Master complex discrete optimization techniques. Learn debugging, predicates, scheduling, and packing in MiniZinc. Solve real-world problems.
Master complex discrete optimization techniques. Learn debugging, predicates, scheduling, and packing in MiniZinc. Solve real-world problems.
Elevate your discrete optimization skills with this advanced course. Building on the Basic Modelling for Discrete Optimization course, you'll learn to tackle complex problems using state-of-the-art modeling techniques. Master debugging and improving models, using predicates for modular design, and solving advanced scheduling and packing problems. Through hands-on assignments, you'll apply these concepts to real-world scenarios, from project scheduling to efficient packing. Gain expertise in symmetry breaking and dominance techniques to enhance solving efficiency. This course equips you with the skills to model and solve previously intractable optimization challenges using the MiniZinc language and constraint solving software.
4.9
(134 ratings)
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Instructors:
English
Tiếng Việt
What you'll learn
Master techniques for debugging and improving complex optimization models
Learn to use predicates for modular and reusable constraint definitions
Develop skills in modeling advanced scheduling problems with various resource constraints
Understand and apply packing algorithms for efficient space utilization
Explore symmetry breaking and dominance techniques to enhance solving efficiency
Gain practical experience in solving real-world optimization challenges
Skills you'll gain
This course includes:
10 Hours PreRecorded video
4 programming assignments
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FullTime access
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There are 5 modules in this course
This course provides an advanced exploration of discrete optimization modeling techniques. Students will learn to debug and improve complex models, use predicates for modular design, and tackle advanced scheduling and packing problems. The curriculum covers symmetry breaking and dominance techniques to enhance solving efficiency. Through five modules, participants gain hands-on experience with real-world optimization challenges, including project scheduling, efficient packing, and multi-objective optimization. The course emphasizes practical application using the MiniZinc language and constraint solving software, preparing students to model and solve previously intractable problems in various domains.
Debugging and Improving Models
Module 1 · 11 Hours to complete
Predicates
Module 2 · 9 Hours to complete
Scheduling
Module 3 · 9 Hours to complete
Packing
Module 4 · 42 Minutes to complete
Symmetry and Dominance
Module 5 · 14 Hours to complete
Fee Structure
Payment options
Financial Aid
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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4.9 course rating
134 ratings
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