mli171/Stochastic-Optimization

Project for Stochastic Programming

Stochastic-Optimization

Project for Stochastic Programming

All the programs are written in Optimization Programing Language "Julia".

The contents of Optimization under Uncertainty:

1. L-shaped method (Single Cut); (Single-cut.ipynb)
2. L-shaped method (Multiple Cuts); (multi-cut.ipynb)
3. Level Decomposition; (Level Decomposition.ipynb)
4. Monte Carlo Approach for Sample Average Approximation (SAA); (Monte Carlo SAA.ipynb)

Another example for adding feasibility cuts;

File "feasibility_cut_addition.pdf" contain the example about how to add feasibility cut;

1. Single-cut; (feasibility_single_cut.ipynb)
2. Multi-cut; (feasibility_multi_cut.ipynb)

Note:

1. The L-shape method is the Bender's Decomposition in stochastic programing;
2. Examples are in another pdf file (Example.pdf);
3. This example has been proved to be complete recourse, which means no need on feasibility cuts;
4. In Level Decomposition.ipynb, you need to specify the sample size N by your own;