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Solving Algorithms for Discrete Optimization

Coursera · The University of Melbourne · 3 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Solving Algorithms for Discrete Optimization" from The University of Melbourne.
Course Description

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.

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Provider Info
This course is offered by The University of Melbourne on the Coursera platform.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this url.
SICP contains a chapter on non-deterministic programming that’s similar to how Prolog executes:

There’s also a chapter on logic programming but it doesn’t quite work the same way as Prolog:

Peter Norvig has a couple of chapters (11,12) on how to implement Prolog in his book PAIP. He also has a chapter (17) on constraint satisfaction but IIRC it’s not integrated into his Prolog system:

Many real Prolog implementations are based on the WAM. For an excellent introduction to the WAM look at:

I don’t have books or papers on implementing constraints and how to integrate them with Prolog. But here are two great classes on Coursera that explain how constraints and constraint propagation work. You can audit them for free:

Discrete Optimization by Hentenryck

Solving Algorithms for Discrete Optimization by Lee and Stuckey

I believe constraints are typically integrated into Prolog through attributed variables:

But I don’t really know how that works. Markus Triska has a few papers on constraint solvers for SWI Prolog on his webpage. He can probably give you more and better pointers:

Many thanks for this very complete response!

It seems to be a niche topic so I didn't expect to find a resource that exactly matched.

I found a paper on compiling constraints for clp(fd) in the context of GNU Prolog:
I found a paper on compiling constraints for clp(fd) in the context of GNU Prolog:
I have enjoyed these courses on Coursera. You can audit them for free.

Discrete Optimization

Solving Algorithms for Discrete Optimization

The second course is the last part of a three part series. The first two parts are on modeling of problems with MiniZinc. They are also quite enjoyable.

Interesting. I don't think that would be my first choices of study.

If you want to know more about how constraint programming works under the hood, I can recommend the coursera course Solving algorithms for Discrete Optimization, in particular weeks 1 and 2 for constraint programming.

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