%global _empty_manifest_terminate_build 0 Name: python-gekko Version: 1.0.6 Release: 1 Summary: Machine learning and optimization for dynamic systems License: MIT URL: https://github.com/BYU-PRISM/GEKKO Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f9/9e/e34c5eb9943a1e8b089577cbd924a46f51164c0af64fd15e815bd468ca51/gekko-1.0.6.tar.gz BuildArch: noarch Requires: python3-numpy %description 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. Gekko models consist of equations and variables that create a symbolic representation of the problem for a single data point or single time instance. Solution modes then create the full model over all data points or time horizon. Gekko supports a wide range of problem types, including: - Linear Programming (LP) - Quadratic Programming (QP) - Nonlinear Programming (NLP) - Mixed-Integer Linear Programming (MILP) - Mixed-Integer Quadratic Programming (MIQP) - Mixed-Integer Nonlinear Programming (MINLP) - Differential Algebraic Equations (DAEs) - Mathematical Programming with Complementarity Constraints (MPCCs) - Data regression / Machine learning - Moving Horizon Estimation (MHE) - Model Predictive Control (MPC) - Real-Time Optimization (RTO) - Sequential or Simultaneous DAE solution Gekko compiles the model into byte-code and provides sparse derivatives to the solver with automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute resources. - [Gekko Homepage](https://machinelearning.byu.edu) - [Gekko Documentation](https://gekko.readthedocs.io/en/latest/) - [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization) - [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko) %package -n python3-gekko Summary: Machine learning and optimization for dynamic systems Provides: python-gekko BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-gekko 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. Gekko models consist of equations and variables that create a symbolic representation of the problem for a single data point or single time instance. Solution modes then create the full model over all data points or time horizon. Gekko supports a wide range of problem types, including: - Linear Programming (LP) - Quadratic Programming (QP) - Nonlinear Programming (NLP) - Mixed-Integer Linear Programming (MILP) - Mixed-Integer Quadratic Programming (MIQP) - Mixed-Integer Nonlinear Programming (MINLP) - Differential Algebraic Equations (DAEs) - Mathematical Programming with Complementarity Constraints (MPCCs) - Data regression / Machine learning - Moving Horizon Estimation (MHE) - Model Predictive Control (MPC) - Real-Time Optimization (RTO) - Sequential or Simultaneous DAE solution Gekko compiles the model into byte-code and provides sparse derivatives to the solver with automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute resources. - [Gekko Homepage](https://machinelearning.byu.edu) - [Gekko Documentation](https://gekko.readthedocs.io/en/latest/) - [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization) - [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko) %package help Summary: Development documents and examples for gekko Provides: python3-gekko-doc %description help 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. Gekko models consist of equations and variables that create a symbolic representation of the problem for a single data point or single time instance. Solution modes then create the full model over all data points or time horizon. Gekko supports a wide range of problem types, including: - Linear Programming (LP) - Quadratic Programming (QP) - Nonlinear Programming (NLP) - Mixed-Integer Linear Programming (MILP) - Mixed-Integer Quadratic Programming (MIQP) - Mixed-Integer Nonlinear Programming (MINLP) - Differential Algebraic Equations (DAEs) - Mathematical Programming with Complementarity Constraints (MPCCs) - Data regression / Machine learning - Moving Horizon Estimation (MHE) - Model Predictive Control (MPC) - Real-Time Optimization (RTO) - Sequential or Simultaneous DAE solution Gekko compiles the model into byte-code and provides sparse derivatives to the solver with automatic differentiation. Gekko includes data cleansing functions and standard tag actions for industrially hardened control and optimization on Windows, Linux, MacOS, ARM processors, or any other platform that runs Python. Options are available for local, edge, and cloud solutions to manage memory or compute resources. - [Gekko Homepage](https://machinelearning.byu.edu) - [Gekko Documentation](https://gekko.readthedocs.io/en/latest/) - [Gekko Examples](https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization) - [Get Gekko Help on Stack Overflow](https://stackoverflow.com/questions/tagged/gekko) %prep %autosetup -n gekko-1.0.6 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-gekko -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 1.0.6-1 - Package Spec generated