Operations Research (2): Optimization Algorithms
Master advanced algorithms for linear, integer, and nonlinear optimization problems in operations research.
Master advanced algorithms for linear, integer, and nonlinear optimization problems in operations research.
This intermediate-level course focuses on efficient algorithms for solving various optimization problems in operations research. Students will learn advanced techniques such as the simplex method, branch-and-bound algorithm, gradient descent, and Newton's method. The course covers practical implementation using Gurobi solver with Python. Through lectures, quizzes, and case studies, learners will develop skills to solve complex optimization problems in business, economics, and engineering contexts.
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What you'll learn
Master the simplex method for solving linear programming problems
Implement the branch-and-bound algorithm for integer programming
Apply gradient descent and Newton's method to nonlinear optimization
Use Gurobi solver with Python to implement optimization algorithms
Analyze and evaluate algorithm performance
Design heuristic algorithms for complex optimization problems
Skills you'll gain
This course includes:
9.62 Hours PreRecorded video
6 assignments
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There are 6 modules in this course
This advanced course in Operations Research focuses on optimization algorithms for solving linear, integer, and nonlinear programming problems. The curriculum begins with a review of linear algebra concepts before delving into the simplex method for linear programming. Students then explore the branch-and-bound algorithm for integer programming, followed by gradient descent and Newton's method for nonlinear optimization. The course emphasizes practical application, teaching students to implement these algorithms using the Gurobi solver with Python. A case study on facility location problem demonstrates the real-world application of these techniques. Throughout the course, learners develop skills in algorithm design, implementation, and performance evaluation, preparing them for advanced optimization challenges in various industries.
Course Overview
Module 1 · 1 Hours to complete
The Simplex Method
Module 2 · 3 Hours to complete
The Branch-and-Bound Algorithm
Module 3 · 2 Hours to complete
Gradient Descent and Newton's Method
Module 4 · 2 Hours to complete
Design and Evaluation of Heuristic Algorithms
Module 5 · 1 Hours to complete
Course Summary and Future Learning Directions
Module 6 · 1 Hours to complete
Fee Structure
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Instructor
Leading Authority in Nephrology and Toxicology at National Taiwan University.
Dr. Chih-Kang Chiang is an expert in internal medicine, nephrology, toxicology, and food safety risk analysis. He received his Ph.D. in 2006 from the Graduate Institute of Toxicology at National Taiwan University. In 2005, he became a clinical lecturer in the Department of Internal Medicine at National Taiwan University and was promoted to clinical assistant professor in 2007 and clinical associate professor in 2012. He has served as an associate professor since 2014 and was promoted to full professor in 2018 at the Graduate Institute of Toxicology, National Taiwan University. A notable scholar in nephrology and toxicology, Dr. Chiang has published over a hundred peer-reviewed articles throughout his career.
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4.9 course rating
134 ratings
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