RN-SLRA is a geometry-adaptive regularized Newton method for manifold-affine intersections that guarantees local linear convergence under intrinsic transversality and quadratic convergence under transversality with residual-dependent regularization.
Structured low-rank approximation and its applications
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Develops a scalable distributed gray-box synthesis method for H2 controllers using partial models, data, and ADMM on physical topologies, demonstrated via simulation on the IEEE 39-bus power system.
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A Geometry-Adaptive Regularized Newton-Type Method for Manifold-Affine Intersection Problems
RN-SLRA is a geometry-adaptive regularized Newton method for manifold-affine intersections that guarantees local linear convergence under intrinsic transversality and quadratic convergence under transversality with residual-dependent regularization.
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Distributed Synthesis of Gray-Box Distributed H2 Controllers
Develops a scalable distributed gray-box synthesis method for H2 controllers using partial models, data, and ADMM on physical topologies, demonstrated via simulation on the IEEE 39-bus power system.