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.

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.

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!

This was harder work than the first two courses in the series. I found it worthwhile even though it took far longer than 4 weeks to complete, mainly because of other commitments. I'm not sure that it was enjoyable, but I'm glad I did it, completed it, and finished it having achieved higher marks than I did on the first two courses.

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.

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

Great innovation for new way of thinking

very poor