15767 8 86732 2 246380 document 86732 10.1137@17M1140601.pdf application/pdf 1af58bb84be8697bbe842d75855c9363 MD5 1534087 2019-02-26 14:38:59 https://pure.iiasa.ac.at/id/eprint/15767/1/10.1137%4017M1140601.pdf 15767 1 1 application/pdf text en public cc_by_nc_4

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published 86733 1 246387 document 86733 indexcodes.txt text/x-c++ 423175f284fe6d640e2fa7917d73dc30 MD5 10341 2019-02-26 14:48:22 https://pure.iiasa.ac.at/id/eprint/15767/2/indexcodes.txt 15767 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/islightboxThumbnailVersionOf https://pure.iiasa.ac.at/id/document/86732 86735 2 246389 document 86735 preview.jpg image/png a2f4bc5919932c8744ad1696f4ef49ec MD5 15499 2019-02-26 14:49:07 https://pure.iiasa.ac.at/id/eprint/15767/4/preview.jpg 15767 4 4 image/png other Thumbnails conversion from text to thumbnail_preview en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/86732 86737 2 246391 document 86737 small.jpg image/png cfffb4dcfb59cebf7cb4d81c2dd4aacb MD5 1436 2019-02-26 14:49:09 https://pure.iiasa.ac.at/id/eprint/15767/6/small.jpg 15767 6 6 image/png other Thumbnails conversion from text to thumbnail_small en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86732 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/86732 archive 353 disk0/00/01/57/67 2019-02-26 14:40:08 2021-08-27 17:31:28 2019-02-26 14:40:08 article show Maggioni F. Pflug G. 1361 0000-0001-8215-3550 pub prog_risk In general, multistage stochastic optimization problems are formulated on the basis of continuous distributions describing the uncertainty. Such “infinite” problems are practically impossible to solve as they are formulated, and finite tree approximations of the underlying stochastic processes are used as proxies. In this paper, we demonstrate how one can find guaranteed bounds, i.e., finite tree models, for which the optimal values give upper and lower bounds for the optimal value of the original infinite problem. Typically, there is a gap between the two bounds. However, this gap can be made arbitrarily small by making the approximating trees bushier. We consider approximations in the first-order stochastic sense, in the convex-order sense, and based on subgradient approximations. Their use is shown in a multistage risk-averse production problem. 2019-02 published Society for Industrial and Applied Mathematics (SIAM) 10.1137/17M1140601 229 public SIAM Journal on Optimization 29 1 454-483 TRUE 1095-7189 FALSE no info:eu-repo/semantics/article