Discrete Optimization
Master discrete optimization techniques: Constraint programming, local search, and mixed-integer programming for complex problem-solving.
Master discrete optimization techniques: Constraint programming, local search, and mixed-integer programming for complex problem-solving.
This course provides a comprehensive introduction to discrete optimization, covering fundamental concepts and algorithms in the field. Students will learn about constraint programming, local search, and mixed-integer programming, and how to apply these techniques to solve complex practical problems. The curriculum includes in-depth exploration of various optimization methods, from dynamic programming and branch-and-bound to advanced topics like large neighborhood search and column generation. Designed for those with programming experience, it offers a mix of theoretical foundations and practical applications.
4.8
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English
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What you'll learn
Understand and apply fundamental concepts in discrete optimization
Implement constraint programming techniques for complex problem-solving
Develop and analyze local search algorithms and metaheuristics
Apply linear programming and mixed-integer programming to optimization problems
Explore advanced topics such as scheduling, routing, and column generation
Gain practical experience through programming assignments on real-world optimization problems
Skills you'll gain
This course includes:
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There are 8 modules in this course
This course offers a comprehensive exploration of discrete optimization techniques, covering both theoretical foundations and practical applications. The curriculum is divided into eight modules, each focusing on key aspects of optimization. Students begin with an introduction to optimization problems, using the knapsack problem as an example. They then delve into constraint programming, local search techniques, and linear programming. The course progresses to more advanced topics such as mixed-integer programming, scheduling, routing, and advanced concepts like large neighborhood search and column generation. Throughout the course, students engage in challenging programming assignments that apply these optimization techniques to real-world problems, gaining hands-on experience in implementing and analyzing various algorithms.
Welcome
Module 1 · 2 Hours to complete
Knapsack
Module 2 · 6 Hours to complete
Constraint Programming
Module 3 · 17 Hours to complete
Local Search
Module 4 · 13 Hours to complete
Linear Programming
Module 5 · 2 Hours to complete
Mixed Integer Programming
Module 6 · 12 Hours to complete
Advanced Topics: Part I
Module 7 · 10 Hours to complete
Advanced Topics: Part II
Module 8 · 41 Minutes to complete
Fee Structure
Payment options
Financial Aid
Instructors
Leader in Data-Intensive Computing and Optimization Research
Pascal Van Hentenryck holds a Vice-Chancellor Strategic Chair in Data-Intensive Computing at the Australian National University and leads the Optimisation Research Group at NICTA. Before this, he was a professor of computer science at Brown University for over 20 years and also served as a professor at the University of Melbourne. His primary research interests include the design and implementation of optimization systems, with current projects focusing on logistics, supply chains, energy, and disaster management.
Adjunct Lecturer
Dr. Carleton Coffrin is a staff researcher at Los Alamos National Laboratory (LANL), specializing in the application of advanced optimization methods to decision support systems in power systems and disaster management. Since joining LANL in 2016, he has focused on leveraging cutting-edge optimization techniques to address critical challenges in these domains. Dr. Coffrin completed his Ph.D. at Brown University, where he studied hybrid optimization for disaster management under the guidance of Pascal Van Hentenryck and Russell Bent. With a strong passion for optimization education, he actively contributes to the Discrete Optimization and Modeling Discrete Optimization MOOCs, sharing his expertise with a broader audience.
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4.8 course rating
760 ratings
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