optimization, python, machine learning, algebraic modeling language, dynamic optimization, model predictive control, moving horizon estimation
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.
Original Publication Citation
BYU ScholarsArchive Citation
Beal, Logan; Hill, Daniel; Martin, Ronald Abraham; and Hedengren, John, "GEKKO Optimization Suite" (2018). Faculty Publications. 4161.
Ira A. Fulton College of Engineering and Technology
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The original paper is found at https://www.mdpi.com/2227-9717/6/8/106
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