Cosimla with General Regeneration Set to Compute Markov Chain Stationary Expectations.

We extend the COSIMLA approach (short for "COmbined SIMulation and Linear Algebra") recently developed in Zheng, Infanger, and Glynn (2022) to compute stationary expectations for Markov chains with large or infinite discrete state space. Our work follows the idea of combing the best of linear algebra and simulation—using linear algebra to compute the "center" of the state space and using simulation to compute the contributions from outside of the "center". Different from Zheng, Infanger, and Glynn (2022) that needed to fix a single regeneration state, our work develops a new method that allows the use of a flexible regeneration set with a finite number of states. We show that this new method allows more efficient computation for the COSIMLA approach.