First published in 1998, this textbook is a broad but rigourous survey of the theoretical basis for the design, definition and implementation of programming languages and of systems for specifying and proving programme behaviour. Both imperative and functional programming are covered, as well as the ways of integrating these aspects into more general languages. Recognising a unity of technique beneath the diversity of research in programming languages, the author presents an integrated treatment of the basic principles of the subject. He identifies the relatively small number of concepts, such as compositional semantics, binding structure, domains, transition systems and inference rules, that serve as the foundation of the field. Assuming only knowledge of elementary programming and mathematics, this text is perfect for advanced undergraduate and beginning graduate courses in programming language theory and also will appeal to researchers and professionals in designing or implementing computer languages.

The design and implementation of programming languages, from Fortran and Cobol to Caml and Java, has been one of the key developments in the management of ever more complex computerized systems. Introduction to the Theory of Programming Languages gives the reader the means to discover the tools to think, design, and implement these languages. It proposes a unified vision of the different formalisms that permit definition of a programming language: small steps operational semantics, big steps operational semantics, and denotational semantics, emphasising that all seek to define a relation between three objects: a program, an input value, and an output value. These formalisms are illustrated by presenting the semantics of some typical features of programming languages: functions, recursivity, assignments, records, objects, ... showing that the study of programming languages does not consist of studying languages one after another, but is organized around the features that are present in these various languages. The study of these features leads to the development of evaluators, interpreters and compilers, and also type inference algorithms, for small languages.

Category Theory is one of the most abstract branches of mathematics. It is usually taught to graduate students after they have mastered several other branches of mathematics, like algebra, topology, and group theory. It might, therefore, come as a shock that the basic concepts of category theory can be explained in relatively simple terms to anybody with some experience in programming.That's because, just like programming, category theory is about structure. Mathematicians discover structure in mathematical theories, programmers discover structure in computer programs. Well-structured programs are easier to understand and maintain and are less likely to contain bugs. Category theory provides the language to talk about structure and learning it will make you a better programmer.

There are several theories of programming. The first usable theory, often called "Hoare's Logic", is still probably the most widely known. In it, a specification is a pair of predicates: a precondition and postcondition (these and all technical terms will be defined in due course). Another popular and closely related theory by Dijkstra uses the weakest precondition predicate transformer, which is a function from programs and postconditions to preconditions. lones's Vienna Development Method has been used to advantage in some industries; in it, a specification is a pair of predicates (as in Hoare's Logic), but the second predicate is a relation. Temporal Logic is yet another formalism that introduces some special operators and quantifiers to describe some aspects of computation. The theory in this book is simpler than any of those just mentioned. In it, a specification is just a boolean expression. Refinement is just ordinary implication. This theory is also more general than those just mentioned, applying to both terminating and nonterminating computation, to both sequential and parallel computation, to both stand-alone and interactive computation. And it includes time bounds, both for algorithm classification and for tightly constrained real-time applications.

Cites successful examples of community-based policing.

Between good intentions and great results lies a program theory not just a list of tasks but a vision of what needs to happen, and how. Now widely used in government and not-for-profit organizations, program theory provides a coherent picture of how change occurs and how to improve performance. Purposeful Program Theory shows how to develop, represent, and use program theory thoughtfully and strategically to suit your particular situation, drawing on the fifty-year history of program theory and the authors' experiences over more than twenty-five years. "From needs assessment to intervention design, from implementation to outcomes evaluation, from policy formulation to policy execution and evaluation, program theory is paramount. But until now no book has examined these multiple uses of program theory in a comprehensive, understandable, and integrated way. This promises to be a breakthrough book, valuable to practitioners, program designers, evaluators, policy analysts, funders, and scholars who care about understanding why an intervention works or doesn't work." Michael Quinn Patton, author, Utilization-Focused Evaluation "Finally, the definitive guide to evaluation using program theory! Far from the narrow 'one true way' approaches to program theory, this book provides numerous practical options for applying program theory to fulfill different purposes and constraints, and guides the reader through the sound critical thinking required to select from among the options. The tour de force of the history and use of program theory is a truly global view, with examples from around the world and across the full range of content domains. A must-have for any serious evaluator." E. Jane Davidson, PhD, Real Evaluation Ltd. Companion Web site: josseybass.com/go/funnellrogers

Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering, must be based on a solid mathematical foundation. This book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, reliable, secure, and economical software.

This engaging text takes an evenhanded approach to major theoretical paradigms in evaluation and builds a bridge from them to evaluation practice. Featuring helpful checklists, procedural steps, provocative questions that invite readers to explore their own theoretical assumptions, and practical exercises, the book provides concrete guidance for conducting large- and small-scale evaluations. Numerous sample studies—many with reflective commentary from the evaluators—reveal the process through which an evaluator incorporates a paradigm into an actual research project. The book shows how theory informs methodological choices (the specifics of planning, implementing, and using evaluations). It offers balanced coverage of quantitative, qualitative, and mixed methods approaches. Useful pedagogical features include: *Examples of large- and small-scale evaluations from multiple disciplines. *Beginning-of-chapter reflection questions that set the stage for the material covered. *"Extending your thinking" questions and practical activities that help readers apply particular theoretical paradigms in their own evaluation projects. *Relevant Web links, including pathways to more details about sampling, data collection, and analysis. *Boxes offering a closer look at key evaluation concepts and additional studies. *Checklists for readers to determine if they have followed recommended practice. *A companion website with resources for further learning.

Optimization problems involving stochastic models occur in almost all areas of science and engineering, such as telecommunications, medicine, and finance. Their existence compels a need for rigorous ways of formulating, analyzing, and solving such problems. This book focuses on optimization problems involving uncertain parameters and covers the theoretical foundations and recent advances in areas where stochastic models are available. Readers will find coverage of the basic concepts of modeling these problems, including recourse actions and the nonanticipativity principle. The book also includes the theory of two-stage and multistage stochastic programming problems; the current state of the theory on chance (probabilistic) constraints, including the structure of the problems, optimality theory, and duality; and statistical inference in and risk-averse approaches to stochastic programming.

Optimization Theory and Methods can be used as a textbook for an optimization course for graduates and senior undergraduates. It is the result of the author's teaching and research over the past decade. It describes optimization theory and several powerful methods. For most methods, the book discusses an idea’s motivation, studies the derivation, establishes the global and local convergence, describes algorithmic steps, and discusses the numerical performance.