New Benchmark Suite for Linear Ordering Problem Using Up-to-Date Economic Data
A research paper introduces a novel benchmark suite for the Linear Ordering Problem (LOP), derived from current real-world economic data, addressing the limitations of outdated macroeconomic benchmarks. The LOP is a combinatorial optimization problem with applications in economics, social choice, and machine learning, notably used for triangulating input-output tables to identify critical industries. Existing algorithms rely on obsolete data that no longer reflect modern economies. Additionally, LOP instances often have multiple distinct global optima, complicating applications requiring a single solution. The proposed algorithmic scheme leverages state-of-the-art metaheuristics to generate diverse high-quality solutions, along with metrics for assessing solution diversity. The paper is available on arXiv under reference 2605.31051.
Key facts
- Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem.
- LOP is used for triangulation of economic input-output tables.
- Existing algorithms evaluated on outdated macroeconomic data.
- LOP instances often have many distinct global optima.
- New benchmark suite derived from up-to-date real-world economic data.
- Algorithmic scheme uses state-of-the-art LOP metaheuristics.
- Generates diverse sets of high-quality solutions.
- Includes metrics for assessing solution diversity.
Entities
Institutions
- arXiv