arXiv:2605. 07386v2 Announce Type: replace Abstract: \emph{Convex Optimization with Nested Evolving Feasible Sets (CONES)} is considered where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).
By Karthick Krishna M., Haricharan Balasundaram, Rahul Vaze
arXiv:2608.29074v1 Announce Type: new
Abstract: We study online optimization with nested shrinking feasible regions in two settings: convex optimization with nested evolving feasible sets (CONES) and...
By Dhruv Sarkar, Aprameyo Chakrabartty
arXiv:2610.32740v1 Announce Type: cross
Abstract: We study strongly adaptive regret for Online Convex Optimization (OCO) with time-varying movement costs, a setting that evaluates a learner's perform...
By Andrew Jacobsen, Emmanuel Esposito, Hao Qiu, Mengxiao Zhang
arXiv:2502. 16744v3 Announce Type: replace Abstract: In adversarial Constrained Online Convex Optimization (COCO), a learner selects actions from a fixed convex set while seeking both low regret and low cumulative constraint violation (CCV) under time-varying constraints.
By Yiyang Lu, Mohammad Pedramfar, Mengbo Wang, Vaneet Aggarwal
arXiv:2605. 21107v2 Announce Type: replace Abstract: We study constrained online convex optimization with adversarial time-varying constraints.
By Dhruv Sarkar, Abhishek Sinha
arXiv:2610.03983v2 Announce Type: replace
Abstract: We study constrained online convex optimization with adversarial convex costs and constraints ($\mathsf{COCO}$). At each round $t\in[T]$, a learner...
By Dhruv Sarkar, Abhishek Sinha