Feng, B.
; Pedrielli, G
; Peng, Y.
; Shashaani, S.
; Song, E.
; Corlu, C.
; Lee, L.
; Chew, E.
; Roeder, T.
; Lendermann, P.
(Ed.)

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.

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