2020 Faculty Courses School of Engineering Department of Industrial Engineering and Economics Graduate major in Industrial Engineering and Economics
Numerical Optimization
- Academic unit or major
- Graduate major in Industrial Engineering and Economics
- Instructor(s)
- Shinji Mizuno / Kazuhide Nakata
- Class Format
- Lecture (Zoom)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (Zoom) / 5-6 Fri (Zoom)
- Class
- -
- Course Code
- IEE.A430
- Number of credits
- 200
- Course offered
- 2020
- Offered quarter
- 3Q
- Syllabus updated
- Jul 10, 2025
- Language
- Japanese
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (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
Evaluation methods and criteria
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
No prerequisites