9780792374954

Book Description: Kluwer Academic Publishers, 2001. Book Condition: New. Bookseller Inventory # 3475113

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Book Description: SPRINGER VERLAG GMBH 01/05/2012, 2012. Hardback. Book Condition: New. New print on demand book. Shipped from US. This item is printed on demand. Bookseller Inventory # IO-9780792374954

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Book Description: Kluwer Academic Publishers. Hardback. Book Condition: new. BRAND NEW PRINT ON DEMAND., Generalized Concavity in Fuzzy Optimization and Decision Analysis, Jaroslav Ramik, Milan Vlach, Convexity of sets in linear spaces, and concavity and convexity of functions, lie at the root of beautiful theoretical results that are at the same time extremely useful in the analysis and solution of optimization problems, including problems of either single objective or multiple objectives. Not all of these results rely necessarily on convexity and concavity; some of the results can guarantee that each local optimum is also a global optimum, giving these methods broader application to a wider class of problems. Hence, the focus of the first part of the book is concerned with several types of generalized convex sets and generalized concave functions. In addition to their applicability to nonconvex optimization, these convex sets and generalized concave functions are used in the book's second part, where decision-making and optimization problems under uncertainty are investigated. Uncertainty in the problem data often cannot be avoided when dealing with practical problems. Errors occur in real-world data for a host of reasons. However, over the last thirty years, the fuzzy set approach has proved to be useful in these situations. It is this approach to optimization under uncertainty that is extensively used and studied in the second part of this book. Typically, the membership functions of fuzzy sets involved in such problems are neither concave nor convex. They are, however, often quasiconcave or concave in some generalized sense. This opens possibilities for application of results on generalized concavity to fuzzy optimization. Despite this obvious relation, applying the interface of these two areas has been limited to date. It is hoped that the combination of ideas and results from the field of generalized concavity on the one hand and fuzzy optimization on the other hand outlined and discussed in Generalized Concavity in Fuzzy Optimization and Decision Analysis will be of interest to both communities. Our aim is to broaden the classes of problems that the combination of these two areas can satisfactorily address and solve. Bookseller Inventory # B9780792374954

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Book Description: SPRINGER VERLAG GMBH 01/04/2012, 2012. Hardback. Book Condition: New. New print on demand book. Shipped from US. This item is printed on demand. Bookseller Inventory # IJ-9780792374954

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Book Description: Kluwer Academic Publishers, United States, 2001. Hardback. Book Condition: New. 234 x 156 mm. Brand New Book with Free Worldwide Delivery ***** Print on Demand *****. Convexity of sets in linear spaces, and concavity and convexity of functions, lie at the root of beautiful theoretical results that are at the same time extremely useful in the analysis and solution of optimization problems, including problems of either single objective or multiple objectives. Not all of these results rely necessarily on convexity and concavity; some of the results can guarantee that each local optimum is also a global optimum, giving these methods broader application to a wider class of problems. Hence, the focus of the first part of the book is concerned with several types of generalized convex sets and generalized concave functions. In addition to their applicability to nonconvex optimization, these convex sets and generalized concave functions are used in the book's second part, where decision-making and optimization problems under uncertainty are investigated. Uncertainty in the problem data often cannot be avoided when dealing with practical problems. Errors occur in real-world data for a host of reasons. However, over the last thirty years, the fuzzy set approach has proved to be useful in these situations.It is this approach to optimization under uncertainty that is extensively used and studied in the second part of this book. Typically, the membership functions of fuzzy sets involved in such problems are neither concave nor convex. They are, however, often quasiconcave or concave in some generalized sense. This opens possibilities for application of results on generalized concavity to fuzzy optimization. Despite this obvious relation, applying the interface of these two areas has been limited to date. It is hoped that the combination of ideas and results from the field of generalized concavity on the one hand and fuzzy optimization on the other hand outlined and discussed in Generalized Concavity in Fuzzy Optimization and Decision Analysis will be of interest to both communities. Our aim is to broaden the classes of problems that the combination of these two areas can satisfactorily address and solve. Bookseller Inventory # APC9780792374954

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Book Description: Springer, 2012. Hardcover. Book Condition: New. 6.14 by 9 inches. (00312 pages) This item is printed on demand. Please allow up to 10 days extra for printing & delivery. {Publisher's Publication Date = 2001-09-30 00:00:00} [ships from USA takes 8-14 days to Europe] illustrated Lang=English accessory:NO ACCESSORY (Hardcover ). Bookseller Inventory # AF0792374959

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Book Description: Hardcover. Book Condition: New. 1st. 234mm x 19mm x 156mm. Print on Demand 312 pages. 0.617. Bookseller Inventory # 9780792374954

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