Exploiting structure of chance constrained programs via submodularity
Damian Frick, Pier Giuseppe Sessa, Tony A Wood, Maryam Kamgarpour
Automatica | Elsevier | Published : 2019
We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario program. We exploit the problem structure by efficiently partitioning the constraint function and considering a multiple chance constrained program that gives the same probabilistic guarantees as the original single chance constrained problem. We formulate the problem of finding the optimal partition, a partition achieving the lowest computational cost, as an optimization problem with nonlinear objective and combinatorial constraints. By us..View full abstract
Awarded by Swiss National Science Foundation
The work of M. Kamgarpour is gratefully supported by Swiss National Science Foundation, under the grant SNSF 200021_172782.