Buchholz, P.Dayar T.Kriege, J.Orhan, M. C.2018-04-122018-04-1220170166-5316http://hdl.handle.net/11693/37344State based analysis of stochastic models for performance and dependability often requires the computation of the stationary distribution of a multidimensional continuous-time Markov chain (CTMC). The infinitesimal generator underlying a multidimensional CTMC with a large reachable state space can be represented compactly in the form of a block matrix in which each nonzero block is expressed as a sum of Kronecker products of smaller matrices. However, solution vectors used in the analysis of such Kronecker-based Markovian representations require memory proportional to the size of the reachable state space. This implies that memory allocated to solution vectors becomes a bottleneck as the size of the reachable state space increases. Here, it is shown that the hierarchical Tucker decomposition (HTD) can be used with adaptive truncation strategies to store the solution vectors during Kronecker-based Markovian analysis compactly and still carry out the basic operations including vector–matrix multiplication in Kronecker form within Power, Jacobi, and Generalized Minimal Residual methods. Numerical experiments on multidimensional problems of varying sizes indicate that larger memory savings are obtained with the HTD approach as the number of dimensions increases. © 2017 Elsevier B.V.EnglishCompact vectorHierarchical tucker decompositionKronecker productMarkov chainReachable state spaceChainsContinuous time systemsJacobian matricesMarkov processesMatrix algebraStochastic modelsStochastic systemsVectorsContinuous time Markov chainGeneralized minimal residual methodsInfinitesimal generatorMarkovian representationMultidimensional problemsTucker decompositionsVector-matrix multiplicationsVector spacesArticle10.1016/j.peva.2017.08.002