Explicit Iteration Complexity for Data-Driven Inverse Optimization of Integer Linear Programs
A recent study published on arXiv (2607.22263) explores the explicit iteration complexity involved in exact data-driven inverse optimization for integer linear programs (ILPs). The data-driven inverse optimization problem (DDIOP) aims to determine the parameters of the objective function that account for the observed optimal solutions. Although gradient-based techniques can precisely resolve ILP inverse optimization in a finite number of oracle iterations, the previous iteration limit relied on an unspecified geometric constant γ(ℓ_sub). This research presents the first clear limit on the iteration count based on the size of the problem for ILPs, thus offering practical guarantees on complexity.
Key facts
- Paper ID: arXiv:2607.22263
- Announce Type: cross
- Focuses on data-driven inverse optimization for integer linear programs
- Provides explicit iteration complexity bound
- Previous bounds depended on unknown geometric constant γ(ℓ_sub)
- Uses gradient-based optimization methods on suboptimality loss
- Enables exact solution within finitely many oracle iterations
- First explicit function of problem size for ILPs
Entities
Institutions
- arXiv