A smoothing-aware AdaGrad Riemannian gradient method achieves O(ε^{p-4}) global complexity for non-Lipschitz manifold optimization with p-norm penalties (p in (0,1]), recovering the known O(ε^{-3}) rate when p=1.
arXiv:2508.19234 (2025)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
FSIPL is a feasibility-safeguarded inexact proximal linearized method for nonsmooth composite optimization on compact embedded submanifolds, with O(ε^{-2}) complexity, subsequential convergence, and full convergence under a KL assumption.
citing papers explorer
-
An Adaptive Smoothing Algorithm for Non-Lipschitz Optimization on Manifolds with Complexity Guarantees
A smoothing-aware AdaGrad Riemannian gradient method achieves O(ε^{p-4}) global complexity for non-Lipschitz manifold optimization with p-norm penalties (p in (0,1]), recovering the known O(ε^{-3}) rate when p=1.
-
An Infeasible Method with Feasibility Safeguard for Nonsmooth Composite Optimization Over Manifolds
FSIPL is a feasibility-safeguarded inexact proximal linearized method for nonsmooth composite optimization on compact embedded submanifolds, with O(ε^{-2}) complexity, subsequential convergence, and full convergence under a KL assumption.