Name
Abstract | ||
|---|---|---|
In data-driven inverse optimization an observer aims to learn the preferences of an agent who solves a parametric optimization problem depending on an exogenous signal. Thus, the observer seeks the agent’s objective function that best explains a historical sequence of signals and corresponding optimal actions. We focus here on situations where the observer has imperfect information, that is, where the agent’s true objective function is not contained in the search space of candidate objectives, where the agent suffers from bounded rationality or implementation errors, or where the observed signal-response pairs are corrupted by measurement noise. We formalize this inverse optimization problem as a distributionally robust program minimizing the worst-case risk that the predicted decision (i.e., the decision implied by a particular candidate objective) differs from the agent’s actual response to a random signal. We show that our framework offers rigorous out-of-sample guarantees for different loss functions used to measure prediction errors and that the emerging inverse optimization problems can be exactly reformulated as (or safely approximated by) tractable convex programs when a new suboptimality loss function is used. We show through extensive numerical tests that the proposed distributionally robust approach to inverse optimization attains often better out-of-sample performance than the state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10107-017-1216-6 | Math. Program. |
Keywords | Field | DocType |
C15 Stochastic programming,90C25 Convex programming,90C47 Minimax problems | Continuous optimization,Mathematical optimization,Data-driven,Vector optimization,Multi-objective optimization,Bounded rationality,Observer (quantum physics),Random optimization,Optimization problem,Mathematics | Journal |
Volume | Issue | ISSN |
167 | 1 | 0025-5610 |
Citations | PageRank | References |
4 | 0.43 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peyman Mohajerin Esfahani | 1 | 206 | 20.74 |
| soroosh shafieezadehabadeh | 2 | 4 | 0.43 |
| Hanasusanto, Grani Adiwena | 3 | 86 | 5.71 |
| Daniel Kuhn | 4 | 559 | 32.80 |