Modern Nonlinear Optimization, 8.0 credits
Modern Nonlinear Optimization, 8.0 hp
6FMAI34
Course level
Third-cycle EducationDescription
Contact the examiner if interested.
Current and recently held PhD courses at the Department of Mathematics can be found here: https://liu.se/artikel/doktorandkurser-vid-matematiska-institutionen
Contact
-
Chuan He
Examiner
Entry requirements
Calculus, Linear Algebra, Introduction to Optimization, Matlab/Python.
Learning outcomes
- Learn optimization theory and apply theory to optimization problems in practice.
- Learn advanced optimization algorithms that can be used to improve performance on challenging problems.
- Learn implementation in software and use built-in optimization tools to tackle practical problems.
Contents
Tentative Contents:
- Optimization Models: Theory and Applications
- Optimization Methods: Proximal Gradient Methods, Accelerated Methods, Stochastic Methods, Constrainted Optimization Methods
- Generalized Problem Formulations: Variational Inequality, Monotone Inclusion
Educational methods
Lectures by the instructor, Homework presentation by students.
Examination
Homework Assignment, Course Project.
Grading
Two-grade scaleCourse literature
- First-Order Methods in Optimization
Book by Amir Beck - *Optimization Methods for Large-Scale Machine Learning
*SIAM Review by Léon Bottou, Frank E. Curtis, and Jorge Nocedal - Nonlinear Optimization
Book by Andrzej Ruszczynski - *Frist-Order and Stochastic Optimization Methods for Machine Learning
*Book by Guanghui Lan