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

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(Created page with "Edited By: Nicholas Schafhauser, Blerand Qeriqi, Ryan Cuppernull")
 
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Edited By: Nicholas Schafhauser, Blerand Qeriqi, Ryan Cuppernull
 
Edited By: Nicholas Schafhauser, Blerand Qeriqi, Ryan Cuppernull
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== Introduction ==
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== Theory ==
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== Methodology ==
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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)
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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.
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== Numerical Example ==
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== Applications ==
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== Conclusion ==
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== References ==

Revision as of 16:33, 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