Sylvester matrix equations are ubiquitous in scientific computing. However, few solution techniques exist for their generalized multiterm version, as they recently arose in stochastic Galerkin finite element discretizations and isogeometric analysis. In this work, we consider preconditioning techniques for the iterative solution of generalized Sylvester equations. They consist in constructing low Kronecker rank approximations of either the operator itself or its inverse. In the first case, applying the preconditioning operator requires solving standard Sylvester equations, for which very efficient solution methods have already been proposed. In the second case, applying the preconditioning operator only requires computing matrix-matrix multiplications, which are also highly optimized on modern computer architectures. Moreover, low Kronecker rank approximate inverses can be easily combined with sparse approximate inverse techniques, thereby further speeding up their application with little or no damage to their preconditioning capability.

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