Logo of Humboldt-Universität zu BerlinLogo of Humboldt-Universität zu Berlin
edoc-Server
Open-Access-Publikationsserver der Humboldt-Universität
de|en
Header image: facade of Humboldt-Universität zu Berlin
View Item 
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2017
  • View Item
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2017
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
All of edoc-ServerCommunity & CollectionTitleAuthorSubjectThis CollectionTitleAuthorSubject
PublishLoginRegisterHelp
StatisticsView Usage Statistics
View Item 
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2017
  • View Item
  • edoc-Server Home
  • Elektronische Zeitschriften
  • Stochastic Programming E-print Series (SPEPS)
  • Volume 2017
  • View Item
2017-07-31Buch DOI: 10.18452/18087
Learning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization
Sen, Suvrajeet
Deng, Yunxiao cc
Mathematisch-Naturwissenschaftliche Fakultät
Several emerging applications, such as “Analytics of Things" and “Integrative Analytics" call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses concepts from these disciplines in a manner which not only enriches both SL and SO, but also provides a framework which supports rapid model updates and optimization, together with a methodology for rapid model-validation, assessment, and selection. Moreover, in many big data/big decisions applications these steps are repetitive, and possible only through a continuous cycle involving data analysis, optimization, and validation. This paper sets forth the foundation for such a framework by introducing several novel concepts such as statistical optimality, hypothesis tests for modeldelity, generalization error of stochastic optimization, and finally, a non-parametric methodology for model selection. These new concepts provide a formal framework for modeling, solving, validating, and reporting solutions for Learning Enabled Optimization (LEO). We illustrate the LEO framework by applying it to an inventory control model in which we use demand data available for ARIMA modeling in the statistical package \R". In addition, we also study a production-marketing coordination model based on combining a pedagogical production planning model with an advertising data set intended for sales prediction. Because the approach requires the solution of several stochastic programming instances, some using continuous random variables, we leverage stochastic decomposition (SD) for the fusion of regression and stochastic linear programming. In this sense, the novelty of this paper is its framework, rather than a specific new algorithm. Finally, we present an architecture of a software framework to bring about the fusion we envision.
Files in this item
Thumbnail
SPEPS-2017-4.pdf — Adobe PDF — 1.342 Mb
MD5: 54356d7e31e02b1f40a38fe759bb0038
Cite
BibTeX
EndNote
RIS
InCopyright
Details
DINI-Zertifikat 2019OpenAIRE validatedORCID Consortium
Imprint Policy Contact Data Privacy Statement
A service of University Library and Computer and Media Service
© Humboldt-Universität zu Berlin
 
DOI
10.18452/18087
Permanent URL
https://doi.org/10.18452/18087
HTML
<a href="https://doi.org/10.18452/18087">https://doi.org/10.18452/18087</a>