序贯蒙特卡洛matlab代码-sequential_chance_constrained:[OR2011]Matlab代码，用于...

# Sequential Convex Approximations to Joint Chance Constrained Programs: A Monte Carlo Approach ## Introduction This is a Matlab implementation of the sequential convex approximation algorithms for joint chance constrained problem. It includes a comparison between both conditional value-at-risk (CVaR) and sequential convex approximation for value-at-risk (Iterative dc). ## Using the code Use Matlab to run `example_run.m` directly. You may expect to see the result figure below: <img src="result.jpg" width="512"> ## Files explanation: - `example_run.m`: runing file, first open - `main_function.m`: including generating samples, apply cvar approximation, epsilon approximation and dc approximation, return results for a particular setting - `gensample.m`: generate normal distributions for all random variables - `obj_fun.m`: objective function - `quantile.m`: quantile for constraints - `opt_cvar.m, opt_dc.m, opt_eps.m`: optimization for cvar, one step dc approximation, epsilon approximation - `con_fun_cvar.m, con_fun_dc.m, con_fun_eps.m`: constraints for cvar, one step dc approximation, epsilon approximation - `lincave.m`: linear approximation for concave function ## Citation @article{hong2011sequential, title={Sequential convex approximations to joint chance constrained programs: A Monte Carlo approach}, author={Hong, L Jeff and Yang, Yi and Zhang, Liwei}, journal={Operations Research}, volume={59}, number={3}, pages={617--630}, year={2011}, publisher={INFORMS} }