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This technical note investigates the model reduction problem for mean square stable discrete time Markov jump linear systems. For this class of systems a balanced truncation algorithm is developed. The reduced order model is suboptimal, however the approximation error, which is captured by means of the stochastic L2 gain, is bounded from above by twice the sum of singular numbers associated to the truncated states of each mode. Such a result allows rigorous simplification of the dynamics of each mode in an independent manner with respect to a metric which is relevant from a robust control point of view. [ABSTRACT FROM PUBLISHER]

In this technical note we introduce a multivariable extension of a recently proposed model reduction algorithm for linear time-invariant systems. Reduced models are found by solving a convex optimization problem with Linear Matrix Inequality constraints given a state space model or frequency samples of a model. In order to illustrate the method we apply it to a large-scale model of a deformable mirror. [ABSTRACT FROM AUTHOR]

This paper develops a generalization of the balanced truncation algorithm applicable to a class of discrete-time stochastic jump linear systems. The approximation error, which is captured by means of the stochastic L2 gain, is bounded from above by twice the sum of singular numbers associated to the truncated states, similar to the case of linear time-invariant systems. A two step model reduction algorithm for hidden Markov models is also developed. The first step relies on the aforementioned balanced truncation algorithm due to a topological equivalence established between hidden Markov models and a subclass of stochastic jump linear systems. In a second step the positivity constraints, which reflect the hidden Markov model structure, are enforced by solving a low dimensional optimization problem. [ABSTRACT FROM AUTHOR]