EAGO.jl: easy advanced global optimization in Julia. (4th March 2022)

• Record Type: Journal Article
• Authors: Wilhelm, M. E.; Stuber, M. D.
• Is Part Of: Optimization methods and software. Volume 37:Number 2(2022)
• Page Start: 425
• Page End: 450
• Publication Date: 2022-03-04
• Subjects: Deterministic global optimization -- nonconvex programming -- McCormick relaxations -- optimization software -- branch-and-bound -- Julia; 90C26 -- 90C34 -- 90C57 -- 90C90; Mathematical optimization -- Periodicals; Algorithms -- Periodicals; 519.7
• DOI: 10.1080/10556788.2020.1786566
• Languages: English
• ISSNs: 1055-6788

Abstract

Abstract : 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.