About this course: 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. Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people. It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals. This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field. It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation.
Who is this class for: Good programming skills, knowledge of fundamental algorithms, and linear algebra.
Taught by: Professor Pascal Van Hentenryck
Taught by: Dr. Carleton Coffrin, Adjunct Lecturer
Computing and Information Systems
| Level | Intermediate |
| Commitment | 8 weeks of study, 10-15 hours per week |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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One of the most challenging and interesting sources among all that I've passed so far. The lector has it's own hilarious style of teaching. All the material is carefully created and well structured. Every time that you pass the programming assignment, you feel that it's a real achievement.
One of the best courses, really learned a lot
I guess this could be the most challenging course I've had on Coursera.
1, almost all assignments are NP-hard combinatorial optimization problems.
2, data scale can be huge, assessment criteria can be strict.
3. choosing between CP, MIP solver and all kinds of stochastic and meta-heuristic algorithms is fun, gives you the chance to learn both with hands-on experience.
4. even discouraged from doing so, you'll still read a lot of research papers, about 20+ for me.
5. several thousands of SLOC to be expected, if you're honest with yourself.
6. even if you're looking to cheat your way through, you still need solid programming ability and understanding of the problem to do it, so don't bother, why ruin all the fun?
7. the facility location problem drove me nuts.
8. randomization is Pandora's box.
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XW
Awesome course, learned a lot for solving NP hard problem. It could be hard for people without basic algorithm and data structure background.