| Academic Year |
2020Year |
School/Graduate School |
School of Engineering |
| Lecture Code |
K5130010 |
Subject Classification |
Specialized Education |
| Subject Name |
数理最適化 |
Subject Name
（Katakana） |
スウリサイテキカ |
Subject Name in
English |
Mathematical Optimization |
| Instructor |
KITAMURA MITSURU |
Instructor
(Katakana) |
キタムラ ミツル |
| Campus |
Higashi-Hiroshima |
Semester/Term |
2nd-Year, Second Semester, 4Term |
| Days, Periods, and Classrooms |
(4T) Mon3-4,Weds1-2：ENG 106 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
|
| Lecture by Web |
| Credits |
2.0 |
Class Hours/Week |
|
Language on Instruction |
B
:
Japanese／English |
| Course Level |
3
:
Undergraduate High-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
10
:
Integrated Engineering |
| Eligible Students |
2nd year students |
| Keywords |
Optimization |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program | |
|---|
Criterion referenced
Evaluation | Program of Mechanical Systems Engineering
（Abilities and Skills）
・Acquring basis of mechanical system engineering steadily and developing the applied skill.
Program of Transportation Systems
（Knowledge and Understanding）
・The area of systems: Technical knowledge on systems, information and transportation systems relating to transportation equipment and coexistence with the environment
（Abilities and Skills）
・The area of systems: The ability to apply technical knowledge of systems, information and transportation systems to solve issues relating to the areas of transportation equipment and coexistence with the environment |
|---|
Class Objectives
/Class Outline |
Optimization |
| Class Schedule |
lesson 1 Guidance and Linear Programing
lesson 2 Slack variables and Simplex method
lesson 3 Dual problem
lesson 4 Multi objective problem
lesson 5 Nonlinear optimization problem, Taylor expansion equation and Numerical derivatives
lesson 6 One variable search and Steepest descent method
lesson 7 Newton method
lesson 8 Quasi-Newton method
lesson 9 Quasi-Newton method, Conjugate gradient method
lesson 10 Conjugate gradient method (step version), Successive linear programming (SLP)
lesson 11 Penalty function method, SUMT method
lesson 12 Lagrange multiplier method
lesson 13 Inequality constraint problem
lesson 14 Krush-Kuhn-Tucker condition (KKT condition)
lesson15 Genetic Algorithm
Finial examination are given |
Text/Reference
Books,etc. |
Text |
PC or AV used in
Class,etc. |
|
| (More Details) |
Textbook and PC.
Sometime Excel is used. |
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Study hard |
| Requirements |
|
| Grading Method |
Examinations and homework |
| 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. |