New Mirror Descent Framework Extends Optimization to Riemannian Manifolds

Researchers have developed a generalized framework for Mirror Descent (MD) on Riemannian manifolds, extending its applicability to complex optimization problems. This new Riemannian Mirror Descent (RMD) framework includes a stochastic variant and provides non-asymptotic convergence guarantees. The RMD framework simplifies to Curvilinear Gradient Descent (CGD) when applied to the Stiefel manifold, and its stochastic extension effectively tackles large-scale manifold optimization. AI

RANK_REASON This is a research paper published on arXiv detailing a new mathematical framework for optimization. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Curvilinear Gradient Descent
• Jiaxin Jiang
• Mirror Descent
• Riemannian manifold
• Stiefel manifold

• paper
• other

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