AI RESEARCH
Constrained Online Convex Optimization with Memory and Predictions
arXiv CS.LG
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ArXi:2603.21375v1 Announce Type: new 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.