Optimization is a common form of decision making, and is ubiquitous in our society. Its applications range from solving Sudoku puzzles to arranging seating in a wedding banquet. The same technology can schedule planes and their crews, coordinate the production of steel, and organize the transportation of iron ore from the mines to the ports. Good decisions in manpower and material resources management also allow corporations to improve profit by millions of dollars. Similar problems also underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods.
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Basic Modeling for Discrete Optimization
墨尔本大学課程信息
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墨尔本大学
The University of Melbourne is an internationally recognised research intensive University with a strong tradition of excellence in teaching, research, and community engagement. Established in 1853, it is Australia's second oldest University.
香港中文大学
Founded in 1963, The Chinese University of Hong Kong (CUHK) is a forward looking comprehensive research university with a global vision and a mission to combine tradition with modernity, and to bring together China and the West. CUHK teachers and students hail from all corners of the world. CUHK graduates are connected worldwide through an expansive alumni network.
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MiniZinc introduction
In this first module, you will learn the basics of MiniZinc, a high-level modeling language for discrete optimization problems. Combining the simplicity of MiniZinc with the power of open-source industrial solving technologies, you will learn how to solve applications such as knapsack problems, graph coloring, production planning and tricky Cryptarithm puzzles, with great ease.
Modeling with Sets
In this module, you will learn how to model problems involving set selection. In particular, you will see different ways of representing set variables when the variable has no constraints on its cardinality, has fixed cardinality and bounded cardinality. You also have to ensure all model decisions are valid decisions, and each valid decision corresponds to exactly one model decision.
Modeling with Functions
In this module, you will learn how to model pure assignment problems and partition problems, which are functions in disguise. These problems find applications in rostering and constrained clustering. In terms of modeling techniques, you will see the power of common subexpression elimination and intermediate variables, and encounter the global cardinality constraint for the first time. MiniZinc also provides constraints for removing value symmetries.
Multiple Modeling
In the final module of this course you will see how discrete optimization problems can often be seen from multiple viewpoints, and modeled completely differently from each viewpoint. Each viewpoint may have strengths and weaknesses, and indeed the different models can be combined to help each other.
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來自BASIC MODELING FOR DISCRETE OPTIMIZATION的熱門評論
Super interesting. Would be nice to have corrections of the assignment too but overall, super fun. The pdf description are sometimes unclea (constraints are sometimes put in the data description.)
Excellent course! I learned a lot. Although the assignments were sometimes hard to fathom, and its online nature mean getting hints was difficult, there's enough there to help get through.
great course for beginners and those who are looking for applications of optimisation. the only problem is it forces you to work with minizinc and may consume extra time than expected
Thank you so much for the course. I had to fight my coding habits, programming CP models is quite different from the traditional programming I'm, and it could even be more fun!
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