A flexible-framework for global and robust optimization in Julia
- Matthew Wilhelm, Department of Chemical and Biomolecular Engineering, University of Connecticut (UCONN)
EAGO is a global and robust optimization platform based on McCormick relaxations. It contains the first widely accessible global optimization routine based on generalized McCormick relaxations. With the exception of calls to local solvers and linear algebra routines, EAGO is written entirely in native Julia. The solver is quite flexibly arranged so the end user can easily customize low-level routines.
EAGO is registered Julia package. It can be installed using the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run the following command
pkg> add EAGO
Currently, EAGO is tied to a 0.21.2+ or greater version of JuMP. This allows a replication of some of the internal features shared by EAGO and JuMP's AD scheme aka generation of Wergert Tapes pass evaluators between JuMP and EAGO etc.
pkg> add JuMP
EAGO v0.4 is the current version requires Julia 1.2+. Use with version 1.4 is recommended as the majority of in-house testing has occured using this version of Julia. The user is directed to the High-Performance Configuration for instructions on how to install a high performance version of EAGO (rather than the basic entirely open-source version). If any issues are encountered when loading EAGO (or when using it), please submit an issue using the Github issue tracker.
A few examples are provided in the documentation website. More involved examples are provided at in the form of Jupyter Notebooks at EAGO-notebooks and can be run using IJulia. To add IJulia
pkg> add IJulia
Then launch the Jupyter notebook using the following command from the Julia terminal,
julia> using IJulia; notebook()
Then simply navigate to the example directory and run the example of most interest.