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minimax-optimization

topic1 events
papersTODAY 04:00 UTC

Riemannian ascent-descent algorithm targets saddle points in nonconvex minimax problems

A new arXiv paper studies distributionally robust optimization problems framed as minimax games over a Euclidean space paired with a Riemannian manifold. The authors propose a Riemannian ascent-descent method and show it converges to basin saddle points in these nonconvex, nonconcave landscapes. The work connects geometric optimization theory with practical robust statistical risk modeling.

optimization/minimax/robust-optimization/research