From Cornell University Computational Optimization Open Textbook - Optimization Wiki
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| ''Optimal Solution for the MINLP: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math><br> | | ''Optimal Solution for the MINLP: ''<math display=inline>x_{1}=2, x_{2}=1,y_{1}=1, y_{2}=0</math><br> |
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| === Code === | | === GAMS Model === |
| The following code is used to solve the above example in the General Algebraic Modeling System (GAMS):<br> | | The above example can be expressed in the General Algebraic Modeling System (GAMS) as follows:<br> |
| <code>Variable z;<br> | | <code>Variable z;<br> |
| Positive Variables x1, x2;<br> | | Positive Variables x1, x2;<br> |
Revision as of 08:11, 26 November 2021
Author: Yousef Aloufi (CHEME 6800 Fall 2021)
Introduction
Theory
Example
Numerical Example
Minimize
Subject to
Solution
Step 1a: Start from
and solve the NLP below:
Minimize
Subject to
Solution: , Upper Bound = 7
Step 1b: Solve the MILP master problem with OA for :
Minimize
Subject to
MILP Solution: , Lower Bound = 6
Lower Bound < Upper Bound, Integer cut:
Step 2a: Start from
and solve the NLP below:
Minimize
Subject to
Solution: , Upper Bound = 6
Upper Bound = 6 = Lower Bound, Optimum!
Optimal Solution for the MINLP:
GAMS Model
The above example can be expressed in the General Algebraic Modeling System (GAMS) as follows:
Variable z;
Positive Variables x1, x2;
Binary Variables y1, y2;
Equations obj, c1, c2, c3, c4, c5, c6, c7;
obj.. z =e= y1 + y2 + sqr(x1) + sqr(x2);
c1.. sqr(x1 - 2) - x2 =l= 0;
c2.. x1 - 2*y1 =g= 0;
c3.. x1 - x2 - 3*sqr(1 - y1) =g= 0;
c4.. x1 + y1 - 1 =g= 0;
c5.. x2 - y2 =g= 0;
c6.. x1 + x2 =g= 3*y1;
c7.. y1 + y2 =g= 1;
x1.lo = 0; x1.up = 4;
x2.lo = 0; x2.up = 4;
model Example /all/;
option minlp = bonmin;
option optcr = 0;
solve Example minimizing z using minlp;
display z.l, x1.l, x2.l, y1.l, y2.l;
Conclusion
References