Conformal Predictive Programming for Chance Constrained Optimization

Motivated by the advances in conformal prediction (CP), we propose conformal predictive programming (CPP), an approach to solve chance constrained optimization (CCO) problems, i.e., optimization problems with nonlinear constraint functions affected by arbitrary random parameters. CPP utilizes samples from these random parameters along with the quantile lemma -- which is central to CP -- to transform the CCO problem into a deterministic optimization problem. We then present two tractable reformulations of CPP by: (1) writing the quantile as a linear …

advances chance cpp cs.lg cs.sy eess.sy functions math.oc optimization parameters prediction predictive programming quantile random samples solve stat.ml