Solving Algorithms for Discrete Optimization
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.
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- Level Professional
- المدة 22 ساعات hours
- الطبع بواسطة The University of Melbourne
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Offered by
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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.الوحدات
Course Preliminaries
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1
Videos
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3
Readings
1
Videos
- Welcome to Solving Algorithms for Discrete Optimization
3
Readings
- Course Overview
- Start of Course Survey
- “Building Decision Support Systems using MiniZinc” by Professor Mark Wallace
Constraint Programming
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6
Videos
6
Videos
- 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
Activities
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1
Videos
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1
Readings
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1
Programming
- Birthday Parade
1
Videos
- Workshop 9
1
Readings
- Workshop 9: CP Basic Search Strategies
Constraint Programming
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6
Videos
6
Videos
- 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
Activities
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1
Videos
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1
Readings
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1
Programming
- Banquet Preparation
1
Videos
- Workshop 10
1
Readings
- Workshop 10: CP Advanced Search Strategies
Mixed Integer Programming
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5
Videos
5
Videos
- 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
Activities
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1
Videos
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1
Readings
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1
Programming
- Kitchen Roster
1
Videos
- Workshop 11
1
Readings
- Workshop 11: MIP Modelling
Local Search
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9
Videos
9
Videos
- 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
Activities
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1
Videos
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1
Readings
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1
Programming
- Refugee Assignment
1
Videos
- Workshop 12
1
Readings
- Workshop 12: Local Search
Course Summary
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1
Readings
1
Readings
- End of Course Survey
Auto Summary
"Solving Algorithms for Discrete Optimization" is a professional-level course in IT & Computer Science offered on Coursera. It focuses on making optimal decisions from numerous possibilities, applicable in diverse fields like scheduling, production, and logistics. Taught by expert instructors, the course extends knowledge from Advanced Modelling for Discrete Optimization, emphasizing solving technologies and high-level modeling with MiniZinc. Spanning 1320 minutes, learners can subscribe through Starter, Professional, or Paid options. Ideal for students aiming to enhance their problem-solving and modeling skills in complex optimization scenarios.
Prof. Jimmy Ho Man Lee
Prof. Peter James Stuckey