LumiX: A type-safe, data-centric python library for modern mathematical optimization
SoftwareX, cilt.33, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 33
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.softx.2026.102533
- Dergi Adı: SoftwareX
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
- Anahtar Kelimeler: Mathematical optimization, Operational research, Python, Sensitivity analysis
- Karadeniz Teknik Üniversitesi Adresli: Evet
Özet
This paper presents LumiX, an open-source Python library for mathematical optimization designed for data-intensive applications. LumiX employs a data-centric, type-safe modeling paradigm in which problem data, scenario parameters, and optimization models are managed within a unified framework. Key features include Object-Relational Mapping (ORM) integration for automatic variable generation, a solver-agnostic API supporting OR-Tools, Gurobi, CPLEX, and GLPK, automatic linearization of common non-linear expressions, native goal programming, and integrated analysis tools for sensitivity, scenario, and what-if analyses. We present a multi-stage timetabling case study and a quantitative benchmark comparing LumiX against Pyomo and PuLP. The evaluation demonstrates LumiX’s position as a framework for researchers and practitioners developing data-driven optimization solutions, addressing the gap between lightweight procedural libraries and traditional Algebraic Modeling Languages (AMLs). Current limitations, including Big-M parameter sensitivity and McCormick relaxation tightness, are discussed.