GEKKO

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GEKKO is a Python package for machine learning and optimization, specializing in time series analysis and solving differential algebraic equations. It provides an interface to the APMonitor optimization suite, supporting various programming types.

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At a glance

Pricing

—

Free tier

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API

Yes

Skill level

Technical

About

What is GEKKO?

GEKKO is a Python package engineered for machine learning and optimization tasks. Its core capabilities include time series analysis and the efficient resolution of differential algebraic equations (DAE) systems. The package offers a robust interface to the APMonitor optimization suite, enabling users to tackle a wide range of problems. GEKKO supports multiple programming paradigms, including linear, quadratic, nonlinear, and mixed integer programming, making it a versatile tool for complex analytical and optimization challenges.

Best used for

Solving complex optimization problems and analyzing time series data within a Python environment.

Common actions

Analyze time series data

Solve optimization problems

Model dynamic systems

Implement machine learning

Perform numerical analysis

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Capabilities

Key features

Python package
Machine learning
Optimization
Time series analysis
DAE solving

Target Audience

Data ScientistsEngineersResearchersDevelopers

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does GEKKO have a free tier or is it entirely open-source?

GEKKO is an open-source Python package, meaning it is free to use. It provides an interface to the APMonitor optimization suite, which is also freely available for academic and research use, with commercial licenses available for enterprise applications.

What are the typical computational limits or scaling considerations when using GEKKO for large-scale optimization problems?

GEKKO leverages the APMonitor optimization suite, which is designed for large-scale problems. Performance depends on the problem's complexity (e.g., number of variables, constraints, non-linearity) and the solver chosen. Users should consider hardware resources for very large DAE or optimization tasks.

Can GEKKO be integrated with other Python data science libraries like Pandas or NumPy for data preprocessing and post-analysis?

Yes, as a Python package, GEKKO is designed to integrate seamlessly with other Python libraries. Users commonly use Pandas for data handling and NumPy for numerical operations to prepare input data for GEKKO models and analyze results.

What types of differential algebraic equations (DAE) can GEKKO effectively solve, and are there specific limitations?

GEKKO is capable of solving a wide range of DAE systems, including those arising from dynamic optimization and model predictive control. It handles both explicit and implicit DAEs. Limitations typically relate to the problem's numerical stability and the availability of appropriate solvers within APMonitor.

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