A latent-variable hypothesis class lets inverse optimization recover feasible regions for known linear objectives, with convex and MILP training reformulations in special cases.
Convex optimization algorithms
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Inverse Optimization via Learning Feasible Regions
A latent-variable hypothesis class lets inverse optimization recover feasible regions for known linear objectives, with convex and MILP training reformulations in special cases.