I think if you find those algorithms obvious and you're able to write memoized solutions, then you already know most of what you need to know about dynamic prog

I think if you find those algorithms obvious and you're able to write memoized solutions, then you already know most of what you need to know about dynamic programming. For example, Dijkstra's algorithm for shortest paths can be seen as DP. The main thing is to write down a formulation for the answer for n in terms of answers for smaller n, or the answer for (n, k) in terms of answers for smaller (n, k). (And don't worry about how you'd compute them, just focus on getting a correct expression in terms of smaller ones. http://okasaki.blogspot.com/2008/10/score-one-for-induction.html http://okasaki.blogspot.com/2008/10/score-one-for-induction....) It can be tricky to formulate exactly what you're counting / measuring (e.g. what's "k" and what's the quantity you're optimizing for (n, k)), but once you have that, and once you have the recurrence, you can consider it a separate (and independent) step to figure out in which order to compute the values, so that you have each value by the time you need it (i.e. the bottom-up formulation that you said is the tricky part for you). Beyond that I guess lots of practice with problems… e.g. about a decade ago there used to be weekly(?) TopCoder contests with editorials written later explaining the solutions (and the problems were graded by difficulty and even how many people solved it), and the DPs could get quite tricky. I believe those are still happening, and now there are other resources like LeetCode or whatnot. Do you have an example of a problem that you struggled with, to see what's missing?