Convex generalized disjunctive programming (GDP): Difference between revisions
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Edited By: Nicholas Schafhauser, Blerand Qeriqi, Ryan Cuppernull | 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 == |
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.