arXiv：cs.LG· Ben Wooding, Simone Garatti, Marco C. Campi, Abolfazl Lavaei·· 7 小时前AI 评分30
Scen-Opt：面向数据驱动凸规划的场景优化工具箱
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
AI 导读
研究者开源了 Python 工具箱 Scen-Opt，首次将凸规划与数据样本结合，并基于场景理论提供统计保证。该工具支持数据驱动的线性、二次和半定规划，提供网页 GUI，可通过在线界面使用或本地安装，支持 CSV、JSON、MAT、Excel、Parquet 等文件上传。论文以代表性基准测试验证了其在数据驱动凸优化中的实际效果。
正文
Abstract:The scenario approach is a well-established statistical framework for data-driven decision-making. In particular, in data-driven optimization, the scenario approach unveils how the problem structure governs out-of-sample generalization, and offers a principled basis for assessing and certifying the reliability of the optimal solution as per constraint satisfaction. Despite its strong theoretical development and wide applicability, no software toolbox has been available to date that enables user-friendly, data-driven convex optimization within the scenario-approach framework. In this paper, we introduce Scen-Opt, an open-source software tool that integrates convex programming with data samples while providing statistical guarantees grounded in scenario theory. Scen-Opt is implemented in Python, supporting data-driven linear, quadratic, and semidefinite programming, and offers a Python-based web application with an intuitive and reactive graphical user interface (GUI) built using modern web technologies. Scen-Opt can be used directly through its online interface or installed locally, accommodating both manual input and data-file uploads (CSV, JSON, TXT, TSV, MAT, Excel, NPY, NPZ, Parquet). Built on a Python backend with a modern JavaScript frontend, Scen-Opt offers a highly user-friendly experience and efficient usability across desktops, laptops, tablets, and mobile devices. In this paper, Scen-Opt is applied to a set of representative benchmarks, demonstrating its practical effectiveness for data-driven convex optimization with guaranteed performance.
| Comments: | 49 pages. Software archived at this https URL (v1.0); source code at this https URL web app at this https URL |
| Subjects: | Mathematical Software (cs.MS); Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC) |
| MSC classes: | 90C15, 90C25, 90C90 |
| Cite as: | arXiv:2610.07846 [cs.MS] |
| (or arXiv:2610.07846v1 [cs.MS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07846 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ben Wooding [view email]
[v1]
Tue, 6 Oct 2026 06:51:26 UTC (4,237 KB)
来源：arXiv：cs.LG · arxiv.org