Adjustable Robust Optimization Reformulations of Two-Stage Worst-Case Regret Minimization Problems
Bibliographic record
Abstract
Although the stochastic optimization paradigm exploits probability theory to optimize the tradeoff between risk and returns, robust optimization has gained significant popularity by reducing computation requirements through the optimization of the worst-case scenario in a set. An appealing alternative to stochastic and robust optimization consists in optimizing decisions using the notion of regret. Although regret minimization models are generally perceived as leading to less conservative decisions than those produced by robust optimization, their numerical optimization is a real challenge in general. In “Adjustable Robust Optimization Reformulations of Two-Stage Worst-case Regret Minimization Problems,” M. Poursoltani and E. Delage show how to reduce a two-stage worst-case absolute/relative regret minimization problem to a two-stage robust optimization one. This opens the way for taking advantage of recent advanced approximate and exact solution schemes for these hard problems. Their experiments corroborate the high-quality performance of affine decision rules as a popular polynomial-time approximation scheme, from which, under mild conditions, one can even expect exact regret-averse decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".