Scenario optimization toolbox simplifies data-driven convex programming
Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
Mathematical SoftwareMachine Learning
Summary
Optimization problems often rely on data to make decisions, but it can be hard to know if a solution will work well for new data. The authors present Scen-Opt, a software tool that helps users solve data-driven optimization problems while giving statistical guarantees about how reliable the solutions are. It supports different types of optimization problems and offers an easy-to-use web interface. This software makes it simpler for practitioners to apply advanced scenario theory ideas without needing deep expertise.
What this means in practice
- •For data scientists: Solve convex optimization problems using real-world data with reliable guarantees directly from a user-friendly interface.
- •For energy grid operators: Optimize energy distribution models with data-driven constraints ensuring performance reliability under varying conditions.
Authors
Ben Wooding, Simone Garatti, Marco C. Campi, Abolfazl Lavaei
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.