Linear polyhedral cone constrained distribution robust optimization problem is a processing method for linear polyhedral cone constrained optimization problems with parameters, which are constrained in uncertain sets in the form of probability distribution. There are many ways to define uncertain sets, regarding that the uncertainty set based on KL-divergence as a distribution function is reasonable. When solving such problems, from the definition of divergence, the unknown empirical distribution is transformed into a known empirical distribution, using classical cutting-plane method, given convergence analysis and scientific experiments.

KL-divergence, distributed robust optimization, cone constraint, cutting-plane method.