WSN30801 Special Lecture on Informatics and Data Science H

Hiroshima University Syllabus

Japanese

Academic Year 2025Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program

Lecture Code WSN30801 Subject Classification Specialized Education

Subject Name 情報科学特別講義H

Subject Name
（Katakana）ジョウホウカガクトクベツコウギエイチ

Subject Name in
English Special Lecture on Informatics and Data Science H

Instructor See the class timetable.

Instructor
(Katakana) ジュギョウジカンワリヲサンショウ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, Intensive

Days, Periods, and Classrooms (Int) Inte

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 1.0 Class Hours/Week 　 Language of Instruction E : English

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students Master course and Doctor course students

Keywords Large-scale optimization, Distributed optimization, Convex optimization, Graph theory, Consensus algorithms

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline The course provides a comprehensive overview of large-scale and distributed optimization. Topics include convex optimization, graph theory, multi-agent systems and distributed algorithms, with an emphasis on consensus and resilient methods through Python simulations. The objective is to equip students with rigorous theoretical and practical tools to address complex challenges in cyber-physical systems.

Class Schedule 1. Introduction to Optimization
2. Convex Optimization
3. Networks and Graph Theory
4. Multi-agent Dynamical Systems
5. Introduction to Distributed Optimization
6. Distributed Algorithms for Static Graphs
7. Distributed Algorithms for Time-varying Graphs
8. Resilient Consensus and Optimization

Text/Reference
Books,etc. Slides will be provided.

PC or AV used in
Class,etc.

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review Students should thoroughly review the materials for the upcoming lecture, as each session forms the foundation for the next. Pre-learning and post-learning activities are strongly recommended to ensure a cumulative understanding of the course content.

Requirements

Grading Method Evaluation will be based on report assignments and class participation.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2025Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program
Lecture Code	WSN30801	Subject Classification	Specialized Education
Subject Name	情報科学特別講義H
Subject Name （Katakana）	ジョウホウカガクトクベツコウギエイチ
Subject Name in English	Special Lecture on Informatics and Data Science H
Instructor	See the class timetable.
Instructor (Katakana)	ジュギョウジカンワリヲサンショウ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	1.0	Class Hours/Week		Language of Instruction	E : English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students	Master course and Doctor course students
Keywords	Large-scale optimization, Distributed optimization, Convex optimization, Graph theory, Consensus algorithms
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	The course provides a comprehensive overview of large-scale and distributed optimization. Topics include convex optimization, graph theory, multi-agent systems and distributed algorithms, with an emphasis on consensus and resilient methods through Python simulations. The objective is to equip students with rigorous theoretical and practical tools to address complex challenges in cyber-physical systems.
Class Schedule	1. Introduction to Optimization 2. Convex Optimization 3. Networks and Graph Theory 4. Multi-agent Dynamical Systems 5. Introduction to Distributed Optimization 6. Distributed Algorithms for Static Graphs 7. Distributed Algorithms for Time-varying Graphs 8. Resilient Consensus and Optimization
Text/Reference Books,etc.	Slides will be provided.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Students should thoroughly review the materials for the upcoming lecture, as each session forms the foundation for the next. Pre-learning and post-learning activities are strongly recommended to ensure a cumulative understanding of the course content.
Requirements
Grading Method	Evaluation will be based on report assignments and class participation.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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