Dear Organizers, many apologies for the late submission… I submit this through the Form, I thought this was enough. Best regards.
Contribution type: lightning talk + poster
CVXlab: a reproducible workflow for data-driven convex optimisation models
Presenter: Matteo V. Rocco
Description:
CVXlab is an open-source Python framework for building, running, maintaining integrated convex optimisation models. It separates model logic, input data and scenario definitions, combining tabular data management, symbolic expression parsing and CVXPY-based solution. The presentation will show how CVXlab supports a transparent workflow from model specification and data input to scenario execution and reproducible results, using a compact optimisation example.
Background:
Optimisation models used in research often combine mathematical formulation, data processing, solver configuration and scenario management in project-specific code. This can make models difficult to inspect, reuse and reproduce. CVXlab provides a structured workflow based on model settings, data tables and a common optimisation interface. It supports independent and integrated problem solving, Excel or CSV input data, YAML-based settings, and user-defined symbolic extensions. The lightning talk will introduce the rationale and workflow; the poster will provide a more detailed view of the software architecture and an example application.
Optional links:
- Documentation: https://cvxlab.readthedocs.io/en/latest/
This version is consistent with the documented CVXPY integration, data management, Excel/CSV inputs, YAML settings, symbolic expressions and integrated solving. CVXlab documentation (https://cvxlab.readthedocs.io/en/dev/changelog.html)