GEKKO Optimization Suite

By Logan D. R. Beal, Daniel C. Hill, R. Abraham Martin, John D. Hedengren. Processes, 2018

350 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9b258147d394a3665eb0a0cc903475aaa1ecf4c2

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In Computational Theory and Mathematics

  1. I. Werning
  2. Victor Chernozhukov
  3. Daron Acemoglu
  4. M. Whinston

In Control and Systems Engineering

  1. Victor Chernozhukov
  2. Alexandre Belloni
  3. Antonio F. Galvao
  4. Kengo Kato
  5. Max H. Farrell
  6. Ivan A. Canay

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