Difference between revisions of "Convex generalized disjunctive programming (GDP)"

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Below is an example of the reformulation of the GDP problem from the Theory section reformulated into an MINLP by using the Big-M method.
 
Below is an example of the reformulation of the GDP problem from the Theory section reformulated into an MINLP by using the Big-M method.
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<math>\begin{align} \min z=f(x)\\
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      s.t.g(x) <= 0\\
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      m_i\ge0,\quad \forall i \in I\\
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      y_j\in {0,1},\quad \forall j \in J \end{align}</math>
  
 
== Numerical Example ==
 
== Numerical Example ==

Revision as of 16:50, 21 November 2020

Edited By: Nicholas Schafhauser, Blerand Qeriqi, Ryan Cuppernull

Introduction

Theory

Methodology

The two most common ways of reformulating a GDP problem into an MINLP are through Big-M (BM) and Hull Reformulation (HR). BM is the simpler of the two, while HR results in tighter relaxation (smaller feasible region) and faster solution times. (https://kilthub.cmu.edu/articles/A_hierarchy_of_relaxations_for_nonlinear_convex_generalized_disjunctive_programming/6466535)

Below is an example of the reformulation of the GDP problem from the Theory section reformulated into an MINLP by using the Big-M method.

Numerical Example

Applications

Conclusion

References