John Hopkins University - Advanced Modeling for Discrete Optimization
- Offered byCoursera
Advanced Modeling for Discrete Optimization at Coursera Overview
Duration | 47 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Advanced Modeling for Discrete Optimization at Coursera Highlights
- 20% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
- Flexible deadlines according to your schedule.
Advanced 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 who have completed Basic Modelling for Discrete Optimization. In this course you will learn much more about solving challenging discrete optimization 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 course will focus on debugging and improving models, encapsulating parts of models in predicates, and tackling advanced scheduling and packing problems. As you master this advanced technology, you will be able to tackle problems that were inconceivable to solve previously.
- Watch the course promotional video here: https://www.youtube.com/watch?v=hc3cBvtrem0&t=8s
Advanced Modeling for Discrete Optimization at Coursera Curriculum
Debugging and Improving Models
Welcome to Advanced Modeling for Discrete Optimization
2.1.1 Model Debugging
2.1.2 Tracing Models
2.1.3 Relational Semantics
2.1.4 Too Many Solutions
2.1.5 Missing Solutions
2.1.6 Basic Model Improvement
2.1.7 Module 1 Summary
Workshop 5 Solution
Assignment Submission - IDE
Assignment Submission - CLI
Reference 1: Basic Features
Reference 2: Booleans Expressions
Reference 3: Sets, Arrays and Comprehensions
Reference 4: Enumerated Types
Reference 5: Strings and Output
Reference 6: Option Types
Reference 7: Predicates
Reference 8: Flattening
Reference 9: Transforming Data
Reference 10: User Defined Functions
Reference 11: Command Line Interface
Course Overview
Start of Course Survey
?Building Decision Support Systems using MiniZinc? by Professor Mark Wallace
Getting MiniZinc
Workshop 5: Poetry Challenge
About the Reference Material
Predicates
2.2.1 Predicates
2.2.2 The let-in Construct
2.2.3 Using Predicates
2.2.4 Contexts
2.2.5 Module 2 Summary
Workshop 6 Solution
Workshop 6: Weighing an Elephant: Part 1
Scheduling
2.3.1 Basic Scheduling
2.3.2 Disjunctive Scheduling
2.3.3 Cumulative Scheduling
2.3.4 Sequence Dependent Scheduling 1
2.3.5 Sequence Dependent Scheduling 2
2.3.6 Module 3 Summary
Workshop 7 Solution
Workshop 7: Visiting Zhuge Liang
Packing
2.4.1 Square Packing
2.4.2 Rectilinear Packing without Rotation
2.4.3 Rectilinear Packing with Rotation
Symmetry and Dominance
2.5.1 Symmetries and LexLeader
2.5.2 Matrix Model Symmetries
2.5.3 Value Symmetries
2.5.4 Dominance
2.5.5 Module 4 & 5 Summary
Workshop 8 Solution
Where to from here?
Workshop 8: The Dieda Plasters
End of Course Survey
Advanced Modeling for Discrete Optimization at Coursera Admission Process
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