SciTech-Mathmatics-Gurobi: ReLU VS MILP(Mixed-Integer Linear Programming) a novel and highly effective method for fitting CPWL(Continuous Piecewise Linear) functions to complex multidimensional data.

Webinar: MILP Formulation for Piecewise Linear Fitting

Neural networks aren't the only option for modeling complex, multidimensional relationships.

Join us on Wednesday, August 20 at 2:00 PM ET for a live webinar,
with Argonne National Laboratory’s Quentin Ploussard,
who will share a rigorous MILP-based approach,
for fitting continuous piecewise linear functions to real-world datasets
—a powerful and interpretable alternative to the ReLU neural network approach.

You'll discover how this method:
Achieves optimal solutions using the Gurobi Optimizer
Requires fewer linear segments for greater efficiency
Enhances model transparency and performance
Applies to real-world challenges in power systems modeling

Date & Time:
Wednesday, August 20, 2025 | 2:00 PM ET

In an era dominated by data-driven insights, accurately modeling complex, non-linear relationships within multidimensional datasets remains a critical challenge. While AI’s ReLU neural networks offer a popular avenue, their “black box” nature and potential for over-parameterization can obscure true underlying dynamics and lead to inefficient models.

This webinar introduces a novel and highly effective Mixed-Integer Linear Programming (MILP) method for fitting Continuous Piecewise Linear (CPWL) functions to multidimensional data. Unlike heuristic approaches, our MILP formulation using the Gurobi solver provides a rigorous, globally optimal solution, guaranteeing a parsimonious representation of non-linearities. The MILP method achieves superior efficiency, yielding significantly fewer linear pieces for a given approximation error, and thereby enhancing model interpretability and computational performance.

This approach has applications in multiple fields, including power system modeling. This MILP-driven CPWL approach can precisely capture complex relationships, such as the interplay between hydropower output, water release, and hydraulic head, offering a more accurate and robust alternative for operational planning and optimization.

Who Should Attend:
Data scientists, optimization professionals, researchers, and engineers interested in advanced modeling techniques for multidimensional data fitting, particularly within energy and infrastructure systems.