GEKKO Optimization Suite

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...

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Bibliographic Details
Published in:Processes 2018-08, Vol.6 (8), p.106
Main Authors: Beal, Logan, Hill, Daniel, Martin, R., Hedengren, John
Format: Article
Language:English
Subjects:
Algebra
Carbon sequestration
Computer simulation
Data processing
Differential equations
Economic models
Libraries
Mathematical problems
Model reduction
Nonlinear control
Nonlinear equations
Optimal control
Optimization
Ordinary differential equations
Packages
Parameter estimation
Partial differential equations
Power
Predictive control
Production planning
Variables
Visualization
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Summary: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.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr6080106