Introduces the Riemannian ball-proximal point method (RB-PPM) that minimizes geodesically convex functions over metric balls on Hadamard manifolds and proves quasi-Fejér monotonicity, finite termination under constant radii, and convergence when the sum of radii diverges.
Boumal.An introduction to optimization on smooth manifolds
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UNVERDICTED 2representative citing papers
DFSOS computes all sparse discriminant vectors at once with global orthogonality via Bregman iteration and augmented Lagrangian, achieving classification accuracy comparable to or better than deflation-based sparse optimal scoring on synthetic and real time series data.
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Ball-proximal point method on a Hadamard Manifolds
Introduces the Riemannian ball-proximal point method (RB-PPM) that minimizes geodesically convex functions over metric balls on Hadamard manifolds and proves quasi-Fejér monotonicity, finite termination under constant radii, and convergence when the sum of radii diverges.
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Deflation-Free Optimal Scoring
DFSOS computes all sparse discriminant vectors at once with global orthogonality via Bregman iteration and augmented Lagrangian, achieving classification accuracy comparable to or better than deflation-based sparse optimal scoring on synthetic and real time series data.