Outer-approximation (OA): Difference between revisions

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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>
'''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>


==Conclusion==
==Conclusion==


==References==
==References==

Revision as of 06:52, 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  :

Conclusion

References