Wednesday, 15:45 - 16:10 h, Room: MA 141


Ruiwei Jiang
Optimization under data-driven chance constraints

Coauthor: Yongpei Guan


Chance constraint is an effective and convenient modeling tool of decision making in uncertain environment. Unfortunately, the solution obtained from a chance-constrained optimization problem might be questionable due to the accessibility of the probability distribution of the random parameters. Usually, decision makers have no access to the distribution itself, but can only observe a series of data sampled from the true (while ambiguous) distribution. In this talk, we develop exact approaches to deal with the data-driven chance constraints (DCC). Starting from the historical data, we construct two types of confidence sets for the ambiguous distribution through statistical estimation of its moments and density functions, respectively. We then formulate DCC as a robust version of chance constraints by allowing the ambiguous distribution to run adversely within its confidence set. By deriving equivalent reformulations, we show that DCC with both (moment- and density-based) confidence sets can be efficiently solved. In addition, we depict the relation between the risk level of DCC and the sample size of historical data, which can a priori determine the robustness of DCC.


Talk 2 of the invited session Wed.3.MA 141
"Algorithms and applications for stochastic programming" [...]
Cluster 22
"Stochastic optimization" [...]


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