Introduction to Optimization
Master fundamental optimization techniques through practical applications: Learn linear programming, network flows, and game theory with real-world examples.
Master fundamental optimization techniques through practical applications: Learn linear programming, network flows, and game theory with real-world examples.
This comprehensive course covers modern optimization fundamentals, balancing theory and practical implementation. Students explore linear and nonlinear programming, network flow models, and game theory applications. The curriculum progresses from basic linear programming concepts through advanced topics like convex optimization and routing games. Real-world applications include supply chain optimization, subway system modeling, and urban railway management. The course emphasizes hands-on implementation using tools like Microsoft Excel while building a strong theoretical foundation.
Instructors:
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What you'll learn
Master the simplex method for solving linear programming problems
Implement optimization techniques using Microsoft Excel
Understand duality theory and game theoretic applications
Solve network flow problems with specialized algorithms
Apply nonlinear programming to real-world optimization challenges
Model and analyze complex systems using routing games
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 8 modules in this course
This course provides a thorough introduction to optimization techniques with balanced coverage of theory and practical applications. The curriculum spans from fundamental linear programming to advanced topics in nonlinear optimization and game theory. Students learn through diverse applications including supply chain management, network flows, and urban transportation systems. The course emphasizes practical implementation while building strong theoretical understanding.
Introduction to Optimization and Linear Programming
Module 1 · 2 Hours to complete
Two-stage Simplex Method and Applications
Module 2 · 2 Hours to complete
Duality Theory and Game Theory
Module 3 · 2 Hours to complete
Network Optimization
Module 4 · 2 Hours to complete
Introduction to Nonlinear Optimization
Module 5 · 2 Hours to complete
Convex Optimization
Module 6 · 2 Hours to complete
Gradient-Based Algorithms
Module 7 · 2 Hours to complete
Subway System Modeling
Module 8 · 2 Hours to complete
Fee Structure
Instructors
1 Course
Leading Korean Optimization Expert Advances Industrial Engineering Research
Dr. Sung-Pil Hong, a distinguished Professor in the Department of Industrial Engineering at Seoul National University, has established himself as a leading authority in optimization theory and its applications. After earning his Ph.D. in Industrial Engineering & Operations Research from UC Berkeley, where he specialized in convex quadratic programming, he has dedicated his career to advancing the field of optimization and operations research. Since joining SNU in 2006, following his tenure at Chung-Ang University and a visiting professorship at the University of Waterloo, Hong has contributed significantly to areas including polynomial optimization, conic relaxation of combinatorial optimization, and network analysis. As the editor-in-chief of the International Journal of Management Science and through his course "Introduction to Optimization," he continues to shape the next generation of industrial engineers while advancing theoretical and practical applications in optimization theory
1 Course
Teaching Assistant Advances Optimization Studies at SNU
Min Hee Lee serves as a Teaching Assistant in the Department of Industrial Engineering at Seoul National University, where she supports Professor Sung-Pil Hong in delivering the "Introduction to Optimization" course. Working within one of Korea's premier engineering programs, she helps students master fundamental concepts in optimization theory, mathematical programming, and their practical applications in industrial engineering. Her role contributes to developing future engineers' understanding of critical optimization principles and problem-solving methodologies
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