From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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| <math display=block>0 \leq x_{2}\leq 4</math> | | <math display=block>0 \leq x_{2}\leq 4</math> |
| ''Solution: ''<math display=inline>x_{1}=2, x_{2}=1</math>, Upper Bound = 7 <br> | | ''Solution: ''<math display=inline>x_{1}=2, x_{2}=1</math>, Upper Bound = 7 <br> |
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| | '''Step 1a:''' Solve the MILP master problem with OA for <math display=inline> x^{*} =[2,1] </math> : <br> |
| | <math display=block>f\big(x\big) =\big( x_{1} \big)^{2} +\big( x_{2} \big)^{2},~~ \bigtriangledown f\big(x\big)=[2x_{1}~~2x_{1}]^{T} ~~for~~x^{*} =[2,1]^{T} </math> |
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| ==Conclusion== | | ==Conclusion== |
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| ==References== | | ==References== |
Revision as of 05:52, 26 November 2021
Author: Yousef Aloufi (CHEME 6800 Fall 2021)
Introduction
Theory
Example
Minimize

Subject to 









Solution
Step 1a: Start from

and solve the NLP below:
Minimize 
Subject to 







Solution: 
, Upper Bound = 7
Step 1a: Solve the MILP master problem with OA for
:
![{\displaystyle f{\big (}x{\big )}={\big (}x_{1}{\big )}^{2}+{\big (}x_{2}{\big )}^{2},~~\bigtriangledown f{\big (}x{\big )}=[2x_{1}~~2x_{1}]^{T}~~for~~x^{*}=[2,1]^{T}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/53a4da719bf07dc10c836a41caa820990b28459d)
Conclusion
References