EAGO - Easy Advanced Global Optimization in Julia

A flexible framework for global and robust optimization in Julia.


  • Matthew Wilhelm, Department of Chemical and Biomolecular Engineering, University of Connecticut (UCONN)
  • Robert Gottlieb, 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 flexibly arranged so the end user can easily customize low-level routines.

Installing EAGO

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 version 1.0.0 - 1.1.1 of JuMP. This allows a replication of some of the internal features shared by EAGO and JuMP's automatic differentiation scheme, e.g., generation of Wergert Tapes, passing evaluators between JuMP and EAGO, etc.

pkg> add JuMP

EAGO v0.7.1 is the current version, and it requires Julia 1.6+. Use with Julia 1.7 is recommended as the majority of in-house testing occured using this version. The user is directed to the High-Performance Configuration section for instructions on how to set up a higher performance version of EAGO (as opposed to 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.


Several examples are provided within this documentation, but additional examples are provided in the form of Jupyter Notebooks at EAGO-notebooks, which can be run using IJulia. To add IJulia, run the command:

pkg> add IJulia

Then launch the Jupyter notebook using the following command from the Julia terminal:

julia> using IJulia; notebook()

And then simply navigate to the example directory and run the example of most interest.