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.
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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.
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