Abstract
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several examples of problems that are enabled by the GEKKO library.
Highlights
Computational power has increased dramatically in recent decades
Computational power and architectures have expanded the capabilities of technology to new levels of automation and intelligence with rapidly expanding artificial intelligence capabilities and computer-assisted decision processing
GEKKO is an algebraic modeling language (AML) for posing optimization problems in simple object-oriented equation-based models to interface with powerful built-in optimization solvers but is a package with the built-in ability to run model predictive control, dynamic parameter estimation, real-time optimization, and parameter update for dynamic models on real-time applications
Summary
Computational power has increased dramatically in recent decades. In addition, there are new architectures for specialized tasks and distributed computing for parallelization. Computational power and architectures have expanded the capabilities of technology to new levels of automation and intelligence with rapidly expanding artificial intelligence capabilities and computer-assisted decision processing. These advancements in technology have been accompanied by a growth in the types of mathematical problems that applications solve. Machine learning (ML) has become the must-have technology across all industries, largely inspired by the recent public successes of new artificial neural network (ANN) applications. Another valuable area that is useful in a variety of applications is dynamic optimization. The purpose of this article is to introduce the unique capabilities in GEKKO and to place this development in context of other packages
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