There are 5 mandatory labs in the course. They aim to solidify some of the algorithmic concepts announced at the lectures, give a bit of algorithmic engineering experience, and even some best practices for experimentation and report design. 

1. Marking trees
2. Randomized Maximum Cut (old version. For 2017, see the GitHub directory instead)
3. Decrease-and-conquer for Independent Set
4. FedUps (see GitHub directory.) 
5. Treewidth

 The source files for the labs are at github.com/thorehusfeldt/edan55-labs and are updated continually; instead of following the above links to the lab descriptions (which may fall out of date), you better clone the repository and build the descriptions yourself.

Note that these source files include Latex skeletons for your lab reports, which are are encouraged to use. (But you don't have to. It’s meant as a service, and possible learning opportunity.)

 Con amore, you can subject yourself to the older assignments of 2013–2016 as well: Rainbow Perfect Matchings, Collatz81, PageRank. (These are really good exercises, too!)