lecture21

# lecture21 - Review and Catchup Nonlinear Programming Review...

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Review and Catchup Nonlinear Programming IE426: Optimization Models and Applications: Lecture 21 Jeﬀ Linderoth Department of Industrial and Systems Engineering Lehigh University November 21, 2006 Jeﬀ Linderoth IE426:Lecture 21 Review and Catchup Nonlinear Programming Stochastic Programming Review Quiz #2 HW #4 Questions? No Class on 11/30 Homework Due 11/30 – In my mailbox by close of business! TV People – Please have proctor verify that you turned in the homework by 11/30. You will need to know a little about nonlinear programming, AMPL, and NEOS, so we’ll do that today. Jeﬀ Linderoth IE426:Lecture 21 Review and Catchup Nonlinear Programming Stochastic Programming Final Discussion Final is on 12/7 from 2:45PM–5:45PM Location: 410 Packard Lab TV People – You can take it anytime on 12/7 Contact me if you cannot take it from 2:45–5:45 on 12/7. You will take it in the morning of 12/7 Jeﬀ Linderoth IE426:Lecture 21 Review and Catchup Nonlinear Programming Stochastic Programming Stochastic Programming Review For stochastic programming, you will need to know how to do the following: 1 Create the “supermodel”: The full stochastic model to optimize the expected value of the decisions 2 Compute Value of Stochastic Solution 3 Compute the Expected Value of Perfect Information Jeﬀ Linderoth IE426:Lecture 21

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Review and Catchup Nonlinear Programming Stochastic Programming VSS and EVPI: Deﬁnitions Let S : Set of scenarios Let s : Be the average scenario. A scenario in which each element is the average over all the elements in S Be sure to weight the average by the probability of each scenario Let SP : Stochastic program Let LP s ) : Linear program that gives optimal (ﬁrst and second stage) actions if scenario ˆ s happens Let LP x,s ) : Linear program that gives optimal (second stage) actions if scenario ˆ s happens, and ﬁrst stage decisions ˆ x are taken Jeﬀ Linderoth IE426:Lecture 21 Review and Catchup Nonlinear Programming Stochastic Programming More Deﬁnitions Let v ( · ) : Be the value of the corresponding problem. Let x ( · ) : Be an optimal solution to the named problem. v ( SP ) : Value of optimal solution to SP v ( LP ( s )) : Optimal solution value of LP ( s ) x ( LP ( s )) : Optimal solution to LP ( s ) Jeﬀ Linderoth IE426:Lecture 21 Review and Catchup Nonlinear Programming Stochastic Programming VSS These deﬁnitions assume maximization
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## lecture21 - Review and Catchup Nonlinear Programming Review...

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