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Computations are designed to solve problems. Programs are descriptions of computations written for execution on computers. The field of computer science is concerned with the development of methodologies for designing programs, and with the development of computers for executing programs. It is therefore of central importance for those involved in the field that the characteristics of programs, computers, problems, and computation be fully understood. Moreover, to clearly and accurately communicate intuitive thoughts about these subjects, a precise and well-defined terminology is required.
This book explores some of the more important terminologies and questions concerning programs, computers, problems, and computation. The exploration reduces in many cases to a study of mathematical theories, such as those of automata and formal languages; theories that are interesting also in their own right. These theories provide abstract models that are easier to explore, because their formalisms avoid irrelevant details.
Organized into seven chapters, the material in this book gradually increases in complexity. In many cases, new topics are treated as refinements of old ones, and their study is motivated through their association to programs.
Chapter 1 is concerned with the definition of some basic concepts. It starts by considering the notion of strings, and the role that strings have in presenting information. Then it relates the concept of languages to the notion of strings, and introduces grammars for characterizing languages. The chapter continues by introducing a class of programs. The choice is made for a class, which on one hand is general enough to model all programs, and on the other hand is primitive enough to simplify the specific investigation of programs. In particular, the notion of nondeterminism is introduced through programs. The chapter concludes by considering the notion of problems, the relationship between problems and programs, and some other related notions.
Chapter 2 studies finite-memory programs. The notion of a state is introduced as an abstraction for a location in a finite-memory program as well as an assignment to the variables of the program. The notion of state is used to show how finite-memory programs can be modeled by abstract computing machines, called finite-state transducers. The transducers are essentially sets of states with rules for transition between the states. The inputs that can be recognized by finite-memory programs are characterized in terms of a class of grammars, called regular grammars. The limitations of finite-memory programs, closure properties for simplifying the job of writing finite-memory programs, and decidable properties of such programs are also studied.
Chapter 3 considers the introduction of recursion to finite-memory programs. The treatment of the new programs, called recursive finite-domain programs, resembles that for finite-memory programs in Chapter 2. Specifically, the recursive finite-domain programs are modeled by abstract computing machines, called pushdown transducers. Each pushdown transducer is essentially a finite-state transducer that can access an auxiliary memory that behaves like a pushdown storage of unlimited size. The inputs that can be recognized by recursive finite-domain programs are characterized in terms of a generalization of regular grammars, called context-free grammars. Finally, limitations, closure properties, and decidable properties of recursive finite-domain programs are derived using techniques similar to those for finite-memory programs.
Chapter 4 deals with the general class of programs. Abstract computing machines, called Turing transducers, are introduced as generalizations of pushdown transducers that place no restriction on the auxiliary memory. The Turing transducers are proposed for characterizing the programs in general, and computability in particular. It is shown that a function is computable by a Turing transducer if and only if it is computable by a deterministic Turing transducer. In addition, it is shown that there exists a universal Turing transducer that can simulate any given deterministic Turing transducer. The limitations of Turing transducers are studied, and they are used to demonstrate some undecidable problems. A grammatical characterization for the inputs that Turing transducers recognize is also offered.
Chapter 5 considers the role of time and space in computations. It shows that problems can be classified into an infinite hierarchy according to their time requirements. It discusses the feasibility of those computations that can be carried out in “polynomial time” and the infeasibility of those computations that require “exponential time.” Then it considers the role of “nondeterministic polynomial time.” “Easiest” hard problems are identified, and their usefulness for detecting hard problems is exhibited. Finally, the relationship between time and space is examined.
Chapter 6 introduces instructions that allow random choices in programs. Deterministic programs with such instructions are called probabilistic programs. The usefulness of these programs is considered, and then probabilistic Turing transducers are introduced as abstractions of such programs. Finally, some interesting classes of problems that are solvable probabilistically in polynomial time are studied.
Chapter 7 is devoted to parallelism. It starts by considering parallel programs in which the communication cost is ignored. Then it introduces “high-level” abstractions for parallel programs, called PRAM’s, which take into account the cost of communication. It continues by offering a class of “hardware-level” abstractions, called uniform families of circuits, which allow for a rigorous analysis of the complexity of parallel computations. The relationship between the two classes of abstractions is detailed, and the applicability of parallelism in speeding up sequential computations is considered.
The motivation for adding this text to the many already in the field originated from the desire to provide an approach that would be more appealing to readers with a background in programming. A unified treatment of the subject is therefore provided, which links the development of the mathematical theories to the study of programs.
