Constrained Online Convex Optimization with Memory and Predictions
We study Constrained Online Convex Optimization with Memory (COCO-M), where both the loss and the constraints depend on a finite window of past decisions made by the learner. This setting extends the previously studied unconstrained online optimization with memory framework and captures practical problems such as the control of constrained dynamical systems and scheduling with reconfiguration budgets. For this problem, we propose the first algorithms that achieve sublinear regret and sublinear cumulative constraint violation under time-varying constraints, both with and without predictions of future loss and constraint functions. Without predictions, we introduce an adaptive penalty appro...
| Authors | Mohammed Abdullah, George Iosifidis, Salah Eddine Elayoubi et al. |
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| Primary category | cs.LG |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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