Work

On contextual inverse multiobjective problems

Year: 2025

Type: article

Abstract: We introduce a novel framework for Contextual Inverse Multiobjective Optimization, where the goal is to recover multiple objective functions and the preferences among them from observed context-decision pairs. Assuming that each decision arises from minimizing a weighted distance to an ideal outcome, we develop a mathematical programming model that infers context-dependent linear objectives consistent with observed data. Our approach accommodates different norm-based scalarizations, including ℓ 1 , ℓ 2 , ℓ ∞ and Ordered Weighted Averages, and enforces interpretability through structured sparsity. By characterizing the conditions under which observed decisions are provably optimal, we formulate a tractable Mixed-Integer Quadratic Program that reveals how contextual features influence decision-making criteria. Numerical experiments on real-world data demonstrate the method’s ability to recover interpretable, sparse models while maintaining strong generalization performance.

Source: European Journal of Operational Research

DOI: 10.1016/j.ejor.2025.12.007

Authors: Rafael Blanquero, Emilio Carrizosa, Nuria Gómez-Vargas

Institution: Universidad de Sevilla

Language: English

Topic: Advanced Multi-Objective Optimization Algorithms

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funders: Ministerio de Ciencia, Innovación y Universidades, Consejería de Transformación Económica, Industria, Conocimiento y Universidades, HORIZON EUROPE European Innovation Council