For inverse optimization of integer linear programs, the iteration budget for exact consistency via projected subgradient descent is made explicit in terms of sample size, dimension, feature ranges, and the Graver-basis norm of the constraint matrix.
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Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs
For inverse optimization of integer linear programs, the iteration budget for exact consistency via projected subgradient descent is made explicit in terms of sample size, dimension, feature ranges, and the Graver-basis norm of the constraint matrix.