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Model reduction of discrete-time Markov jump linear systems

This paper proposes a model reduction algorithm for discrete-time, Markov jump linear systems. The main point of the reduction method is the formulation of two generalized dissipation inequalities that in conjunction with a suitably defined storage function enable the derivation of reduced order models that come with a provable a priori upper bound on the stochastic L2 gain of the approximation error