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Found 6 papers, 0 papers with code
Tight Bounds for Online Convex Optimization with Adversarial Constraints
A well-studied generalization of the standard online convex optimization (OCO) is constrained online convex optimization (COCO).
Capacity Provisioning Motivated Online Non-Convex Optimization Problem with Memory and Switching Cost
An online non-convex optimization problem is considered where the goal is to minimize the flow time (total delay) of a set of jobs by modulating the number of active servers, but with a switching cost associated with changing the number of active servers over time.
Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
Furthermore, in the case of strongly convex cost and convex constraint functions, the regret guarantee can be improved to $O(\log T)$ while keeping the CCV bound the same as above.
Online Convex Optimization with Switching Cost and Delayed Gradients
In addition, we show that the competitive ratio of any online algorithm is $\max\{\Omega(L), \Omega(\frac{L}{\sqrt{\mu}})\}$ in the limited information setting when the switching cost is quadratic.
On Dynamic Regret and Constraint Violations in Constrained Online Convex Optimization
Subsequently the loss function and the constraint violation penalty evaluated at the chosen action point is revealed.
Continuous Time Bandits With Sampling Costs
CTMAB is fundamentally different than the usual multi-arm bandit problem (MAB), e. g., even the single-arm case is non-trivial in CTMAB, since the optimal sampling frequency depends on the mean of the arm, which needs to be estimated.
