4.5 Article

EAGO.jl: easy advanced global optimization in Julia

Journal

OPTIMIZATION METHODS & SOFTWARE
Volume 37, Issue 2, Pages 425-450

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/10556788.2020.1786566

Keywords

Deterministic global optimization; nonconvex programming; McCormick relaxations; optimization software; branch-and-bound; Julia

Funding

  1. National Science Foundation [1932723]
  2. University of Connecticut
  3. Directorate For Engineering
  4. Div Of Chem, Bioeng, Env, & Transp Sys [1932723] Funding Source: National Science Foundation

Ask authors/readers for more resources

EAGO is an extensible open-source global optimizer developed in Julia language. It offers unique implementations for constructing convex/concave relaxations and parsing/symbolic transformation of user-defined functions for improved solution speed. EAGO also provides compatibility with various local optimizers and transcendental function libraries, accessible through JuMP modeling language.
An extensible open-source deterministic global optimizer (EAGO) programmed entirely in the Julia language is presented. EAGO was developed to serve the need for supporting higher-complexity user-defined functions (e.g. functions defined implicitly via algorithms) within optimization models. EAGO embeds a first-of-its-kind implementation of McCormick arithmetic in an Evaluator structure allowing for the construction of convex/concave relaxations using a combination of source code transformation, multiple dispatch, and context-specific approaches. Utilities are included to parse user-defined functions into a directed acyclic graph representation and perform symbolic transformations enabling dramatically improved solution speed. EAGO is compatible with a wide variety of local optimizers, the most exhaustive library of transcendental functions, and allows for easy accessibility through the JuMP modelling language. Together with Julia's minimalist syntax and competitive speed, these powerful features make EAGO a versatile research platform enabling easy construction of novel meta-solvers, incorporation and utilization of new relaxations, and extension to advanced problem formulations encountered in engineering and operations research (e.g. multilevel problems, user-defined functions). The applicability and flexibility of this novel software is demonstrated on a diverse set of examples. Lastly, EAGO is demonstrated to perform comparably to state-of-the-art commercial optimizers on a benchmarking test set.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available