Analyzing Markov Chains using Kronecker Products: Theory and by Tugrul Dayar

By Tugrul Dayar

Kronecker items are used to outline the underlying Markov chain (MC) in quite a few modeling formalisms, together with compositional Markovian types, hierarchical Markovian types, and stochastic method algebras. the incentive at the back of utilizing a Kronecker based illustration instead of a flat one is to relieve the garage standards linked to the MC. With this procedure, structures which are an order of significance better may be analyzed at the comparable platform. The advancements within the resolution of such MCs are reviewed from an algebraic perspective and attainable components for extra learn are indicated with an emphasis on preprocessing utilizing reordering, grouping, and lumping and numerical research utilizing block iterative, preconditioned projection, multilevel, decompositional, and matrix analytic equipment. Case reviews from closed queueing networks and stochastic chemical kinetics are supplied to inspire decompositional and matrix analytic equipment, respectively.

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1/. h/ Qk ¤ I for h D 1; : : : ; H or all Qk D I for h D l C 2; : : : ; H . Such vectors need not be stored either. H hD2 n P H 1 QH requirement of at most K lD1 n floating-point values to facilitate the hDlC1 h Kronecker representation of the aggregated CTMCs. The grouping of factors will further reduce the storage requirement for vectors. ). Consider our three-dimensional problem with parameter set . 0;0/ D 1. 16), respectively. 1/ . 0; 1/. 3/ diagonal of QQ 0;1 since Q1 D Q1 D I . Q1 e/ D I .

N1 ; : : : ; nH / 2 N satisfies H hD1 nh D K. nh / depends on column h of P , the states of the queues corresponding to nonzero elements in that column, and the rates by which the queues complete the last phases of their service processes for h D 1; : : : ; H . nh / for queue h is not known a priori but is to be determined as a result of steady-state analysis. 2). n; m/ is nonzero if customers who depart from queue h can join queue l. h; l/ ¤ 0 and nl < cl . h; l/ D 0 are zero. 3)], plus a diagonal correction (first term) so as to sum up the rows to zero.

S; s/j; 1/ is the uniformization parameter of the power method and ! 0; 2/ is the relaxation parameter of the BJOR and BSOR methods. Here, forward iteration refers to computing unknowns ordered toward the beginning of the state space earlier than unknowns ordered later in the state space. The power method works at level l D H since it is a point method. Furthermore, BJOR and BSOR reduce to the block Jacobi (BJacobi) and block Gauss–Seidel (BGS) methods for ! D 1, and they become point JOR and point SOR methods for l D H .

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