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This book constitutes the refereed proceedings of the 26th International Symposium on Mathematical Foundations of Computer Science, MFCS 2001, held in Marianske Lazne, Czech Republic in August 2001. The 51 revised full papers presented together with 10 invited contributions were carefully reviewed and selected from a total of 118 submissions. All current aspects of theoretical computer science are addressed ranging from mathematical logic and programming theory to algorithms, discrete mathematics, and complexity theory. Besides classical issues, modern topics like quantum computing are discussed as well.
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This IMA Volume in Mathematics and its Applications DISCRETE EVENT SYSTEMS, MANUFACTURING SYSTEMS AND COMMUNICATION NETWORKS is based on the proceedings of a workshop that was an integral part of the 1992-93 IMA program on "Control Theory. " The study of discrete event dynamical systems (DEDS) has become rapidly popular among researchers in systems and control, in communication networks, in manufacturing, and in distributed computing. This development has created problems for re searchers and potential "consumers" of the research. The first problem is the veritable Babel of languages, formalisms, and approaches, which makes it very difficult to determine the commonalities and distinctions among the competing schools of approaches. The second, related, problem arises from the different traditions, paradigms, values, and experience that scholars bring to their study of DEDS, depending on whether they come from control, com munication, computer science, or mathematical logic. As a result, intellectual exchange among scholars becomes compromised by unexplicated assumptions. The purpose of the Workshop was to promote exchange among scholars representing some of the major "schools" of thought in DEDS with the hope that (1) greater clarity will be achieved thereby, and (2) cross-fertilization will lead to more fruitful questions. We thank P. R. Kumar and P. P. Varaiya for organizing the workshop and editing the proceedings. We also take this opportunity to thank the National Science Foundation and the Army Research Office, whose financial support made the workshop possible. A vner Friedman Willard Miller, Jr.
Systems Analysis and Design: An Object-Oriented Approach with UML, 5th Edition by Dennis, Wixom, and Tegarden captures the dynamic aspects of the field by keeping students focused on doing SAD while presenting the core set of skills that every systems analyst needs to know today and in the future. The text enables students to do SAD—not just read about it, but understand the issues so they can actually analyze and design systems. The text introduces each major technique, explains what it is, explains how to do it, presents an example, and provides opportunities for students to practice before they do it for real in a project. After reading each chapter, the student will be able to perform that step in the system development process.
Each number is the catalogue of a specific school or college of the University.
This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de mand can be described by a probability distribution, the problem is called a stochastic optimization problem.