Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex Programming
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.
ProofPaper ↗
Key points
- 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.
- 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.
- 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.
Sources (1)
- [1]Scen-Opt: A Scenario Optimization Toolbox for Data-Driven Convex ProgrammingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:51 AM
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.
The scenario approach is a well-established statistical framework for data-driven decision-making.
Extractive summary: sentences quoted from the sources.