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Compte tenu, d’une part, de l’importance grandissante de la concurrence et de la portée géopolitique de l’enseignement supérieur et de la recherche, et, d’autre part, du retard de développement de nombreux EES nouveaux, cette étude cherche à analyser les processus et les stratégies élaborés par les nouveaux EES pour développer leur activité de recherche. Cet ouvrage propose un panorama unique des expériences d’un groupe d’établissements qui n’a jusqu’à présent pas été défini ni exploré. Il examine les résultats d’une étude de fond portant sur 25 EES situés dans 16 pays : Australie, Belgique, Canada, Danemark, Espagne, Finlande, Grèce, Hong Kong Chine, Hongrie, Irlande, Japon, Nouvelle-Zélande, Portugal, République tchèque, Royaume-Uni, Suède.

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Applied probability is a broad research area that is of interest to scientists in diverse disciplines in science and technology, including: anthropology, biology, communication theory, economics, epidemiology, finance, geography, linguistics, medicine, meteorology, operations research, psychology, quality control, sociology, and statistics. Recent Advances in Applied Probability is a collection of survey articles that bring together the work of leading researchers in applied probability to present current research advances in this important area. This volume will be of interest to graduate students and researchers whose research is closely connected to probability modelling and their applications. It is suitable for one semester graduate level research seminar in applied probability.

Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Distribution (Probability theory. --- Mathematics. --- Probability Theory and Stochastic Processes. --- Operations Research, Management Science. --- Mathematics, general. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Operations research. --- Management science. --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory

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This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form without neglecting rigour. The work should enable the professional to apply optimization theory and algorithms to his own particular practical field of interest, be it engineering, physics, chemistry, or business economics. Most importantly, for the first time in a relatively brief and introductory work, due attention is paid to the difficulties—such as noise, discontinuities, expense of function evaluations, and the existence of multiple minima—that often unnecessarily inhibit the use of gradient-based methods. In a separate chapter on new gradient-based methods developed by the author and his coworkers, it is shown how these difficulties may be overcome without losing the desirable features of classical gradient-based methods. Audience It is intended that this book be used in senior- to graduate-level semester courses in optimization, as offered in mathematics, engineering, computer science, and operations research departments, and also to be useful to practising professionals in the workplace.

Mathematical optimization. --- Programming (Mathematics). --- Programming (Mathematics) --- Algorithms. --- Numerical analysis. --- Optimization. --- Operations Research, Management Science. --- Numerical Analysis. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Algorism --- Algebra --- Arithmetic --- Foundations --- Operations research. --- Management science. --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory

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Peter Kall and János Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization. STOCHASTIC LINEAR PROGRAMMING: Models, Theory, and Computation is a definitive presentation and discussion of the theoretical properties of the models, the conceptual algorithmic approaches, and the computational issues relating to the implementation of these methods to solve problems that are stochastic in nature. The application area of stochastic programming includes portfolio analysis, financial optimization, energy problems, random yields in manufacturing, risk analysis, etc. In this book models in financial optimization and risk analysis are discussed as examples, including solution methods and their implementation. Stochastic programming is a fast developing area of optimization and mathematical programming. Numerous papers and conference volumes, and several monographs have been published in the area; however, the Kall & Mayer book will be particularly useful in presenting solution methods including their solid theoretical basis and their computational issues, based in many cases on implementations by the authors. The book is also suitable for advanced courses in stochastic optimization.

Linear programming. --- Stochastic processes. --- Random processes --- Probabilities --- Production scheduling --- Programming (Mathematics) --- Mathematical optimization. --- Engineering mathematics. --- Distribution (Probability theory. --- Operations research. --- Mathematics. --- Optimization. --- Operations Research, Management Science. --- Mathematical and Computational Engineering. --- Probability Theory and Stochastic Processes. --- Operations Research/Decision Theory. --- Applications of Mathematics. --- Math --- Science --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Distribution functions --- Frequency distribution --- Characteristic functions --- Engineering --- Engineering analysis --- Mathematical analysis --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Mathematics --- Management science. --- Applied mathematics. --- Probabilities. --- Decision making. --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Choice (Psychology) --- Problem solving --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Quantitative business analysis --- Statistical decision --- Decision making --- Engineering—Data processing. --- Operations Research, Management Science . --- Mathematical and Computational Engineering Applications. --- Probability Theory. --- Operations Research and Decision Theory.

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• The book introduces the key ideas behind practical nonlinear optimization. • Computational finance—an increasingly popular area of mathematics degree programmes—is combined here with the study of an important class of numerical techniques. • The financial content of the book is designed to be relevant and interesting to specialists. However, this material—which occupies about one-third of the text—is also sufficiently accessible to allow the book to be used on optimization courses of a more general nature. • The essentials of most currently popular algorithms are described and their performance is demonstrated on a range of optimization problems arising in financial mathematics. • Theoretical convergence properties of methods are stated and formal proofs are provided in enough cases to be instructive rather than overwhelming. • Practical behaviour of methods is illustrated by computational examples and discussions of efficiency, accuracy and computational costs. • Supporting software for the examples and exercises is available (but the text does not require the reader to use or understand these particular codes). • The author has been active in optimization for over thirty years in algorithm development and application and in teaching and research supervision. Audience The book is aimed at lecturers and students (undergraduate and postgraduate) in mathematics, computational finance and related subjects. It is also useful for researchers and practitioners who need a good introduction to nonlinear optimization.

Nonlinear programming. --- Mathematical optimization. --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis --- Programming (Mathematics) --- Electronic data processing. --- Computer science --- Finance. --- Numerical analysis. --- Optimization. --- Operations Research, Management Science. --- Numeric Computing. --- Computational Mathematics and Numerical Analysis. --- Finance, general. --- Numerical Analysis. --- Mathematics. --- Funding --- Funds --- Economics --- Currency question --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Computers --- Office practice --- Mathematics --- Automation --- Mathematical optimization --- Nonlinear programming --- Optimisation mathematique --- Programmation non lineaire --- dataverwerking --- lineaire programmering --- mathematische modellen, toegepast op economie --- portfoliobeheer --- Operations research. --- Management science. --- Computer mathematics. --- Quantitative business analysis --- Management --- Problem solving --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Operations Research, Management Science . --- Financial Economics. --- Data processing.