Exploiting structure of chance constrained programs via submodularity

TL;DR

This paper presents a new method to efficiently solve chance constrained programs by exploiting problem structure and submodularity, significantly reducing computational costs through optimal constraint partitioning.

Contribution

It introduces a submodularity-based polynomial-time algorithm for optimal constraint partitioning in chance constrained programs, improving computational efficiency.

Findings

Computational cost savings can be arbitrarily large.

The approach maintains probabilistic guarantees.

Demonstrated effectiveness on production and multi-agent planning cases.

Abstract

We introduce a novel approach to reduce the computational effort of solving mixed-integer 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 using submodularity of the support rank of a set of constraints, we propose a polynomial-time algorithm to find suboptimal solutions to this partitioning problem and we give approximation guarantees for special classes of cost metrics. We illustrate that the resulting computational cost savings can be arbitrarily large and demonstrate our approach on two case studies from production and multi-agent planning.