Basic Modeling for Discrete Optimization
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Basic Modeling for Discrete Optimization at Coursera Overview
Duration | 28 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Basic Modeling for Discrete Optimization at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 28 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Basic Modeling for Discrete Optimization at Coursera Course details
- 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.
- This course is intended for students interested in tackling all facets of optimization applications. You will learn an entirely new way to think about solving these challenging problems by stating the problem in a state-of-the-art high level modeling language, and letting library constraint solving software do the rest. This will allow you to unlock the power of industrial solving technologies, which have been perfected over decades by hundreds of PhD researchers. With access to this advanced technology, problems that are considered inconceivable to solve before will suddenly become easy.
- Watch the course promotional video here: https://www.youtube.com/watch?v=hc3cBvtrem0&t=8s
Basic Modeling for Discrete Optimization at Coursera Curriculum
MiniZinc introduction
Welcome to Basic Modeling for Discrete Optimization
1.1.1 First Steps
1.1.2 Second Model
1.1.3 Third Model
1.1.4 Models and Instances
1.1.5 Modeling Objects
1.1.6 Arrays and Comprehensions
1.1.7 Global Constraints
1.1.8 Module 1 Summary
Workshop 0 Solution
Workshop 1 Solution
Assignment Submission - IDE
Assignment Submission - CLI
Reference 1: Basic Features
Reference 2: Booleans Expressions
Reference 3: Sets, Arrays, Comprehensions
Reference 4: Enumerated Types
Reference 5: Strings and Output
Reference 6: Option Types
Reference 7: Command Line Interface
Course Overview
Start of Course Survey (Research Team: NTHU & CUHK)??Get the course Signature T-shirt??
Start of Course Survey (Researcher: Professor Gregor Kennedy, Melbourne Centre for the Study of Higher Education)
?Building Decision Support Systems using MiniZinc? by Professor Mark Wallace
Getting MiniZinc
Workshop 0: First Steps
Workshop 1: Temperature
About the Reference Material
Modeling with Sets
1.2.1 Selecting a Set
1.2.2 Choosing a Set Representation
1.2.3 Choosing a Fixed Cardinality Set
1.2.4 Sets with Bounded Cardinality
1.2.5 Module 2 Summary
Workshop 2 Solution
Workshop 2: Surrender Negotiations
Modeling with Functions
1.3.1 Modeling Functions
1.3.2 Another Assignment Problem Example
1.3.3 Modeling Partitions
1.3.4 Global Cardinality Constraint
1.3.5 Pure Partitioning
1.3.6 Module 3 Summary
Workshop 3 Solution
Workshop 3: Feast Trap
Multiple Modeling
1.4.1 Multiple Modeling
1.4.2 Permutation
1.4.3 More Permutation Problem
1.4.4 More Multiple Models
1.4.5 Module 4 Summary
Workshop 4 Solution
Workshop 4: Composition
End of Course Survey (Research Team: NTHU & CUHK)??Get the course Signature T-shirt??
End of Course Survey (Researcher: Professor Gregor Kennedy, Melbourne Centre for the Study of Higher Education)
Basic Modeling for Discrete Optimization at Coursera Admission Process
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