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. |