The cross-entropy method for optimization in computational science and engineering

At LMSSC, Paris, February 17th 2025, 1.30 p.m.

Americo Barbosa da Cunha Junior
Associate Professor, Institute of Mathematics and Statistics (IME), Rio de Janeiro State University (UERJ), Brazil

The Cross-Entropy (CE) method, a robust stochastic optimization technique, has garnered attention for its efficacy in addressing complex optimization problems across various disciplines. Originating from the field of rare event simulation, the CE method has evolved into a versatile tool for combinatorial optimization, continuous optimization, and machine learning tasks. Its core strategy involves generating sample solutions and iteratively refining probability distributions to hone in on the optimal regions of the solution space.

This seminar introduces the CE method and its practical implementation through the CEopt code (www.ceopt.org), a MATLAB-based framework designed to simplify the application of CE techniques. The CEopt code encapsulates the method's adaptability and effectiveness, featuring support for both constrained and unconstrained optimization problems. Its modular architecture, equipped with input validation, adaptive sampling mechanisms, and dynamic parameter adjustment, enables users to tackle a wide array of optimization challenges without deep dives into algorithmic intricacies.

Participants from machine learning and computational mechanics backgrounds will find particular interest in how the CEopt code can integrate into their workflows to optimize performance metrics and system designs. This seminar will cover the theoretical foundations, practical considerations, and potential applications of the CE method, demonstrating its utility with real-world examples and discussing future directions in optimization technology.