Explicit Iteration Complexity of Exact Data-Driven Inverse Optimization for Integer Linear Programs
A data-driven inverse optimization problem (DDIOP) is the problem of estimating the objective-function parameters (weights) that explain observed optimal-solution data, and it arises in many applications, including integer linear programming (ILP). It is known that, by applying gradient-based optimization methods to the suboptimality loss, the inverse optimization of ILPs can be solved exactly within finitely many oracle iterations, and that the required number of iterations is bounded as $T=O(1/γ(\ell_{\mathrm{sub}})^2)$ in terms of a problem-dependent geometric constant $γ(\ell_{\mathrm{sub}})$. However, no means of bounding $γ(\ell_{\mathrm{sub}})$ from below as a function of the probl...
| Authors | Akira Kitaoka |
|---|---|
| Primary category | math.OC |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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