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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CONVEX OPTIMIZATION,r in §1.C may not be able to order more than . newspapers and would need to select an order quantity from the set .. A maximum likelihood problem (§1.E) permits only certain types of estimates. This chapter initiates our treatment of such optimization problems with .. As we’ll see, a problem of this

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OPTIMIZATION UNDER UNCERTAINTY,uture demand when its inventory is re-stocked. A classifier is fitted to data that may not represent the context in which it will be applied. Under such circumstances, we would like to formulate a model that accounts for uncertainty and produces good decisions even though future conditions are unkno

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PERTURBATION AND DUALITY,olution is obtained, we typically would like to assess the validity of the model. Is the solution insensitive to changes in model parameters? This is important because the exact values of parameters may not be known and one would like to avoid being misled by a solution obtained using incorrect valu

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WITHOUT CONVEXITY OR SMOOTHNESS, that are . for a point to be a local minimizer, even without convexity, and thereby supplementing the necessary conditions furnished by the Oresme and 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 t

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