Metadata-Version: 2.1
Name: LPFICS
Version: 0.0.1
Summary: LPFICS aims to enable to find ONE infeasible constraint set if a model is considered infeasible once resolved
Home-page: UNKNOWN
Author: Guillaume VERGER, Lou MORRIET
Author-email: omegalpes-users@groupes.renater.fr
License: UNKNOWN
Description: LPFICS Lib to find incompatible constraints set in Linear Problems
        ================================================================
        
        LPFICS Idea
        -----------
        **Objective**: This project aims to enable to find ONE incompatible constraint set if a 
        model is considered infeasible once resolved.
        Note that it MAY NOT be the ONLY ONE set of constraints making your problem infeasible.
        
        This project is based on PuLP (https://github.com/coin-or/pulp) a LP modeler written 
        in Python. PuLP integrates the solver CBC. This solution works with CBC, and GLPK solvers
        but does not work with Gurobi (as the output is always NotSolved and not Infeasible if 
        the problem is overconstrainted).
        
        
        Here is a simplified explaination:
        If a problem is overconstrainted, the problem will:
        - test to solve the problem with the first half of the constraints
            - if there is no solution it continues taking the first half (of the half) 
            of the constraints
            - if there is a solution, it will test the other half
                - if the second half has no solution it will continue dividing the second half
                set of constraints to test it
                - if the second half has a solution it will rearrange the constraints and 
                test again
        
        
        References
        ----------
        The methodology (quickXplain) on which the program is based is described, discussed and modeled on the following references: 
        
        Junker, Ulrich. 2004. Â« QUICKXPLAIN: Preferred Explanations and Relaxations 
        for Over-Constrained Problems Â», In: _AAAI-04_. San Jose. https://www.aaai
        .org/Papers/AAAI/2004/AAAI04-027.pdf
        
        Morriet, Lou, Benoit Delinchant, Gilles Debizet, FrÃ©dÃ©ric Wurtz. 2020. Â« 
        Algorithme d identification de contraintes incompatibles pour les problemes 
        d optimisation : application Ã  un projet energetique Â». In: _soumis Ã  IBPSA 
        2020_. 
        
        Rodler, Patrick. 2020. Â« Understanding the QuickXPlain Algorithm: Simple 
        Explanation and Formal Proof Â». ArXiv:2001.01835, janvier. http://arxiv
        .org/abs/2001.01835.
        
        Verger, Guillaume, 2016. Ruby-Cbc [Logiciel]. Ruby. In : _Github_. Consulte
         le 15 novembre 2020, https://github.com/gverger/ruby-cbc.
        
        
        
        Installation Help
        -----------------
        Please install LPFICS Lib with pip using the command prompt and type:   
        
        pip install git+https://gricad-gitlab.univ-grenoble-alpes.fr/omegalpes/lpfics.git
        
        Please ask omegalpes-users@groupes.renater.fr if you are struggling during your installation
        
        
        Library Installation Requirements
        ---------------------------------
        PuLP > 1.6.10
         
         
        Copyright
        =========
        This project contains a derivative work based on https://github.com/gverger/ruby-cbc. 
        Project ruby-cbc is released under the MIT license, Copyright 2016 Guillaume Verger.
        Modifications Copyright 2019 G2Elab / MAGE
        See the LICENSE file for copyright information.
         
         
        Main Authors: 
        =============
        Guillaume VERGER
        
        Lou MORRIET
        
        
        Partners:
        =========
        Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, CEA, UniversitÃ© Paris-Sud
        
        
        Acknowledgments:
        ================
        This work has been partially supported by the CDP Eco-SESA receiving fund from the French National Research 
        Agency in the framework of the "Investissements dâ€™avenir" program (ANR-15-IDEX-02), by the ADEME, the French Agency for Environment and Energy
        Management, with the RETHINE project and by the Region Auvergne RhÃ´ne Alpes with the project OREBE.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Debuggers
Description-Content-Type: text/markdown
