A unifying framework for ADI-like methods for linear matrix equations and beneficial consequences
Jonas Schulze, Jens Saak

TL;DR
This paper introduces a unifying ADI framework for linear matrix equations that allows arbitrary initial values, leading to improved efficiency and significant speed-ups in solving Lyapunov and Riccati equations.
Contribution
It extends ADI methods to arbitrary low-rank initial values and generalizes key properties, enhancing performance and flexibility in solving linear matrix equations.
Findings
Significant reduction in ADI steps with new initial values.
17% and 8x speed-up over zero initial value in experiments.
Improved efficiency in solving Lyapunov and Riccati equations.
Abstract
We derive the alternating-directions implicit (ADI) method based on a commuting operator split and apply the results in detail to the continuous time algebraic Lyapunov equation with low-rank constant term and approximate solution, giving pointers for the Sylvester case. Previously, it has been mandatory to start the low-rank ADI for Lyapunov equations (CF-ADI, LR-ADI, G-LR-ADI) or Sylvester equations (fADI, G-fADI) with an all-zero initial value. Our approach extends the known efficient iteration schemes of low-rank increments and residuals to arbitrary low-rank initial values for all these methods. We further generalize two properties of the low-rank Lyapunov ADI to the generic ADI applied to arbitrary linear equations using a commuting operator split, namely the invariance of iterates under permutations of the shift parameters, and the efficient handling of complex shift parameters.…
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Taxonomy
TopicsSparse and Compressive Sensing Techniques · Control Systems and Identification · Advanced Control Systems Optimization
