The Price of Hidden Curvature: An (cid:101) Ω ( 𝑑 5 / 4 √ 𝑇 ) Lower Bound for Bandit Convex Optimization
Nived Rajaraman
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Preprint
Jul 2026
The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization
Improved lower bounds are established on the minimax expected regret of stochastic bandit convex optimization for $1$-Lipschitz functions on the $d$-dimensional Euclidean ball, showing that stochastic bandit convex optimization is fundamentally harder than linear bandits.
Nived Rajaraman, Yanjun Han
· 1 citation