Joint dynamic probabilistic constraints with projected linear decision rules
Submission Status: | published |
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- We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto
hard constraints of wait-and-see type. We establish the relation between the original (infinite
dimensional) problem and approximating problems working with projections from different subclasses of decision policies. Considering the subclass of linear decision rules and a generalized
linear model for the underlying stochastic process with noises that are Gaussian or truncated
Gaussian, we show that the value and gradient of the objective and constraint functions of the
approximating problems can be computed analytically.