Titlebook: An Optimization Primer; Johannes O. Royset,Roger J-B Wets Textbook 2021 The Editor(s) (if applicable) and The Author(s), under exclusive l

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1431-8598 is and other advanced subjects.Suitable for graduate and advThis richly illustrated book introduces the subject of optimization to a broad audience with a balanced treatment of theory, models and algorithms. Through numerous examples from statistical learning, operations research, engineering, finan

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,Würfel, Quader, Zylinder, Kegel,t any restrictions and is the main subject of this chapter. In addition to theoretical foundations and algorithms for computing solutions, we’ll cover applications in inventory control, statistical estimation and data analytics. ., involving also side conditions, as well as more general . will be dealt with later on.

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,Durchdringungen und Übergangsformen,ch circumstances, we would like to formulate a model that accounts for uncertainty and produces good decisions even though future conditions are unknown. This leads to problems of .. We’ll now examine several formulations of such problems and construct algorithms that address the often large-scale nature of the resulting models.

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https://doi.org/10.1007/978-3-662-11510-7d Fermat rules. We’ll extend the duality theory of Chap. . and close any duality gap that may be present in the absence of convexity. This will lead to a successful class of algorithms for nonconvex problems. Optimality conditions without leveraging smoothness, functions lacking epi-regularity and error analysis will also be covered.

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OPTIMIZATION UNDER UNCERTAINTY,ch circumstances, we would like to formulate a model that accounts for uncertainty and produces good decisions even though future conditions are unknown. This leads to problems of .. We’ll now examine several formulations of such problems and construct algorithms that address the often large-scale nature of the resulting models.

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