Applied Optimization

This teaching unit will be held online.

Nowadays, numerical optimization is a fundamental component of many applications, e.g. in engineering, finances, biomedical applications, machine learning and many more. Therefore, understanding the underlying principles and available algorithms of numerical optimization can be considered an essential skill for a computer scientist. This course offers an applied introduction, covering a broad range of practically important topics, as for instance: Mathematical modeling of real-world problems, theory of convexity, Lagrange dualism, algorithms for unconstrained and constrained optimization with inequalities (e.g. gradient descent, Newton’s method, trust-region methods, active set approaches, interior point methods, …). A major goal of the course is to train students in appropriately modelling optimization problems, and identifying suitable optimization algorithms, based on the understanding of their specific strengths and weaknesses.

Details

Code 31099
61099
Type Course
ECTS 5
Site Bern
Track(s) T3 – Advanced Information Processing
T6 – Data Science
Semester A2020

Teaching

Learning Outcomes

Upon successful completion of this class, a student will be able to:

  • Understand which classes of optimization problems are easy/hard to solve.
  • Model or re-formulate problems in a way that they become easier (e.g. convex).
  • Understand the fundamental ideas behind unconstrained, constrained and mixed-integer optimization.
  • Implement and use various optimization algorithms (programming exercises are in C++).
  • Understand and tune the parameters and output statistics that are exposed by optimization packages.
Lecturer(s) David Bommes
Language english
Course Page

The course page in ILIAS can be found at https://ilias.unibe.ch/goto_ilias3_unibe_crs_1841335.html.

Schedules and Rooms

Period Weekly
Schedule Thursday, 09:15 - 12:00
Location online

Additional information

Comment

First Lecture
The first lecture will take place online on Thursday, 17.09.2020 at 09:15.

Literature

  • S. Boyd, L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004
  • J. Nocedal, S.J. Wright, Numerical Optimization, Springer, 2006