Solving Algorithms for Discrete Optimization
Master advanced discrete optimization techniques. Learn constraint programming, mixed integer programming, and local search methods in 22 hours.
Master advanced discrete optimization techniques. Learn constraint programming, mixed integer programming, and local search methods in 22 hours.
Dive deep into solving challenging discrete optimization problems with this intermediate-level course. Extend your understanding of solving technologies used in discrete optimization, including constraint programming, mixed integer programming, and local search methods. Learn how high-level MiniZinc models are transformed for execution by underlying solvers. Improve your modeling capabilities and choose appropriate solving technologies for various optimization scenarios. This course builds on the Advanced Modelling for Discrete Optimization course, offering practical insights into solver mechanics and advanced search strategies.
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What you'll learn
Understand the basic machinery of Constraint Programming solvers
Master advanced search strategies for optimization problems
Explore the inner workings of global constraints like alldifferent and cumulative
Learn linear programming and the Simplex algorithm for continuous optimization
Understand Mixed Integer Programming and cutting plane methods
Explore local search methods and techniques for escaping local minima
Skills you'll gain
This course includes:
5 Hours PreRecorded video
4 programming assignments
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FullTime access
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There are 4 modules in this course
This course delves into the solving technologies used in discrete optimization, focusing on constraint programming, mixed integer programming, and local search methods. Students will learn about constraint propagation, search strategies, and the inner workings of global constraints. The curriculum covers linear programming, the Simplex algorithm, and advanced techniques like Gomory Cuts and Branch and Cut methods. Participants will also explore local search methods, including simulated annealing, tabu search, and large neighborhood search. Throughout the course, students will gain practical experience by applying these concepts to real-world optimization problems using MiniZinc.
Basic Constraint Programming
Module 1 · 5 Hours to complete
Advanced Constraint Programming
Module 2 · 5 Hours to complete
Mixed Integer Programming
Module 3 · 4 Hours to complete
Local Search
Module 4 · 5 Hours to complete
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Instructors
Innovative Educator and Researcher in Constraint Satisfaction
Professor Jimmy Ho Man Lee is a Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, where he specializes in the theory and application of constraint satisfaction and optimization. His research encompasses critical areas such as combinatorial optimization, scheduling, and resource allocation. In addition to his scholarly contributions, which include serving on the editorial boards of several prestigious journals, he was the Program Chair for the 17th International Conference on Principles and Practice of Constraint Programming in Perugia, Italy, in 2011. As an educator, Professor Lee is passionate about transforming the learning experience for his students; he actively engages in pedagogical research focused on folklore-based and game-based learning methodologies. His dedication to teaching excellence has been recognized with the CUHK Vice-Chancellor’s Exemplary Teaching Award, which he received in both 2005 and 2016. Through his research and commitment to innovative teaching practices, Professor Lee is making significant contributions to the fields of computer science and education.
Professor at The University of Melbourne and Pioneer in Constraint Programming
Peter J. Stuckey is a Professor in the Department of Computing and Information Systems at The University of Melbourne. He is a pioneer in the field of constraint programming and has made significant contributions, including co-authoring one of the first constraint logic programming systems, CLP(R), and writing the first textbook on constraint programming. His accolades include the 2010 Google Australia Eureka Prize for Innovation in Computer Science and the 2010 University of Melbourne Woodward Medal for Science and Technology. His current research focuses on developing solver-independent modeling languages for complex optimization problems and advancing solving technologies.
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4.8 course rating
42 ratings
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