Course title
M02300003
Fundamentals of Engineering Optimization

 BUI NGOC TAM
Middle-level Diploma Policy (mDP)
Program / Major mDP Goals
先進国際課程 A-1 A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
(改組前)先進国際課程 A-1 A-1 Students shall obtain basic and advanced knowledge and skills in mathematics, natural and computer sciences as well as presentation skills to communicate on their knowledge with scholars from various fields.
先進国際課程 A-2 A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
(改組前)先進国際課程 A-2 A-2 To suitably lead an international team in the future, students will be able to consider and make decisions on issues in various kinds of problems by grasping what kind of problems are tackled to solve in what way in a wide range of fields in science and technology.
Purpose of class
This course aims to provide fundamental knowledge of engineering optimization theory that can be applied to relevant real design problems.
Course description
The course deals with the fundamentals of engineering optimization and algorithms including Linear Optimization (linear programming and mathematical modeling; Simplex method, Convex Optimization), Non-linear Optimization (Lagrange multipliers; Kuhn-Tucker conditions, Steepest descent, quasi-Newton methods, Quadratic programming, None-Convex Optimization), Advanced Optimization (heuristic, metaheuristic such as Genetic Algorithms (GA); Simulated Annealing (SA); Particle Swarm Optimization (PSO). Furthermore, the course deals with how these skills are used in two or three selected applications, such as geometric measurement problems and design optimization. Skills training and increased understanding are acquired through computer programs on MATLAB tools for solving optimization problems.
Goals and objectives
  1. Students can understand the main optimization concepts and the benefits of using optimization in engineering design.
  2. Students can learn various aspects of Optimization Algorithms.
  3. Students can apply optimization methods to solve various problems in the field of engineering
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Mid-term exam Final Exam Total.
1. 10% 24% 34%
2. 10% 24% 34%
3. 10% 22% 32%
Total. 30% 70% -
Evaluation method and criteria
Mid-term exam (30%) and Final Exam (70%) are the criteria for the grade. More than 60% of the total score is needed for the course credit.
＜Note＞
Students are marked absent from the class if they are late regardless of the delay time.
If students are absent from more than one-third of the total number of classes, the credit for this course cannot be given to them.
Even though students are absent from the class, whatever the reason, e.g., sickness, delay of public transportation systems, or forgetting to bring the student ID card, it is counted as an absence.
Language
English
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. A Brief History of Optimization, Definition of Engineering Optimization Read chapters 1 and 2 in the textbook 100minutes
Work on homework problems 90minutes
2. Mathematical Foundations:
- Basic Calculus
- Optimality
- Vector and Matrix Norms
- Eigenvalues and Definiteness
- Linear and Affine Functions
- Gradient and Hessian Matrices
- Convexity 		Read Chapter 3 in the textbook 100minutes
Work on homework problems 90minutes
3. Classic Optimization Method 1:
- Unconstrained Optimization
- Gradient-Based Methods 		Read Chapter 4 in the textbook 100minutes
Work on homework problems 90minutes
4. Classic Optimization Method 1:
- Constrained Optimization
- Simplex Method
- Karush-Kuhn-Tucker Conditions 		Read Chapter 4 in the textbook 100minutes
Work on homework problems 90minutes
5. Classic Optimization Method 2:
- BFGS Method
- Non-linear Simplex method
- Nelder-Mead Method 		Read Chapter 5 in the textbook. 100minutes
Work on homework problems 90minutes
6. Classic Optimization Method 2:
- Trust-Region Method
- Sequential Quadratic Programming 		Read Chapter 5 in the textbook. 100minutes
Work on homework problems 90minutes
7. Mid-term examination Preparation for the mid-term exam 100minutes
Work on homework problems. 90minutes
8. Convex Optimization:
- Lagrange method for solving constrained non-linear optimization problems;
- Karush Kuhn Tucker (KKT) conditions for a constrained local optimum 		Read Chapter 6 in the textbook 100minutes
Work on homework problems 90minutes
9. Genetic algorithms Read Chapter 11 in the textbook 100minutes
Work on homework problems 90minutes
10. Simulated Annealing Read Chapter 12 in the textbook 100minutes
Work on homework problems 90minutes
11. Particle swarm optimization Read Chapter 15 in the textbook 100minutes
Work on homework problems 90minutes
12. Applications of Engineering Optimization Read Chapter 19 in the textbook 100minutes
Work on homework problems 90minutes
13. MATLAB tools for solving engineering design optimization problems Using MATLAB tools for solving optimization problems 100minutes
Work on homework problems 90minutes
14. Final examination Preparation for final exam 100minutes
Work on homework problems 90minutes
Total. - - 2660minutes
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback outside of the class (ScombZ, mail, etc.) Feedback on exams, assignments, etc., can be done inside of the class/ or outside of the class via Scombz, email, or Google chat.
Textbooks and reference materials
Xin-SheYang , Engineering Optimization: An Introduction with Metaheuristic Applications, A JOHN WILEY ＆ SONS, INC., PUBLICATION July 2010
Prerequisites
Calculus I, II, III
Linear Algebra
Office hours and How to contact professors for questions
  • Weekdays: From 10:00 - 16:30 by email or face-to-face discussion at 6F-building No4 Omiya Campus
  • Dr. Bui Ngoc Tam: tambn@shibaura-it.ac.jp
  • Dr. Shahrol Mohamaddan: mshahrol@shibaura-it.ac.jp
  • Dr. RAJAGOPALAN UMAMAHESWARI: uma@shibaura-it.ac.jp
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
Most classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A N/A
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
Last modified : Sat Mar 14 13:28:31 JST 2026