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Online Inverse Linear Optimization Algorithm Achieves O(d) Regret
A new arXiv paper presents a deterministic algorithm for online inverse linear optimization that attains O(d) regret, a bound that holds uniformly across the time horizon. Each round requires O(d^2) computation time. The result matches a recent bound by Dewasurendra and addresses an open question raised by Gollapudi and colleagues.