Metadata-Version: 2.1
Name: symro
Version: 0.0.2
Summary: SYMbolic Reformulation and Optimization (SYMRO) package
Home-page: https://github.com/ari-bou/symro
Author: Ariel A. Boucheikhchoukh
Author-email: ariel.boucheikh@gmail.com
License: MIT
Description: The Symbolic Reformulation and Optimization (SymRO) package is a multi-purpose modelling/optimization toolset. The aim of this package is to provide ready-to-use, model-agnostic implementations of advanced optimization algorithms. SymRO reads a problem formulation provided by the user, and constructs a symbolic representation of each construct in the problem. The only input format supported at this time is a text file written in the AMPL modelling language [1]. SymRO comes with a set of tools related to problem reformulation and/or optimization. To solve an optimization problem, SymRO connects to a backend engine. The AMPL engine is the only backend supported at this time.
        
        
        **Input Formats**
        * Model file formulated in the AMPL modelling language
        
        
        **Backends**
        * AMPL (separate installation required)
        
        
        **Features**
        * Generalized Benders Decomposition (GBD) [2]
        
        
        **Planned Features**
        * Convex Relaxation
        * Nonconvex GBD [3]
        * Surrogate Modelling
        * Pyomo support
        
        
        **Acknowledgements**
        
        SymRO was developed under the auspices of the McMaster Advanced Control Consortium (MACC). The support of the MACC is gratefully acknowledged.
        
        
        **References**
        1. Fourer R, Gay DM, Kernighan BW. A Modeling Language for Mathematical Programming.
        Management Science. 1990;36(5):519-554.
        1. Geoffrion A. Generalized Benders Decomposition. Journal of Optimization Theory and
        Applications. 1972;10(4):237-260.
        1. Li X, Tomasgard A, Barton PI. Nonconvex Generalized Benders Decomposition for Stochas-
        tic Separable Mixed-Integer Nonlinear Programs. Journal of Optimization Theory and
        Applications. 2011;151(3):425-454.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 2 - Pre-Alpha
Requires-Python: >=3.8
Description-Content-Type: text/markdown
