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Welcome! |
Welcome to the website of the 2025-2026 optimization course at Mines Nancy.
:::{warning} In construction! These lectures notes are still in construction, and will be updated throughout the first semester of 2025-2026. :::
In short: an hands-on introduction to numerical optimization with Python
:::{hint} Objectives
- Learn the basics of optimization: terminology, zoology of problems, key principles
- Be able to characterize problems and their solutions
- Know standard first and second-order descent algorithms and how-to implement them :::
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36h of classes, including lectures, exercices and lab work (Python).
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Course material in in english. Lectures will be given in french or english, depending on the audience.
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Main results will be on lecture slides and/or on the course website, but detailed computations, proofs and exercises solutions will not, in general. So take notes!
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Bring your computer to class! We'll alternate between exercices on paper and on the computer.
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Lecture slides, exercise sheets and additional documents are all available from the Arche website. (Internal access only).
:::{note} Prerequisites The course requires basic notions of elementary differential calculus and linear algebra. It assumes you have some notions of Python, and some knowledge of the standard libraries (NumPy, matplotlib) :::
This might be subject to slight changes, but for now the course will be graded as follows:
- intermediate exam (about 40%): somewhere in november
- final exam (about 60%): January 13th, 2026 The final exam will cover both theoretical and practical (Python programming) aspects of the course.
Most of the material of this course has been adapted from classical textbooks of the literature on numerical optimization. This course also largely benefited from previous course material kindly shared by Y. Privat at Mines Nancy.
:::{important} Books
- Nocedal, J., & Wright, S. J. (2006). Numerical optimization. New York, NY: Springer New York. (Second Edition)
- Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press
- Beck, A. (2017). First-order methods in optimization. Society for Industrial and Applied Mathematics.
- Bertsekas, D. P. (1999). Nonlinear programming. Athena Scientific. (Second Edition) :::
Many interesting material on numerical optimization can also be found online.
:::{important} Online material
- Convex optimization book's webpage: includes free pdf of the book, lectures slides, exercises, etc.
- MIT OpenCourseWare: several courses on optimization by D. Bertsekas.
- Numerical tours: several implementations and applications in machine learning / signal processing of optimization algorithms, by G. Peyré. :::
For any questions or inquiries, please write me an email at [email protected]
