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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
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- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Mizuno Shinji Nakata Kazuhide
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue5-6() Fri5-6()
- Group
- -
- Course number
- IEE.A430
- Credits
- 2
- Academic year
- 2021
- Offered quarter
- 3Q
- Syllabus updated
- 2021/4/9
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
This course treats interior point methods for solving linear programming. Especially, students acquire with mathematical theory, optimal condition, polynomial convergence, and computational efficiency of interior point methods.
In addition, this course teats techniques to mining useful knowledge from Japanese documents. Especially, students study various methods of separating words and word embedding.
Student learning outcomes
By the end of this course, students will be able to:
1. Understand the theoretical properties of interior-point methods for linear programming problems and can apply them to real problems.
2. Understand the theoretical properties of separation words and word embedding for Japanese documents and can apply them to real problems.
Keywords
Interior-point method, Linear programming, Text mining
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Attendance is taken in every class.
Students are required to read the text before coming to class.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Linear programming | We instruct in each class |
| Class 2 | Primal interior-point method (affine scaling algorithm) | |
| Class 3 | Primal interior-point method (Karmarkar's algorithm) | |
| Class 4 | Analytic center and center path | |
| Class 5 | Primal-dual interior-point method (affine scaling algorithm) | |
| Class 6 | Primal-dual interior-point method (path following mathed) | |
| Class 7 | Infeasible interior-point method | |
| Class 8 | Japanese documents | |
| Class 9 | Separating words | |
| Class 10 | Implementation preparation | |
| Class 11 | Implementation of Separating words | |
| Class 12 | Word embedding | |
| Class 13 | Word2Vec | |
| Class 14 | Implementation of Word2Vec |
Out-of-Class Study Time (Preparation and Review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required
Reference books, course materials, etc.
Course materials can be found on OCW-i
Assessment criteria and methods
Students will be assessed on their understanding of interior point method, and their ability to apply them to solve problems.
Students' course scores are based on reports (50%) and mini exams (50%).
Related courses
- IEE.A206 ： Operations Research
- IEE.A330 ： Advanced Operations Research
- IEE.A331 ： OR and Modeling
Prerequisites (i.e., required knowledge, skills, courses, etc.)
No prerequisites
