.png){kind=icon}
.png){kind=icon}
Return
On Contextual Inverse Multiobjective Problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2025.12.007.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Inverse optimization framework with multiple criteria shaped by context features. • Decisions modeled via ideal point scalarization using norm-based distance metrics. • Characterizes inverse feasibility conditions using tools from convex analysis. • Structured sparsity constraints enhance interpretability of the inferred problem. • Validated on financial data, showing strong generalization and sparse recovery.
Keywords:
Multiple objective programming
Inverse optimization
Contextual optimization
Interpretable decision-making
Ideal point problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W