Problem
Research questions and friction points this paper is trying to address.
Scenario Approach
Data-Driven Optimization
Convex Programming
Software Toolbox
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
🤖 AI Summary
This study addresses the absence of user-friendly tools supporting scenario-based methods in data-driven convex optimization. This work proposes the first end-to-end, open-source Python toolbox that integrates scenario theory with modern web technologies. Utilizing a Python backend and a JavaScript frontend, the framework supports linear, quadratic, and semidefinite programming alongside multi-format data parsing. It deeply integrates convex programming with empirical data samples while providing rigorous statistical performance guarantees. Benchmark evaluations demonstrate that the toolbox enables efficient cross-device interaction and reliable solution computation. By bridging the gap between theoretical methodologies and engineering practice, this contribution offers a practical, scalable infrastructure for implementing scenario-based approaches to data-driven convex optimization.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Scenario Approach
Data-Driven Convex Programming
Open-Source Toolbox
Web-based GUI
Statistical Guarantees
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