Acerca de este Curso
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Nivel intermedio

Aprox. 19 horas para completar

Sugerido: 4 weeks of study, 6-12 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

Constraint ProgrammingProblem SolvingMathematical ModelDiscrete Optimization

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 19 horas para completar

Sugerido: 4 weeks of study, 6-12 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
7 horas para completar

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.

...
20 videos (Total 219 minutos), 6 readings, 1 quiz
20 videos
1.1.3 Third Model6m
1.1.4 Models and Instances10m
1.1.5 Modeling Objects8m
1.1.6 Arrays and Comprehensions16m
1.1.7 Global Constraints9m
1.1.8 Module 1 Summary5m
Workshop 0 Solution19m
Workshop 1 Solution21m
Assignment Submission - IDE7m
Assignment Submission - CLI4m
Reference 1: Basic Features13m
Reference 2: Booleans Expressions13m
Reference 3: Sets, Arrays, Comprehensions19m
Reference 4: Enumerated Types7m
Reference 5: Strings and Output8m
Reference 6: Option Types12m
Reference 7: Command Line Interface8m
6 lecturas
Course Overview10m
Start of Course Survey (Researcher: Professor Gregor Kennedy, Melbourne Centre for the Study of Higher Education)12m
Getting MiniZinc10m
Workshop 0: First Steps20m
Workshop 1: Temperature45m
About the Reference Material2m
Semana
2
5 horas para completar

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.

...
6 videos (Total 64 minutos), 1 reading, 1 quiz
6 videos
1.2.4 Sets with Bounded Cardinality13m
1.2.5 Module 2 Summary3m
Workshop 2 Solution19m
1 lectura
Workshop 2: Surrender Negotiations1h 15m
Semana
3
8 horas para completar

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.

...
7 videos (Total 86 minutos), 1 reading, 1 quiz
7 videos
1.3.4 Global Cardinality Constraint9m
1.3.5 Pure Partitioning14m
1.3.6 Module 3 Summary5m
Workshop 3 Solution28m
1 lectura
Workshop 3: Feast Trap2h 50m
Semana
4
7 horas para completar

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.

...
6 videos (Total 67 minutos), 2 readings, 1 quiz
6 videos
1.4.4 More Multiple Models12m
1.4.5 Module 4 Summary7m
Workshop 4 Solution13m
2 lecturas
Workshop 4: Composition2h 5m
End of Course Survey (Researcher: Professor Gregor Kennedy, Melbourne Centre for the Study of Higher Education)10m
4.8
44 revisionesChevron Right

Principales revisiones sobre Basic Modeling for Discrete Optimization

por CJMay 30th 2019

Good teachers. They go gradually from very easy to challenging concepts. Also a good intro to the main optimization problems.

por CTJun 8th 2019

Certainly more effort went into this course than I was expecting; well worth the $0 cost of entry.

Instructores

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Prof. Peter James Stuckey

Professor
Computing and Information Systems
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Prof. Jimmy Ho Man Lee

Professor
Department of Computer Science and Engineering

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