A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
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RR2D completes partial sample covariance matrices under stationarity and structural constraints to enable hybrid sample matrix inversion beamforming that approaches hybrid MVDR performance on 32-element arrays.
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A second-order method landing on the Stiefel manifold via Newton$\unicode{x2013}$Schulz iteration
A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
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Hybrid SMI Realization via Matrix Completion and Riemannian Manifold Optimization on Narrowband Sub-Array Based Architectures
RR2D completes partial sample covariance matrices under stationarity and structural constraints to enable hybrid sample matrix inversion beamforming that approaches hybrid MVDR performance on 32-element arrays.