|Course title||Numerical Optimization
|Course description||This course aims to provide students with the basic knowledge of optimization theory and introduce various computational libraries and programming techniques to perform optimization. Topics include unconstrained optimization methods, conjugate gradient methods, quasi-newton methods, theory of constrained optimization, linear programming, non-linear constrained optimization, etc.
|Pre-requisites||ENGG1120/ESTR1005 or ENGG1130/ESTR1006 or MATH1510|
|Grade Descriptors||A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions.
B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered.
C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems.
D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems.
F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course.
|Learning outcomes||At the end of the course of studies, students will have acquired the ability to
1. formulate unconstrained optimization problems;
2. solve unconstrained optimization problems, either analytically or via computing modules;
3. formulate constrained optimization problems;
4. solve constrained optimization problems, either analytically or via computing modules.
(for reference only)
|Essay test or exam: 60%
Homework or assignment: 40%
|Recommended Reading List||1. Numerical Optimization by Jorge Nocedal, Stephen J. Wright
2. A Gentle Introduction to Optimization by B. Guenin, J. Knnemain, L. Tuncel
3. Introduction to Optimization, by Edwin K.P. Chong, Stanislaw H. Zak
4. Optimization Theory: A Concise Introduction, by Jiongmin Yong
|AISTN programme learning outcomes||Course mapping|
|Upon completion of their studies, students will be able to:|
|1. identify, formulate and solve AI-related engineering problems (K/S);||Y|
|2. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S/V);
|3. understand the impact of AI solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (K/V);|
|4. communicate and work effectively in multi-disciplinary teams (S/V);
|5. apply knowledge of mathematics, science, and engineering appropriate to the AI degree discipline (K/S);
|6. design and conduct experiments, as well as to analyze and interpret massive data (K/S);|
|7. use the techniques, skills, and modern computing tools necessary for engineering practice appropriate to the AI and computing discipline (K/S);|
|8. understand professional and ethical responsibility (K/V); and|
|9. recognize the need for and the importance of life-long learning (V).
|Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes|