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Linear programming --- Stochastic processes --- Programmation linéaire --- Processus stochastiques --- Linear Programming --- Linear programming. --- Stochastic processes. --- Programmation linéaire --- 519.85 --- 519.85 Mathematical programming --- Mathematical programming --- Programmation (mathématiques) --- Programmation stochastique. --- Programming (Mathematics) --- Stochastic programming --- Programmation (mathématiques)
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Operations research --- Programming (Mathematics) --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Mathematical optimization --- Planning (firm)
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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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Stochastic programming --- Congresses --- Stochastic programming - Congresses
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This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. … The presentation includes geometric interpretation, linear programming duality, and the simplex method in its primal and dual forms. … The authors have made an effort to collect … the most useful recent ideas and algorithms in this area. … A guide to the existing software is included as well." (Darinka Dentcheva, Mathematical Reviews, Issue 2006 c) "This is a graduate text in optimisation whose main emphasis is in stochastic programming. The book is clearly written. … This is a good book for providing mathematicians, economists and engineers with an almost complete start up information for working in the field. I heartily welcome its publication. … It is evident that this book will constitute an obligatory reference source for the specialists of the field." (Carlos Narciso Bouza Herrera, Zentralblatt MATH, Vol. 1104 (6), 2007).
Electronic books. -- local. --- Linear programming. --- Stochastic processes. --- Linear programming --- Stochastic processes --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Random processes --- Mathematics. --- Operations research. --- Decision making. --- Applied mathematics. --- Engineering mathematics. --- Mathematical optimization. --- Management science. --- Probabilities. --- Optimization. --- Operations Research, Management Science. --- Appl.Mathematics/Computational Methods of Engineering. --- Probability Theory and Stochastic Processes. --- Operation Research/Decision Theory. --- Applications of Mathematics. --- Probabilities --- Production scheduling --- Programming (Mathematics) --- Distribution (Probability theory. --- Mathematical and Computational Engineering. --- Operations Research/Decision Theory. --- 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 --- 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
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Mathematical optimization --- Stochastic processes --- Stochastic programming --- Congresses.
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