GDP Branch and Bound Solver

The GDP Branch and Bound solver is used to solve Generalized Disjunctive Programming (GDP) Problems. It branches through relaxed subproblems with inactive disjunctions. It explores the possibilities based on best lower bound, eventually activating all disjunctions and presenting the global optimal.

Using GDP Branch and Bound Solver

To use the GDPbb solver, define your Pyomo GDP model as usual:

Required import
>>> from pyomo.environ import *
>>> from pyomo.gdp import Disjunct, Disjunction

Create a simple model
>>> m = ConcreteModel()
>>> m.x1 = Var(bounds = (0,8))
>>> m.x2 = Var(bounds = (0,8))
>>> m.obj = Objective(expr=m.x1 + m.x2, sense=minimize)
>>> m.y1 = Disjunct()
>>> m.y2 = Disjunct()
>>> m.y1.c1 = Constraint(expr=m.x1 >= 2)
>>> m.y1.c2 = Constraint(expr=m.x2 >= 2)
>>> m.y2.c1 = Constraint(expr=m.x1 >= 3)
>>> m.y2.c2 = Constraint(expr=m.x2 >= 3)
>>> m.djn = Disjunction(expr=[m.y1, m.y2])

Invoke the GDPbb solver
>>> results = SolverFactory('gdpbb').solve(m)

>>> print(results)  
>>> print(results.solver.status)
>>> print(results.solver.termination_condition)

>>> print([value(m.y1.indicator_var), value(m.y2.indicator_var)])
[1, 0]

GDP Branch and Bound implementation and optional arguments

class pyomo.contrib.gdpbb.GDPbb.GDPbbSolver[source]

A branch and bound-based solver for Generalized Disjunctive Programming (GDP) problems

The GDPbb solver solves subproblems relaxing certain disjunctions, and builds up a tree of potential active disjunctions. By exploring promising branches, it eventually results in an optimal configuration of disjunctions.

Keyword arguments below are specified for the solve function.


Check if solver is available.

TODO: For now, it is always available. However, sub-solvers may not always be available, and so this should reflect that possibility.