Hiroshima University Syllabus

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Japanese
Academic Year 2022Year School/Graduate School School of Informatics and Data Science
Lecture Code KA117001 Subject Classification Specialized Education
Subject Name 数値計算
Subject Name
(Katakana)
スウチケイサン
Subject Name in
English
Numerical Computation
Instructor OKAMURA HIROYUKI
Instructor
(Katakana)
オカムラ ヒロユキ
Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term
Days, Periods, and Classrooms (4T) Fri5-8:ENG 218
Lesson Style Lecture Lesson Style
(More Details)
 
Lecture & practice 
Credits 2.0 Class Hours/Week   Language of Instruction B : Japanese/English
Course Level 3 : Undergraduate High-Intermediate
Course Area(Area) 25 : Science and Technology
Course Area(Discipline) 02 : Information Science
Eligible Students Second-year students
Keywords Matrix-vector computation, optimization and simulation 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
Informatics and Data Science Program
(Comprehensive Abilities)
・I2. Ability to provide the most appropriate system solution to a cross-sectional problem in the diversified and complicated information society based on the many forms of cutting edge information technology.
 
Class Objectives
/Class Outline
This lecture provides the fundamental knowledge and algorithms for numerical analysis such as linear equation, eigenvalue problem, non-linear equation, optimization and Monte-Carlo simulation. Also the students practice the implementation of these algorithms through a programming language. 
Class Schedule lesson1: Floating-point expression and numerical errors
lesson2: Floating-point expression and numerical errors
lesson3: Programming, Complexity
lesson4: Programming, Complexity
lesson5: Linear equations
lesson6: Linear equations
lesson7: Eigenvalue and eigenvector problems, Sparse matrix
lesson8: Eigenvalue and eigenvector problems, Sparse matrix
lesson9: Non-linear equations
lesson10: Non-linear equations
lesson11: Non-linear optimization
lesson12: Non-linear optimization
lesson13: Monte-Carlo simulation
lesson14: Monte-Carlo simulation
lesson15: Other topics (numerical integration, etc.)

Reports should be submitted every week. 
Text/Reference
Books,etc.
Basic (minimal necessary) materials are distributed as handouts or electronic files through Bb9. 
PC or AV used in
Class,etc.
 
(More Details) Google Classroom, Google Colaboratory 
Learning techniques to be incorporated  
Suggestions on
Preparation and
Review
Lessons 1, 2:
Understand the expression of integers and real values in computers, and understand numerical errors

Lessons 3, 4:
Learn how to write numerical computation programming, and understand computational complexity.

Lessons 5, 6:
Learn the programs for basic linear computation (vector and matrix), and the algorithms to solve linear equations.

Lessons 7, 8:
Learn the concepts of eigenvalues ​​and eigenvectors in linear algebra, and learn the data structure of sparse matrix.

Lessons 9, 10;
Learn the algorithms to solve non-linear equations.

Lessons 11, 12:
Learn the algorithm to solve non-linear optimization problems.

Lessons 13, 14:
Learn the concept of pseudo-random numbers and Monte-Carlo simulation
 
Requirements  
Grading Method The score will be judged as the learning achievement on the knowledge of numerical computation. A pass grade is 60 points or more. 
Practical Experience  
Summary of Practical Experience and Class Contents based on it  
Message It is encouraged that students actively ask questions to the teacher and assistants. 
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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