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optimization lower bounds

topic1 events
papersTODAY 04:00 UTC

Paper narrows complexity gaps in nonconvex finite-sum optimization

A new arXiv paper studies finite-sum optimization under individual smoothness assumptions, where the best achievable incremental first-order oracle complexity has remained unresolved. It proposes a "dense weak hiding" approach that tightens the gap between existing algorithms, which need roughly n plus sqrt(n) times a smoothness-scaled term, and previously known lower bounds. The results cover both general nonconvex objectives and those satisfying the Polyak-Lojasiewicz condition.

optimization/theory/machine-learning/research