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.
Basic Modeling for Discrete Optimization
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Basic Modeling for Discrete Optimization
Acerca de este Curso
Habilidades que obtendrás
100 % en línea
Fechas límite flexibles
Nivel intermedio
Aprox. 20 horas para completar
Inglés (English)
Programa - Qué aprenderás en este curso
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.
Revisiones
Principales revisiones sobre BASIC MODELING FOR DISCRETE OPTIMIZATION
Super interesting. Would be nice to have corrections of the assignment too but overall, super fun.\n\nThe pdf description are sometimes unclea (constraints are sometimes put in the data description.)
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!
Challenging programming assignments and helpful workshops. Good video material. Hoped to have some more real world examples, rather than just fable-based examples.
I recommend this course for any one starts constraint programming. It is easy to understand but I think it is required logic background.
Good teachers. They go gradually from very easy to challenging concepts. Also a good intro to the main optimization problems.
Wonderful course, very engaging and professionally made. A great course to begin programming in minizinc.
Certainly more effort went into this course than I was expecting; well worth the $0 cost of entry.
very nice introduction to minizinc with great and easy assignments to get you started
A very interesting course! I learned a lot from this course. Thanks instructors!
A creative way of teaching discrete optimization. Well appreciated.
A very good course. Thank you to the teachers and Staff in general.
Acerca de Universidad de Melbourne
Acerca de Universidad China de Hong Kong
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