From Cornell University Computational Optimization Open Textbook - Optimization Wiki
|
|
Line 37: |
Line 37: |
|
| |
|
| <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 06:19, 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