Mirror descent
PulseAugur coverage of Mirror descent — every cluster mentioning Mirror descent across labs, papers, and developer communities, ranked by signal.
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New research shows entropy-smooth convex optimization cannot be accelerated
A new paper published on arXiv by Dragomir et al. demonstrates that entropy-smooth convex optimization cannot be accelerated. The research proves a lower bound for the convergence rate of minimization methods within thi…
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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 includ…
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New framework links group theory to flexible machine learning optimization
Researchers have developed a new framework that combines group theory and group entropies with machine learning to create a flexible family of Mirror Descent optimization algorithms. This approach uses generalized entro…