The only cost of this approach occurs in the introduction of transducers, instead of restricting the attention to abstract computing machines that produce no output. The cost, however, is minimal because there is negligible variation between these corresponding kinds of computing machines.
On the other hand, the benefit is extensive. This approach helps considerably in illustrating the importance of the field, and it allows for a new treatment of some topics that is more attractive to those readers whose main background is in programming. For instance, the notions of nondeterminism, acceptance, and abstract computing machines are introduced here through programs in a natural way. Similarly, the characterization of pushdown automata in terms of context-free languages is shown here indirectly through recursive finite-domain programs, by a proof that is less tedious than the direct one.
The choice of topics for the text and their organization are generally in line with what is the standard in the field. The exposition, however, is not always standard. For instance, transition diagrams are offered as representations of pushdown transducers and Turing transducers. These representations enable a significant simplification in the design and analysis of such abstract machines, and consequently provide the opportunity to illustrate many more ideas using meaningful examples and exercises.
As a natural outcome, the text also treats the topics of probabilistic and parallel computations. These important topics have matured quite recently, and so far have not been treated in other texts.
The level of the material is intended to provide the reader with introductory tools for understanding and using formal specifications in computer science. As a result, in many cases ideas are stressed more than detailed argumentation, with the objective of developing the reader’s intuition toward the subject as much as possible.
This book is intended for undergraduate students at advanced stages of their studies, and for graduate students. The reader is assumed to have some experience as a programmer, as well as in handling mathematical concepts. Otherwise no specific prerequisite is necessary.
The entire text represents a one-year course load. For a lighter load some of the material may be just sketched, or even skipped, without loss of continuity. For instance, most of the proofs in Section 2.6, the end of Section 3.5, and Section 3.6, may be so treated.
Theorems, Figures, Exercises, and other items in the text are labeled with triple numbers. An item that is labeled with a triple i.j.k is assumed to be the kth item of its type in Section j of Chapter i.
Finally, I am indebted to Elizabeth Zwicky for helping me with the computer facilities at Ohio State University, and to Linda Davoli and Sonia DiVittorio for their editing work. I would like to thank my colleagues Ming Li , Tim Long , and Yaacov Yesha for helping me with the difficulties I had with some of the topics, for their useful comments, and for allowing me the opportunities to teach the material. I am also very grateful to an anonymous referee and to many students whose feedback guided me to the current exposition of the subject.
1 GENERAL CONCEPTS
1.1 Alphabets, Strings, and Representations
1.2 Formal Languages and Grammars
1.5 Reducibility among Problems
2 FINITE-MEMORY PROGRAMS
2.2 Finite-State Transducers
2.3 Finite-State Automata and Regular Languages
2.4 Limitations of Finite-Memory Programs
2.5 Closure Properties for Finite-Memory Programs
2.6 Decidable Properties for Finite-Memory Programs
3 RECURSIVE FINITE-DOMAIN PROGRAMS
3.2 Pushdown Transducers
3.3 Context-Free Languages
3.4 Limitations of Recursive Finite-Domain Programs
3.5 Closure Properties for Recursive Finite-Domain Programs
3.6 Decidable Properties for Recursive Finite-Domain Programs
4 GENERAL PROGRAMS
4.1 Turing Transducers
4.2 Programs and Turing Transducers
4.3 Nondeterminism versus Determinism
4.4 Universal Turing Transducers
4.6 Turing Machines and Type 0 Languages
4.7 Post’s Correspondence Problem
5 RESOURCE-BOUNDED COMPUTATION
5.1 Time and Space
5.2 A Time Hierarchy
5.3 Nondeterministic Polynomial Time
5.4 More NP-Complete Problems
5.5 Polynomial Space
5.6 P-Complete Problems
6 PROBABILISTIC COMPUTATION
6.1 Error-Free Probabilistic Programs
6.2 Probabilistic Programs That Might Err
6.3 Probabilistic Turing Transducers
6.4 Probabilistic Polynomial Time
7 PARALLEL COMPUTATION
7.1 Parallel Programs
7.2 Parallel Random Access Machines
7.4 Uniform Families of Circuits
7.5 Uniform Families of Circuits and Sequential Computations
7.6 Uniform Families of Circuits and PRAM’s
A MATHEMATICAL NOTIONS
A.1 Sets, Relations, and Functions
A.2 Graphs and Trees
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