitive level, trying to give the feeling of the main ideas behind parallel approximability. In this chapter, we are going to review in a more formal setting the basic definitions about PRAM computations and approximation that we shall use through the text. We will also introduce the tools and notation needed. In any case, this chapter will not be a deep study of these topics. There exists a large body of literature for the reader who wishes to go further in the theory of PRAM computation, among others the books by Akl [Akl89], Gibbons and Rytter [GR88], Reif [Rei93] and JáJá [JaJ92]. There are also excellent short surveys on this topic, we just mention the one by Karp and Ramachandran [KR90] and the collection of surveys from the ALCOM school in Warwick [GS93]. In a similar way, many survey papers and lecture notes have been written on the topic of approximability, among others the Doctoral Dissertation of V. Kann [Kan92] with a recent update on the appendix of problems [CK95], the lecture notes of R. Motwani [Mot92], the survey by Ausiello et al. [ACP96] which includes a survey of non-approximability methods, the recent book edited by Hochbaum [Hoc96] and a forthcoming book by Ausiello et al. [ACG+96]

The PRAM Model of Computation

We begin this section by giving a formal introduction to our basic model of computation, the Parallel Random Access Machine. A PRAM consists of a number of sequential processors, each with its own memory, working synchronously and communicating between themselves through a common shared memory.

Exact matching: what's the problem?

Given a string P called the pattern and a longer string T called the text, the exact matching problem is to find all occurrences, if any, of pattern P in text T.

For example, if P = aba and T = bbabaxababay then P occurs in T starting at locations 3, 7, and 9. Note that two occurrences of P may overlap, as illustrated by the occurrences of P at locations 7 and 9.

Importance of the exact matching problem

The practical importance of the exact matching problem should be obvious to anyone who uses a computer. The problem arises in widely varying applications, too numerous to even list completely. Some of the more common applications are in word processors; in utilities such as grep on Unix; in textual information retrieval programs such as Medline, Lexis, or Nexis; in library catalog searching programs that have replaced physical card catalogs in most large libraries; in internet browsers and crawlers, which sift through massive amounts of text available on the internet for material containing specific keywords; in internet news readers that can search the articles for topics of interest; in the giant digital libraries that are being planned for the near future; in electronic journals that are already being “published” on-line; in telephone directory assistance; in on-line encyclopedias and other educational CD-ROM applications; in on-line dictionaries and thesauri, especially those with cross-referencing features (the Oxford English Dictionary project has created an electronic on-line version of the OED containing 50 million words); and in numerous specialized databases.