| Academic Year |
2024Year |
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
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Comprehensive Abilities）
・D2. Ability to derive optimal system solutions based on abundant cutting-edge information technologies for cross-sectoral issues in a diversified and complicated information society.
Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Intelligence Science Program
（Comprehensive Abilities）
・D3. Ability to grasp complexly intertwined social needs and issues from a bird's-eye view and solve issues with a multifaceted perspective and analytical ability based on a wide range of knowledge in intelligent science. |
|---|
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. Final examination will be conducted. |
Text/Reference
Books,etc. |
Basic (minimal necessary) materials are distributed as handouts or electronic files through LMS. |
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. |