# 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. | ||

+ | |||

+ | <math>\begin{align} \min z=f(x)\\ | ||

+ | |||

+ | s.t.g(x) <= 0\\ | ||

+ | |||

+ | m_i\ge0,\quad \forall i \in I\\ | ||

+ | |||

+ | 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.