Tired of solving Sudokus by hand? This class teaches you how to solve complex search problems with discrete optimization concepts and algorithms, including constraint programming, local search, and mixed-integer programming.
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이산형 최적화
멜버른 대학교이 강좌에 대하여
귀하가 습득할 기술
- Constraint Programming
- Branch And Bound
- Discrete Optimization
- Linear Programming (LP)
제공자:

멜버른 대학교
The University of Melbourne is an internationally recognised research intensive University with a strong tradition of excellence in teaching, research, and community engagement. Established in 1853, it is Australia's second oldest University.
강의 계획표 - 이 강좌에서 배울 내용
Welcome
These lectures and readings give you an introduction to this course: its philosophy, organization, and load. They also tell you how the assignments are a significant part of the class. This week covers the common input/output organization of the assignments, how they are graded, and how to succeed in this class.
Knapsack
These lectures introduce optimization problems and some optimization techniques through the knapsack problem, one of the most well-known problem in the field. It discusses how to formalize and model optimization problems using knapsack as an example. It then reviews how to apply dynamic programming and branch and bound to the knapsack problem, providing intuition behind these two fundamental optimization techniques. The concept of relaxation and search are also discussed.
Constraint Programming
Constraint programming is an optimization technique that emerged from the field of artificial intelligence. It is characterized by two key ideas: To express the optimization problem at a high level to reveal its structure and to use constraints to reduce the search space by removing, from the variable domains, values that cannot appear in solutions. These lectures cover constraint programming in detail, describing the language of constraint programming, its underlying computational paradigm and how it can be applied in practice.
Local Search
Local search is probably the oldest and most intuitive optimization technique. It consists in starting from a solution and improving it by performing (typically) local perturbations (often called moves). Local search has evolved substantially in the last decades with a lot of attention being devoted on which moves to explore. These lectures explore the theory and practice of local search, from the concept of neighborhood and connectivity to meta-heuristics such as tabu search and simulated annealing.
검토
- 5 stars89.05%
- 4 stars8.13%
- 3 stars1.03%
- 2 stars0.14%
- 1 star1.62%
이산형 최적화의 최상위 리뷰
The class is great. Lectures are engaging and I can't believe how much I've learned, despite knowing nothing going in.
This is, without a doubt, one of the most interesting courses I have ever taken. You will be challenged to create your own ideas and you will get to know what NP hard means in practice.
Well-defined course and interesting lecturer. He's highly engaging although the material of the course is pretty technical. Thanks a lot for making this course!!!!
This was more than introduction for me. Loved every second for me, got grades, but will try to improve obviously.
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