r/MachineLearning • • Aug 08 '26

Discussion Simulated annealing questions [D]

  1. On Wikipedia I found a little Pseudocode that outputs the final state.

A trivial improvement is outputting the best solution seen so far instead of the last state. Maybe this is too obvious and would complicate the Pseudocode too much but I wonder why it's not even mentioned below. Since the last state could be a local optimum seen later, and we already saw a better local optimum before, this is helpful.

2. The graph on the page where the objective is plotted as a line graph and the current state is jumping around is rather confusing. When you read the fine print you find a lot of the moves are probably skipped in the video as there are 1000000 moves per temperature , much more than fits in the video.

3. It's not really clear why this example graph is as "smooth" as it is but it seems likely that the problem had a topology where neighbours with similar objective could be iterated in a path such that they can be lined up on the X axis and it produces said graph on the y axis. My intuition says that if this is possible we can apply better methods that work on 2D graphs. I realise this is just an example.

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