About This Course

This course is aimed at graduate students. Through the course, students can learn about cutting-edge optimization basic theories and how to use the learned theories to solve practical problems. This course mainly introduces the basic theory and algorithms of continuous optimization, including the optimality conditions of constrained optimization, unconstrained optimization, and classical algorithms. In addition, it covers the application of semi smooth Newton's method method and classical optimization algorithm in frontier scientific research problems. Through course learning, one can become familiar with the basic algorithm design of optimization and how to use optimization methods to solve practical problems. This course has the following features:
1. Case based teaching, introducing cutting-edge research cases, introducing the application of optimization algorithms in cutting-edge research, and allowing learners to see three-dimensional knowledge points. 2. Research based courses: Based on case based teaching, integrate personal research gains with teaching, break down barriers between teaching and research, achieve mutual promotion between teaching and research, and form a virtuous cycle. 3. Stimulate the potential of applying what is learned, so that knowledge points are presented under the pull of demand, and the understanding of knowledge points forms a closed loop from origin to development to application. 4. Graphical presentation makes the understanding of knowledge points simple and easy to grasp.

Requirements

Mathematical Analysis, Advanced Algebra

Course Staff

Qingna Li #1

Li Qingna, doctor, professor and doctoral supervisor of School of Mathematics and Statistics, Beijing Institute of Technology. Hosting youth and general programs funded by the National Natural Science Foundation of China At present, he is a young director of the Mathematics Optimization Branch of the China Operation Research Society and a director of the Beijing Operation Research Society. As a front-line teacher, I undertake the education and teaching of undergraduate and graduate students of Beijing Institute of Technology. Mainly researching optimization theory and algorithms, as well as their applications in the fields of medicine, communication, and artificial intelligence. He has written teaching materials such as "Lectures on Convex Analysis" and monograph "Multidimensional scaling Method". #1

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