Fundamental techniques covered in this course include linear, discrete and nonlinear optimization. The underlying theory is motivated through concrete examples across several application areas such as project management, portfolio selection, production planning, revenue management, transportation, etc. We will use Python to model and solve practical decision problems.

The following topics will tentatively be covered in the course:

Part I: Linear Optimization

• Applications
• The Simplex Method
• Duality
• Large-Scale Optimization

Part II: Discrete Optimization

• Applications
• Branch & Bound and Cutting Planes
• Lagrangian Methods (if time)

Part III: Nonlinear Optimization

• Applications
• Optimality Conditions
• Local Optimization

Linear optimization, discrete optimization, nonlinear optimization

A good background in linear algebra and calculus is required. Basic knowledge of probability theory is useful but not necessary.

• Recognize the power of using optimization methods and models in their careers
• Compare and appraise the basic theories that underlie current thinking in optimization
• Use these theories to structure practical decision-making situations
• Apply the fundamental quantitative methods and tools used in operations research
• Formulate managerial decision problems as optimization models
• Solve linear, nonlinear and discrete optimization models using Python
• Model uncertainty in linear optimization using techniques from stochastic programming

• Communicate effectively with professionals from other disciplines.
• Use both general and domain specific IT resources and tools
• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Write a scientific or technical report.

Classical formal teaching interlaced with practical exercices

• 70% final exam
• 30% group project

1. Dimitris Bertsimas and John Tsitsiklis, Introduction to Linear Optimization, Dynamic Ideas & Athena Scientific, 2008.
2. Dimitri P. Bertsekas, Nonlinear Programming, Athena Scientific, 2016. 

• Find the references at the Libraryhttps://slsp-epfl.primo.exlibrisgroup.com/discovery/search?query=any,contains,%22MGT-483%202025-2026%22,AND&tab=41SLSP_EPF_MyInst_and_CI&search_scope=MyInst_and_CI&sortby=rank&vid=41SLSP_EPF:prod&mode=advanced&offset=0

• https://go.epfl.ch/MGT-483https://go.epfl.ch/MGT-483