Description: "Subspace Identification for Linear Systems" focuses on thetheory, implementation and applications of "subspaceidentification" algorithms for linear time-invariant finite-dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariablemodels from measured input-output data.The "theory" of subspace identification algorithms is presented indetail. Several chapters are devoted to deterministic, stochastic andcombined deterministic-stochastic subspace identification algorithms.For each case, the geometric properties are stated in a main'subspace' Theorem. Relations to existing algorithms and literatureare explored, as are the interconnections between different subspacealgorithms. The subspace identification theory is linked to the theoryof frequency weighted model reduction, which leads to newinterpretations and insights.The "implementation" of subspace identification algorithms isdiscussed in terms of the robust and computationally efficient RQ andsingular value decompositions, which are well-established algorithmsfrom numerical linear algebra. The algorithms are impleme

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