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
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中文总结 AI 辅助
本文提出Scen-Opt，一个基于场景理论的开源Python工具箱，支持数据驱动凸规划并提供统计保证，通过Web界面和多种数据格式实现用户友好的优化求解。
中文摘要 AI 辅助
场景方法是一个成熟的数据驱动决策统计框架。特别是在数据驱动优化中，场景方法揭示了问题结构如何支配样本外泛化，并为评估和认证最优解在约束满足方面的可靠性提供了原则性基础。尽管其理论发展强劲且适用广泛，但迄今为止尚无软件工具箱能够在场景方法框架内实现用户友好的数据驱动凸优化。在本文中，我们介绍了Scen-Opt，一个开源软件工具，它将凸规划与数据样本相结合，同时提供基于场景理论的统计保证。Scen-Opt使用Python实现，支持数据驱动的线性、二次和半定规划，并提供基于Python的Web应用程序，该应用程序使用现代Web技术构建了直观且响应式的图形用户界面（GUI）。Scen-Opt可通过其在线界面直接使用，也可本地安装，支持手动输入和数据文件上传（CSV、JSON、TXT、TSV、MAT、Excel、NPY、NPZ、Parquet）。Scen-Opt基于Python后端和现代JavaScript前端构建，在台式机、笔记本电脑、平板电脑和移动设备上提供高度用户友好的体验和高效可用性。在本文中，Scen-Opt应用于一组代表性基准测试，展示了其在保证性能的数据驱动凸优化方面的实际有效性。
英文摘要
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.
发表机构
- Vanderbilt University（范德堡大学）
- Politecnico di Milano（米兰理工大学）
- University of Brescia（布雷西亚大学）
- Newcastle University（纽卡斯尔大学）
机构由 AI 辅助整理，请以论文原文为准。
