"On-the-fly" solution techniques for stochastic Petri nets and extensions

High level modeling representations, such as stochastic Petri nets, frequently generate very large state spaces and corresponding state transition rate matrices. We propose a new steady state solution approach that avoids explicit storing of the matrix in memory. This method does not impose any structural restrictions on the model, uses Gauss Seidel and variants as the numerical solver, and uses less memory than current state of the art solvers. An implementation of these ideas shows that one can realistically solve very large, general models in relatively little memory.