Object-Oriented Implementation and Parallelization of the Rapid Gaussian Markov Improvement Algorithm

The Rapid Gaussian Markov Improvement Algorithm (rGMIA) solves discrete optimization via simulation problems by using a Gaussian Markov random field and complete expected improvement as the sampling and stopping criterion. rGMIA has been created as a sequential sampling procedure run on a single processor. In this paper, we extend rGMIA to a parallel computing environment when $q$ + 1 solutions can be simulated in parallel. To this end, we introduce the q-point complete expected improvement criterion to determine a batch of $q$ + 1 solutions to simulate. This new criterion is implemented in a new object-oriented rGMIA package.