I.The Basic Information of the Course
Course Number:202032020006
The EnglishName of theCourse：Optimization Theory and Method
The ChineseName of theCourse：最优化理论与方法
In-classHours andAllocation(total class hours:32, classroom teaching: 32 classhours)
Credit(s):2
Semester:1
AppliedDiscipline(Professional Degree Category)：Science and Engineering
CourseObjectOriented：Master,Doctor
EvaluationMode:Closed-book Examination:80%, ordinary achievement: 20%
TeachingMethod:Blended teaching method
CourseOpening Department:College of Mathematical Science
II.Prerequisite Course
Students whostudy this course should have a certain mathematical theoretical basis, should have studied mathematics analysis, linear algebra and other courses, and understand the main theories and ideas of computer algorithm design and analysis.
III.The Objectives and Requirements of the Course
Optimization method is a widely used subject. It discusses the characteristics of the optimal choice of decision-making problems, constructs the calculation method to seek the optimal solution, and studies the theoretical properties and the actual performance of these method. Through the study of the optimization methods, the students can understand the theory and method in order to solve the mathematical problems in economic planning, process design and production. By learning this course, the students are required to find the numerical optimized solution or approximate solution of the expression, and they can give solutions to the optimal performance, optimal indicators, optimal program problems and corresponding mathematical models required in practical problems. It also briefly introduces the latest fields and directions of this subject.
IV.The Content of the Course
1.Linear programming and integer programming: including the basic mathematical model and solution methods of unconstrained linear programming, the basic mathematical model and solving methods of integer programming, and the modeling and solving method of assignment problem.
2.Nonlinear programming: including unconstrained nonlinear programming, the basic mathematical model of constrained nonlinear programming and its optimal solution methods.
3.Data optimization: including the concept of least squares and typical problems, linear regression, nonlinear regression, etc.
4.Modern optimization methods, such as genetic algorithm, particle swarm optimization algorithm and artificial neural network algorithm, and their applications in optimization problems.
Ideological and political cases：
1.The germination of optimization thoughts and their initial use can be traced back to ancient China. The military thoughts in the “Sun Tzu's art of war”, which is known as the "First Wonder Book of the World" by the west, such as the five elements of art of War: degree, quantity, number, scale, victory, etc.,have many similarities with the proposal and decision-making of today's optimization plan. " Sun Tzu's art of war" is a systematic and complete book of countermeasures and strategies that takes war as the research object, including countermeasure wisdom, countermeasure principles, countermeasure types, and countermeasure methods. As a book of countermeasures and strategies, it not only has certain optimized characteristics, but also constitutes a zero-sum dynamic game model under unilateral complete information. If starting from the way of thinking of the game, " Sun Tzu's art of war" is based on "wisdom", with " plan " as the core, and "mou" as the highest state (optimization), in the application of "plan" and "mou" Complete the optimization process of the single player game. The "plan" can be understood as "countermeasures", which includes the choice of "countermeasures" under various environments and conditions; the "strategy" can be understood as an optimal state or status, which is not only the result of "plan", but also the choice higher than "plan". It is the highest and the most perfect strategic goal and war realm.
2.Modern intelligent optimization methods have many applications in practical engineering. sFor example, the multi-AUV collaborative intelligent decision-making and control theory method is currently a research hotspot in the field of multiple intelligent unmanned systems. Let the students find and share the literature and materials by themselves, understand the research progress of multi AUV collaborative intelligent decision-making problems at home and abroad, understand our school's contribution to multi AUV research, understand the modeling method of multi-AUV collaborative formation problem and its solution ideas, etc. , And analyze the swarm intelligence method based on ant colony algorithm and particle swarm algorithm, behavior-based method, leader-follower method and machine learning (such as reinforcement learning) based method in multiple AUV path planning, formation maintenance And obstacle avoidance. Through this application example, we will show students the achievements made by our school in this field, stimulate students' interest in learning and pride, as well as passion for research and patriotism, and hope that they will make greater contributions to my country's scientific research in the future.
V. Reference Books, Reference Literatures, and Reference Materials
A. Text Books, Monographs and References
1. Huang P, Meng Y.G., Optimization Theory and Method, Tsinghua University Press,2009
2. Ashok D. Belegundu(Writing), Li Z.Y.(translating), Optimization concepts and Applications in Engineering (2nd Edition), Electronic Industry Press,2018
3.Chen B.L., Optimization theory and algorithm (2nd Edition), Tsinghua University Press, 2005.
4. Edwin K. P. Chong; Stanislaw H. Zak, An Introduction to Optimization, fourth edition,Wiley, 2013.
5. Shi G.Y., Dong J.L., Optimization Methods(2nd Edition),Higher Education Press, 2004.
6. Yuan Y.X., Sun W.Y., Optimization Theory and Method,Science Press, 1999.
B. Learning Resources
The name of the platform and website for online course learning
Tencent Classroom: Optimization Methods,
https://ke.qq.com/course/943967?taid=1349010
Outline Writer (Signature):
Leader in charge of teaching at the College (Signature)：
Date：
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