Notes After Finishing Advent of Code 2016

I finished this year's Advent of Code. I'd love to go back and clean up my code and write notes about each problem and the process I went through and the things I learned. Maybe I'll get around to that, maybe not.

Here are some ways of doing things that I found useful.

  • Write code to help visualize the problem. Helps me; YMMV.

    • Write code that draws pictures of the data. Take a hint from the problem descriptions — generate pictures similar to theirs. I found this helpful for the maze problems, among others. And sometimes it just makes things more fun.

    • For one problem I found that simply writing code to generate the following from the input helped me feel less confused about what had to be what mod what:

      we want t % 13 == 11
      we want t % 19 == 7
      we want t % 3 == 1
      we want t % 7 == 2
      we want t % 5 == 2
      we want t % 17 == 6
      
    • Another textual "visualization" aid: I used comments and indentation to mark up the "assembunny" code so that I could understand better what it was doing (here's what I did).

    • I didn't do much analog drawing (on paper or whiteboard), only a little during the later exercises. I used to be a compulsive whiteboarder — I'd like to recultivate that instinct.

  • If something feels like a hint it probably is, at least for AoC.

    • Example: for some reason Day 22 (moving data from disk to disk) was by far the hardest for me. I'm not sure how much or or how little that was due to inherent difficulty of the problem. But I do know I kept sabotaging myself by not letting the hints sink in, even when I was vaguely aware that the author of the problem was trying to tell me something.
  • Study the data — look for simplifying assumptions you can make that might help you get the answer by solving a less general case than the problem description alone might lead you to believe.

    • Day 22 is an example where this made all the difference in the world. Another that comes to mind is the "assembunny" problems.
  • If something is taking crazy long to execute, you may either have misunderstood or misread the problem, or you may be overlooking a hint.

  • Instrument your code with printf's that periodically show things like the size of your cache. This can provide clues to where there are leaks or inefficiencies in your code. For example, I realized my A* implementation was not properly putting nodes in the closed set, which led to an explosion of the open set — no wonder it was taking so long!

  • Testing. Do regression tests. Test the example data, which should run almost instantaneously, before trying your solution on Part 1. If Part 2 requires a lot of additional work, see if you can do sanity checks along the way to see if your code still solves Part 1. Sometimes your work on Parts 1 and 2 will be different enough that you can't really do this.

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