Introduction to optimization and operations research

Summary

Introduction to major operations research models and optimization algorithms

Content

Learning Prerequisites

Required courses

Linear algebra

Analysis

Preliminary knowledge of Python is useful, but not required.

Teaching methods

The course combines ex-cathedra lectures (2 hours) with hands-on computer labs (2 hours).

The computer labs are based on Jupyter notebooks using Python, allowing students to experiment with the algorithms covered in class. The core structure of the Python code is provided, and students are tasked with completing specific parts to reinforce their understanding.

To support self-paced learning and avoid bottlenecks due to programming difficulties, fully worked solutions are also made available.

Assessment methods

The evaluation consists of two parts:

• A closed-book exam with multiple-choice questions assessing the theoretical concepts.

• An open-book, computer-based exam conducted in Jupyter notebooks, focused on practical exercises.

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Bierlaire (2015) Optimization: principles and algorithms, EPFL Press

http://optimizationprinciplesalgorithms.com

Ressources en bibliothèque

• Optimization / Bierlaire

Moodle Link

• https://go.epfl.ch/MATH-265

Prerequisite for