Python Gekko v1.3.0

Gekko v1.3.0 introduces several enhancements aimed at expanding modeling capabilities and improving user experience. This release adds energy dispatch optimization benchmark problems available through a new Jupyter notebook, along with the integration of tree-based models into the ML.py module, complete with accompanying documentation. The solver extension package now includes more descriptive error handling, aiding in troubleshooting and interpretation of solver feedback. Connectivity has been improved with an updated websocket address for GenAI Gekko support and a new GenAI Support Agent that enables real-time streaming responses in Jupyter notebooks and console applications. Additionally, the release resolves Issue #163, which previously caused the final solver option to be ignored in local solves, and updates the LICENSE file to the latest version of the open-source MIT license.

Python Gekko v1.2.1

GEKKO v1.2.1 brings several new features and improvements to the Python package for machine learning and optimization of mixed-integer and differential algebraic equations. This release includes the following updates:

• Pyomo Extension and Interface
• Example Jupyter Notebook showcasing Optimization Under Uncertainty with a Gaussian Process Regression (GPR)
• The gk_solver_extension.py file has been divided into three separate files to enhance modularity and maintainability
• Solver Extension Option: The m.options.SOLVER_EXTENSION option has been modified for improved flexibility
  • 0 / GEKKO (default): Solver extension off, solve with GEKKO as usual.
  • 1 / AMPL / AMPLPY: Solver extension on, solve through amplpy, maintaining compatibility with previous versions.
  • 2 / PYOMO: Solver extension on, solve through Pyomo.
• Documentation Updates: Significant updates to the documentation

Thank you for your continued support and contributions to the GEKKO community!

Python Gekko v1.1.3

Version 1.1.3 Stable Release, Date: 2024-06-17

• Improve model initialization (Timer 47) with improved duplicate variable name searching for larger models
• Update local APM executables to v1.0.3 with remote=False
  • Windows apm.exe still 32-bit, includes APOPT, BPOPT, IPOPT solvers
  • Linux apm 64-bit, includes APOPT and BPOPT solvers
  • MacOS apm_mac 64-bit, includes APOPT and BPOPT solvers
  • ARM: apm_arm 32 bit, aarch64 64 bit includes BPOPT solver
• Web service remote=True is 64-bit with APOPT, BPOPT, IPOPT solvers

Python Gekko v1.1.1

Gekko is a Python library designed for optimization of mixed-integer and differential algebraic equations. It is coupled with large-scale solvers for various types of programming, including linear, quadratic, nonlinear, and mixed integer programming (LP, QP, NLP, MILP, MINLP). Gekko is versatile and supports various modes of operation such as parameter regression, data reconciliation, real-time optimization, dynamic simulation, and nonlinear predictive control.

Primarily, Gekko functions as an object-oriented library that makes it easier to execute local solvers like APMonitor, which it uses on the backend to solve optimization problems. The library simplifies the declaration of constants, parameters, and variables, and it organizes equations and objective functions for optimization tasks. These features make Gekko a powerful tool for both educational purposes and real-world applications in fields where optimization is required【21†source】.

Full Changelog: v1.1.0...v1.1.1

Python Gekko v1.1.0

Python Gekko with Generative AI support

Python Gekko v1.0.7

GEKKO is a python package for machine learning and optimization, specializing in dynamic optimization of differential algebraic equations (DAE) systems. It is coupled with large-scale solvers APOPT and IPOPT for linear, quadratic, nonlinear, and mixed integer programming. Capabilities include machine learning, discrete or continuous state space models, simulation, estimation, and control.

Python Gekko v1.0.6

GEKKO is a Python package for machine learning and optimization of mixed-integer and differential algebraic equations. It is coupled with large-scale solvers for linear, quadratic, nonlinear, and mixed integer programming (LP, QP, NLP, MILP, MINLP). Modes of operation include parameter regression, data reconciliation, real-time optimization, dynamic simulation, and nonlinear predictive control.

Python Gekko v1.0.5

Python Gekko with new interface to GPflow and Scikit-learn to import ML models

Python Gekko v1.0.4

Algorithm improvements and preparation for hosting package on conda-forge, in addition to pypi.org.

Python Gekko v1.0.2

Minor bug fixes for cycling simulations