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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math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
New stochastic method based on decomposed search directions and Fletcher's augmented Lagrangian achieves O(ε^{-3}) expected oracle complexity for nonconvex equality-constrained optimization.
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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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A Fletcher's Augmented Lagrangian-Based Stochastic First-Order Method for Nonconvex Equality-Constrained Optimization
New stochastic method based on decomposed search directions and Fletcher's augmented Lagrangian achieves O(ε^{-3}) expected oracle complexity for nonconvex equality-constrained optimization.