A new class of low-rank short recurrences is proposed for nonsymmetric linear matrix equations, combining subspace projection with truncation and randomization to limit memory while accelerating convergence.
On the eigenvalue decay of solutions to operator
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A class of low-rank short recurrences for nonsymmetric linear matrix equations
A new class of low-rank short recurrences is proposed for nonsymmetric linear matrix equations, combining subspace projection with truncation and randomization to limit memory while accelerating convergence.