Inverse Mixed Integer Optimization: An Interior Point Perspective
Bibliographic record
Abstract
We propose a novel solution framework for inverse mixed-integer optimization based on analytic center concepts from interior point methods. We characterize the optimality gap of a given solution, provide structural results, and propose models that can efficiently solve large problems. First, we exploit the property that mixed-integer solutions are primarily interior points that can be modeled as weighted analytic centers with unique weights. We then demonstrate that the optimality of a given solution can be measured relative to an identifiable optimal solution to the linear programming relaxation. We quantify the absolute optimality gap and pose the inverse mixed-integer optimization problem as a bi-level program where the upper-level objective minimizes the norm to a given reference cost, while the lower-level objective minimizes the absolute optimality gap to an optimal linear programming solution. We provide two models that address the discrepancies between the upper and lower-level problems, establish links with noisy and data-driven optimization, and conduct extensive numerical testing. We find that the proposed framework successfully identifies high-quality solutions in rapid computational times. Compared to the state-of-the-art trust region cutting plane method, it achieves optimal cost vectors for 95% and 68% of the instances within optimality gaps of e-2 and e-5, respectively, without sacrificing the relative proximity to the nominal cost vector. To ensure the optimality of the given solution, the proposed approach is complemented by a classical cutting plane method. It is shown to solve instances that the trust region cutting plane method could not successfully solve as well as being in very close proximity to the nominal cost vector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".