Journal Article10.1016/J.COMPCHEMENG.2006.09.013
Stochastic MINLP optimization using simplicial approximation
TL;DR: A decomposition algorithm is presented to solve convex stochastic MINLP problems and is an extension of the simplicial approximation approach proposed by Goyal and Ierapetritou, based on the idea of representing the feasible region by a close approximation of its convex hull.
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About: This article is published in Computers & Chemical Engineering. The article was published on 01 Sep 2007. The article focuses on the topics: Feasible region & Global optimization.
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References
Introduction to Stochastic Programming
John R. Birge,Franois Louveaux +1 more
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TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.
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GAMS, a user's guide
TL;DR: JuMP is an open-source modeling language that allows users to express a wide range of ideas in an easy-to-use manner.
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Introduction to Stochastic Programming
TL;DR: In this paper, an introduction to stochastic programming is presented, which is based on the idea of Stochastic Programming (SPP) and is used in our work.
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Introduction to stochastic programming
Antonio Alonso Ayuso,Laureano Fernando Escudero Bueno,María Celeste Pizarro Romero +2 more
- 24 Apr 2009
2.4K
The Sample Average Approximation Method for Stochastic Discrete Optimization
TL;DR: A Monte Carlo simulation--based approach to stochastic discrete optimization problems, where a random sample is generated and the expected value function is approximated by the corresponding sample average function.