There are several algorithmic choices that can be selected with BONMIN. B-BB is a NLP-based branch-and-bound algorithm, B-OA is an outer-approximation decomposition algorithm, B-iFP is an iterated feasibility pump algorithm, B-QG is an implementation of Quesada and Grossmann’s branch-and-cut algorithm, B-Hyb is a hybrid outer-approximation based branch-and-cut algorithm and B-Ecp is a variant of B-QG based on adding additional ECP cuts.
Some of the algorithmic choices require the ability to solve MILP (Mixed Integer Linear Programming) problems and NLP (NonLinear Programming) problems. The default solvers for these are, respectively, the COIN-OR codes Cbc and Ipopt. In turn, Cbc uses further COIN-OR modules: Clp (for LP (Linear Programming) problems), Cgl (for generating MILP cutting planes), as well as various other utilities. It is also possible to step outside the open-source realm and use Cplex as the MILP solver and FilterSQP as the NLP solver.
Additional documentation can be found on the Bonmin
| minf(x) | |||
| s.t. | |||
| gL ≤ g(x) ≤ gU, | |||
| xL ≤ x ≤ xU, | |||
| x ∈ ℝn,x i ∈ ℤ∀i ∈ I, |
The different methods that BONMIN implements are exact algorithms when the functions f and g are convex but are only heuristics when this is not the case (i.e., BONMIN is not a global optimizer).
In this manual, we will not go into a further description of these algorithms.
Mathematical details of these algorithms and some details of their implementations
can be found in [Bonami et al. 2008] and [Bonami K
lnc Linderoth 2009]
.
Whether or not you are interested in the details of the algorithms, you certainly want to know which one of these six algorithms you should choose to solve your particular problem. For convex MINLPs, experiments we have made on a reasonably large test set of problems point in favor of using B-Hyb (it solved the most of the problems in our test set in 3 hours of computing time). Nevertheless, there are cases where B-OA is much faster than B-Hyb and others where B-BB is interesting. B-QG and B-ECP correspond mainly to a specific parameter setting of B-Hyb but they can be faster in some case. B-iFP is more tailored at finding quickly good solutions to very hard convex MINLP. For nonconvex MINLPs, we strongly recommend using B-BB (the outer-approximation algorithms have not been tailored to treat nonconvex problems at this point). Although even B-BB is only a heuristic for such problems, we have added several options to try and improve the quality of the solutions it provides (see non convex options). Because it is applicable to more classes problem B-BB is the default algorithm in BONMIN.
Note that Lapack and the Blas are free for commercial use from the Netlib Repository, but they are not OSI Certified Open Source Software. The linear solver MA27 is freely available for noncommercial use.
The above software is sufficient to run BONMIN as a stand-alone C++ code, but it does not provide a modeling language. For functionality from a modeling language, BONMIN can be invoked from AMPL (no extra installation is required provided that you have a licensed copy of AMPL installed), though you need the ASL (AMPL Solver Library) which is obtainable from the Netlib.
BONMIN can use FilterSQP [FletcherLeyffer1998] as an alternative to Ipopt for solving NLPs.
Also, in the outer approximation methods B-OA and B-iFP, some MILP problems are solved. By default BONMIN uses Cbc to solve them, but it can also be set up to use the commercial solver Cplex.