Hiroshima University Syllabus |

Japanese

Academic Year 2022Year School/Graduate School School of Informatics and Data Science Lecture Code KA118001 Subject Classification Specialized Education Subject Name 数理計画法 Subject Name

（Katakana）スウリケイカクホウ Subject Name in

EnglishMathematical Programming Instructor NISHIZAKI ICHIROU Instructor

(Katakana)ニシザキ イチロウ Campus Higashi-Hiroshima Semester/Term 2nd-Year, First Semester, 1Term Days, Periods, and Classrooms (1T) Weds3-4,Fri1-2：ENG 220 Lesson Style Lecture Lesson Style

(More Details)Lecture

There is a possibility that lectures are conducted face-to-face.Credits 2.0 Class Hours/Week Language of Instruction J : Japanese Course Level 2 : Undergraduate Low-Intermediate Course Area（Area） 25 : Science and Technology Course Area（Discipline） 11 : Electrical, Systems, and Control Engineering Eligible Students Students enrolled in and before 2020 Keywords Linear programming, simplex method, two-phase simplex method, dual simplex method, integer programming, branch and bound method, nonlinear programming, Kuhn-Tucker conditions, Lagrangian function, descent method Special Subject for Teacher Education Special Subject Class Status

within Educational

ProgramCriterion referenced

EvaluationInformatics and Data Science Program

（Comprehensive Abilities）

・I2. Ability to provide the most appropriate system solution to a cross-sectional problem in the diversified and complicated information society based on the many forms of cutting edge information technology.

Class Objectives

/Class OutlineExplanation and practice of mathematical programming theory which is one of most basic systems optimization methods Class Schedule lesson1 Linear programming: Summary of mathematical programming

lesson2 Linear programming: Algebraic computations and definitions of linear programming problems

lesson3 Linear programming: Theory and algorithm of simplex method

lesson4 Linear programming: Theory and algorithm of two-phase method

lesson5 Linear programming: Theory and algorithm of dual simplex method

lesson6 Integer programming: Modelling based on integer programming probmes

lesson7 Integer programming: Basic framework of integer programming

lesson8 Integer programming: Theory and algorithm of branch and bound method

lesson9 Practices of linear and integer programming

lesson10 Intermediate exam (linear and integer programming)

lesson11 Nonlinear programming: Nonlinear programming problems and their conceptual foundations

lesson12 Nonlinear programming: Optimality condition for constrained and non-constrained optimization problems

lesson13 Nonlinear programming: Algorithm for solving non-constrained optimization problems

lesson14 Nonlinear programming: Algorithm for solving nonlinear programming problems

lesson15 Practices of nonlinear programming

Intermediate and final examinations, assignmentsText/Reference

Books,etc.Textbook: Masatoshi Sakawa and Ichiro Nishizaki, ``Introduction to Mathematical Programming'', Morikita Publishing Co., Ltd. (in Japanese) PC or AV used in

Class,etc.(More Details) Textbook, PC, projector Learning techniques to be incorporated Suggestions on

Preparation and

Review1. Understand examples of mathematical programming problems in the real world

2. Learn basic concepts and terms about linear programming problems

3. Understand assumptions, the principle and the algorithm of simplex method by applying it to examples

4. Understand the principle and the algorithm of two-phase simplex method by applying it to examples

5. Understand the principle and the algorithm of dual simplex method by applying it to examples

6. Understand the formulation process of actual optimization problems as integer programming problems

7. Learn basic concepts and terms about integer programming problems

8. Understand the principle and the algorithm of branch and bound method by applying it to examples

9. Master the optimization methods for linear and integer programming problems by exercises

10. Understand the optimization methods for linear and integer programming problems

11. Understand the formulation process of actual optimization problems as nonlinear programming problems

12. Understand Kuhn-Tucker conditions and Lagrangian function

13. Understand the principle and the algorithm of descent method and Newton method by applying them to examples

14. Understand the principle and the algorithm of penalty method and generalized reduced gradient method by applying them to examples

15. Master the optimization methods for nonlinear programming problems by exercisesRequirements 1. Watch all videos and submit assignments. (Attendance at all classes is necessary as a rule.)

2. An elementary knowledge of mathematics is necessary.Grading Method Intermediate and final examinations, and assignments, the passing mark is 60. 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.