Volver a Solving Algorithms for Discrete Optimization

Opiniones y comentarios de aprendices correspondientes a Solving Algorithms for Discrete Optimization por parte de Universidad de Melbourne

4.8
estrellas
38 calificaciones

Acerca del Curso

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...

Filtrar por:

1 - 8 de 8 revisiones para Solving Algorithms for Discrete Optimization

por Alex D

11 de oct. de 2018

The course is a good sequel in the “… Discrete Optimization” series. With just about any programming language, the true understanding of how the program is run by the computer helps tuning the program, minimizing the execution time. The same is especially true in optimization, as instead of the “classical” imperative programs we have “models” that are digested by some “solver”, which actually does all the number crunching. Different solvers (and the same solver with different configuration) can behave drastically different while running the same model. So this course finally removes the veil and uncovers the things inside these solvers, that were considered as black boxes in the previous two courses. The course is likely to motivate you to experiment with different solvers for the same models, and, maybe, even implementing your own solver.

por Miles G

24 de sep. de 2020

Tougher than the first two courses in the series, but very rewarding. It was fun to look under the hood of Liu Bei's magic tablet and learn how the algorithms that underlie MiniZinc (and other constrained optimizers) work: armed with this knowledge, we learned about how to use MiniZinc's annotations to improve search performance. We also learned about local search algorithms like simulated annealing, tabu search and (particularly cool) large neighbourhood search.

por lucas d

8 de feb. de 2020

A course to learn in depth the inner workings of CP solvers and how to get the best of them. With an introduction to other advanced techniques to find the best approach to discrete problems (MIP, Local Search). The Romance of the Three Kingdoms-inspired animations and puzzles are engaging and makes for an exciting learning experience.

por Boris O

10 de dic. de 2019

The course was extremely useful. I'm still a long way from mastering the material, but it helped me immensely in understanding some of the aspects of discrete optimization. I now feel inspired to learn more in the field. Many thanks to the creators of the course!

por Jan G

12 de may. de 2019

very good introduction, lessons are fun to watch and exercises are useful

por Leo

20 de jun. de 2019

Great innovation for new way of thinking

por VAISHNAVI S

2 de ago. de 2020

very poor