Metadata-Version: 2.1
Name: stochoptim
Version: 0.0.5
Summary: StochOptim is a Stochastic Optimization package with scenario generation tools for two- and multi-stage problems
Home-page: https://github.com/julienkeutchayan/StochOptim
Author: Julien Keutchayan
Author-email: j.keutchayan@gmail.com
License: UNKNOWN
Description: # StochOptim
        StochOptim is a Stochastic Optimization package that provides tools for formulating and solving two-stage and 
        multi-stage problems.
        
        Three main reasons why you would like to use it:
        * If you want an easy way to formulate your stochastic problem, represent its uncertainty via a scenario tree, 
        and generate/solve the equivalent deterministic program (EDP).
        * If you want to build non-standard optimized scenario trees from given probability distributions (**scenario generation**)
        * If you want to build non-standard optimized scenario trees directly from historical data (**scenario clustering**)
        
        By *non-standard* we mean scenario trees with non-homogeneous branching structures (which may depend on the stage and 
        the scenarios). \
        By *optimized* we mean scenario trees with a branching structure that minimizes some criterion which measures its 
        suitability to the problem.
        
        The concept of building scenario trees suitable to the problem is gaining popularity over the past years. 
        Suitability is measured by the ability of the EDP to provide optimal decisions that are as close as possible from those 
        of the original problem. It falls under the umbrella of **problem-driven methods**, which are opposed to 
        **distribution-driven methods** where suitability is defined with respect to the uncertainty and not the problem. 
        
        The tools implemented in this package have been developed in the papers: 
        * J. Keutchayan, D. Munder, M. Gendreau (2019) [On the Scenario-Tree Optimal-Value Error for Stochastic Programming Problems](https://pubsonline.informs.org/doi/10.1287/moor.2019.1043) (*Mathematics of Operations Research*)
        * J. Keutchayan, D. Munder, M. Gendreau, F. Bastin (2018) [The Figure of Demerit: A Quality Measure for the Discretization of Probability Distributions in Multistage Stochastic Optimization](https://www.researchgate.net/profile/Julien_Keutchayan/publication/322644958_The_Figure_of_Demerit_A_Quality_Measure_for_the_Discretization_of_Probability_Distributions_in_Multistage_Stochastic_Optimization/links/5bdcddd14585150b2b9a4b82/The-Figure-of-Demerit-A-Quality-Measure-for-the-Discretization-of-Probability-Distributions-in-Multistage-Stochastic-Optimization.pdf)
        * J. Keutchayan, M. Gendreau, F. Bastin (2018) [Problem-Driven Scenario Trees in Multistage Stochastic Optimization: An Illustration in Option Pricing](https://www.researchgate.net/profile/Julien_Keutchayan/publication/328703934_Problem-Driven_Scenario_Trees_in_Multistage_Stochastic_Optimization_An_Illustration_in_Option_Pricing/links/5bdcde684585150b2b9a4b89/Problem-Driven-Scenario-Trees-in-Multistage-Stochastic-Optimization-An-Illustration-in-Option-Pricing.pdf)
        
        for the **scenario generation** part, and in:
        * J. Keutchayan, J. Ortmann, W. Rei (2020) [Problem-Driven Scenario Clustering in Stochastic Optimization]() (soon available)
        
        for the **scenario clustering** part. 
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
