Talk:Dynamic programming

Confusion
The first paragraph of the second part and the article on algorithms states that dynamic programming is a bottom-up approach, but later this article says dynamic programming may use bottom-up or top-down approaches. --zeno 11:46, 9 Feb 2004 (UTC)
 * I've revised that section. It was very vague and somewhat incorrect. I hope there's less confusion now. Derrick Coetzee 20:18, 5 Oct 2004 (UTC)

Weirdness
The page says "i dont know" in those terms, which is not only weirdly misplaced, but also improperly formatted. Someone check.

Misplaced section
The part following "The steps for using dynamic program goes as follows:" is out of place. Out of nowhere the article starts using terms like 'gap penalties' and 'sequence alignment.' Seems like this was copied from some article on a specific application, such as protien sequencing. Anon, Wed Jul 11 14:11:43 EDT 2007

Too many examples
This is not a textbook. One good example would suffice. Even worse, many of them are just too long (and boring). Put them in wikibooks. Unfortunately, any attempt to fix this article would be blocked as "vandalism". Way to go, Wikipedia!

Why ALGOL
Hello, in

function fib(n) if n <= 1 return n   return fib(n − 1) + fib(n − 2)

I doubt that the return function would return a false if, so maybe you make a if n larger or equal to 1 out of it ? That also has the nice side effect that the Fibonacci numbers would become larger than -1, like the original series is larger than +1, I guess that is what you intended ...

Approach to this article
I think this article suffers from bush-beating which is a common feature of computer language theory articles. If one closely examines the literature on DP, and compares it to algorithms for converting recursive programs into iterative (and thus, forgetful) programs, one will see a lot of overlap. The introduction here doesn't quite do the topic credit with words like 'paradigm' or 'optimization method'. It's an algorithm itself, and it operates on recursively defined data. That's concrete. You convert each sub-problem to also return its intermediates, then convert all sub-problems to re-usages of returned intermediates. Rinse and repeat. If the substructure is optimal, you get the optimum.

DP should be a reliable device in your brain, but this article contributes greatly to the nebular thinking about it. This device is really important because it comprehensively solves a key problem and reminds people to observe limits of applicability. 184.19.20.241 (talk) 12:47, 1 October 2023 (UTC)


 * I'd also like to add that Bellman's equation (although possibly correct, hard to tell) is completely overloaded for the purpose and that optimal substructure is the correct condition for optimality of DP. Concerns about infinite problems are addressed by noting that DP can apply to both numerical and symbolic computations. When DP is applied to data that define symbolic terms, that's how you do it with infinite and continuous objects. 184.19.20.241 (talk) 13:11, 1 October 2023 (UTC)