Speaker

Bio of the Speaker

Masaaki Nagahara received a bachelor's degree in engineering from Kobe University in 1998, and the master's degree and the Doctoral degree in informatics from Kyoto University in 2000 and 2003, respectively.

He is currently a Full Professor at the Institute of Environmental Science and Technology, The University of Kitakyushu. He has been a Visiting Professor with the Indian Institute of Technology (IIT) Bombay since 2017 and IIT Guwahati since 2020. His research interests include control theory, machine learning, and sparse modeling.

He received the Transition to Practice Award in 2012 and George S. Axelby Outstanding Paper Award in 2018 from IEEE Control Systems Society. He also received the Young Authors Award in 1999, Best Paper Award in 2012, and Best Book Authors Award in 2016, and Kimura Award in 2020 from SICE, and Best Tutorial Paper Award in 2014 from IEICE Communications Society. He is a senior member of IEEE.

About the course

The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems and control to design resource-aware control systems. This course gives a comprehensive guide to sparsity methods for systems and control.

This course has two parts: Part I for introduction to compressed sensing (sparsity methods for finite-dimensional vector spaces), and Part II for sparsity methods applied to optimal control in infinite-dimensional function spaces.

The primary objective of this course is to show how to use sparsity methods for several engineering problems. For this, the lecture will provide MATLAB programs by which you can try sparsity methods for themselves. You will obtain a deep understanding of sparsity methods by running these MATLAB programs.

This course is suitable for graduate students, though it should also be comprehendible to undergraduate students who have a basic knowledge of linear algebra and elementary calculus. Also, especially part II of the lecture should appeal to professional researchers and engineers who are interested in applying sparsity methods to systems and control.

Schedule

Part I: Introduction to Compressed Sensing

1. Historical Review of Compressed Sensing (12/May/2021, 2:00 PM - 3:30 PM)

2. What is Sparsity? (13/May/2021, 2:00 PM - 3:30 PM)

3. Curve Fitting and Sparse Optimization (14/May/2021, 2:00 PM - 3:30 PM)

4. Algorithms for Convex Optimization (19/May/2021, 2:00 PM - 3:30 PM)

5. Greedy Algorithms (20/May/2021, 2:00 PM - 3:30 PM)

6. Applications of Sparse Representation (21/May/2021, 2:00 PM - 3:30 PM)

Part II: Introduction to Sparse Control

7. Dynamical Systems and Optimal Control (26/May/2021, 2:00 PM - 3:30 PM)

8. Maximum Hands-off Control (27/May/2021, 2:00 PM - 3:30 PM)

9. Numerical Optimization by Time Discretization (28/May/2021, 2:00 PM - 3:30 PM)

Resources

Textbook: M. Nagahara, Sparsity Methods for Systems and Control, Now Publishers, 2020. Here's the free PDF of the book.