Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Outperformance inverse optimization learns MILP objective weights that beat observed decisions on every component.
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Outperformance inverse optimization learns MILP objective weights that beat observed decisions on every component.
A deterministic polynomial-time algorithm achieves optimal O(sqrt d) regret for online inverse linear optimization.
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