Solving Algorithms for Discrete Optimization
- Offered byCoursera
Solving Algorithms for Discrete Optimization at Coursera Overview
Duration | 22 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Solving Algorithms 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. 22 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Solving Algorithms for Discrete Optimization at Coursera Course details
- Discrete Optimization aims to make good decisions when we have many possibilities to choose from. Its applications are ubiquitous throughout 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 on the use of scarce or expensive resources such as staffing and material resources also allow corporations to improve their 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, these problems are a nightmare to solve using traditional undergraduate computer science methods.
- This course is intended for students who have completed Advanced Modelling for Discrete Optimization. In this course, you will extend your understanding of how to solve challenging discrete optimization problems by learning more about the solving technologies that are used to solve them, and how a high-level model (written in MiniZinc) is transformed into a form that is executable by these underlying solvers. By better understanding the actual solving technology, you will both improve your modeling capabilities, and be able to choose the most appropriate solving technology to use.
- Watch the course promotional video here: https://www.youtube.com/watch?v=-EiRsK-Rm08
Solving Algorithms for Discrete Optimization at Coursera Curriculum
Basic Constraint Programming
Welcome to Solving Algorithms for Discrete Optimization
3.1.1 Constraint Programming Solvers
3.1.2 Domains + Propagators
3.1.3 Bounds Propagation
3.1.4 Propagation Engine
3.1.5 Search
3.1.6 Module 1 Summary
Workshop 9
Course Overview
Start of Course Survey
?Building Decision Support Systems using MiniZinc? by Professor Mark Wallace
Workshop 9: CP Basic Search Strategies
Advanced Constraint Programming
3.2.1 Optimization in CP
3.2.2 Restart and Advanced Search
3.2.3 Inside Alldifferent
3.2.4 Inside Cumulative
3.2.5 Flattening
3.2.6 Module 2 Summary
Workshop 10
Workshop 10: CP Advanced Search Strategies
Mixed Integer Programming
3.3.1 Linear Programming
3.3.2 Mixed Integer Programming
3.3.3 Cutting Planes
3.3.4 MiniZinc to MIP
3.3.5 Module 3 Summary
Workshop 11
Workshop 11: MIP Modelling
Local Search
3.4.1 Local Search
3.4.2 Constraints and Local Search
3.4.3 Escaping Local Minima- Restart
3.4.4 Simulated Annealing
3.4.5 Tabu List
3.4.6 Discrete Langrange Multiplier Methods
3.4.7 Large Neighbourhood Search
3.4.8 MiniZinc to Local Search
3.4.9 Module 4 Summary
Workshop 12
Workshop 12: Local Search
End of Course Survey
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
TechnologyComputer ScienceData Structures and AlgorithmsSolving Algorithms for Discrete Optimization