Outer-approximation (OA): Difference between revisions

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<math display=block>g\big(x^{*}\big)+ \bigtriangledown  g\big(x^{*}\big)^{T}\big(x-x^{*}\big)=-1+[0~~~~-1] \begin{bmatrix}x_{1}-2  \\x_{2}-1  \end{bmatrix}=-x_{2}</math>
<math display=block>g\big(x^{*}\big)+ \bigtriangledown  g\big(x^{*}\big)^{T}\big(x-x^{*}\big)=-1+[0~~~~-1] \begin{bmatrix}x_{1}-2  \\x_{2}-1  \end{bmatrix}=-x_{2}</math>
''Minimize'' <math display=block> \alpha </math>
''Subject to'' <math display=block>\alpha\geq y_{1}  </math>


==Conclusion==
==Conclusion==


==References==
==References==

Revision as of 07:19, 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  :

Minimize

Subject to

Conclusion

References