Course title
M02300003
Fundamentals of Engineering Optimization

 BUI NGOC TAM

RAJAGOPALAN UMAMAHESWARI

SHAHROL BIN MOHAMADDAN
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.
Purpose of class
This course aims to provide fundamental knowledge of engineering optimization theory that can be applied to relevant real design problems.
Goals and objectives

 Goals and objectives Course Outcomes
1. Students can understand the main optimization concepts and the benefits of using optimization in engineering design.
A-1
2. Students can learn various aspects of Optimization Algorithms.
A-1
3. Students can apply optimization methods to solve various problems in the field of engineering
A-1
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% -
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 100分
Work on homework problems 90分
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 100分
Work on homework problems 90分
3. Classic Optimization Method 1:
- Unconstrained Optimization
- Gradient-Based Methods 		Read Chapter 4 in the textbook 100分
Work on homework problems 90分
4. Classic Optimization Method 1:
- Constrained Optimization
- Simplex Method
- Karush-Kuhn-Tucker Conditions 		Read Chapter 4 in the textbook 100分
Work on homework problems 90分
5. Classic Optimization Method 2:
- BFGS Method
- Non-linear Simplex method
- Nelder-Mead Method 		Read Chapter 5 in the textbook. 100分
Work on homework problems 90分
6. Classic Optimization Method 2:
- Trust-Region Method
- Sequential Quadratic Programming 		Read Chapter 5 in the textbook. 100分
Work on homework problems 90分
7. Mid-term examination Preparation for the mid-term exam 100分
Work on homework problems. 90分
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 100分
Work on homework problems 90分
9. Genetic algorithms Read Chapter 11 in the textbook 100分
Work on homework problems 90分
10. Simulated Annealing Read Chapter 12 in the textbook 100分
Work on homework problems 90分
11. Particle swarm optimization Read Chapter 15 in the textbook 100分
Work on homework problems 90分
12. Applications of Engineering Optimization Read Chapter 19 in the textbook 100分
Work on homework problems 90分
13. MATLAB tools for solving optimization problems Using MATLAB tools for solving optimization problems 100分
Work on homework problems 90分
14. Final examination Preparation for final exam 100分
Work on homework problems 90分
Total. - - 2660分
Goals and objectives (Other Courses)
A:Fundamental Mechanical Engineering B:Advanced Mechanical Engineering C:Environment and Materials Engineering D:Chemistry and Biotechnology E:Electrical Engineering and Robotics G:Advanced Electronic Engineering F:Information and Communications Engineering L:Computer Science and Engineering H:Urban Infrastructure and Environment
Language
English
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.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in 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 : Thu Mar 13 04:12:17 JST 2025