LumiX: A type-safe, data-centric python library for modern mathematical optimization

IF 1.9 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2026-02-01 Epub Date: 2026-01-23 DOI:10.1016/j.softx.2026.102533

Beyzanur Siyah , Tolga Berber

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引用次数: 0

Abstract

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.

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LumiX：用于现代数学优化的类型安全、以数据为中心的python库

本文介绍了LumiX，一个为数据密集型应用程序设计的数学优化的开源Python库。LumiX采用以数据为中心、类型安全的建模范式，在该范式中，问题数据、场景参数和优化模型在统一框架中进行管理。关键特性包括用于自动变量生成的对象关系映射（ORM）集成，支持OR-Tools、Gurobi、CPLEX和GLPK的求解器不确定API，常见非线性表达式的自动线性化，本机目标规划，以及用于灵敏度、场景和假设分析的集成分析工具。我们提出了一个多阶段的时间表案例研究和定量基准比较LumiX与Pyomo和PuLP。该评估证明了LumiX作为研究人员和从业者开发数据驱动优化解决方案的框架的地位，解决了轻量级过程库与传统代数建模语言（AMLs）之间的差距。讨论了当前的限制，包括大m参数灵敏度和麦考密克弛豫紧度。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.