Talk:Dynamic programming
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The first paragraph of the second part and the article onalgorithms states that dynamic programming is abottom-up approach, but later this article says dynamic programming may use bottom-up or top-down approaches.--zeno 11:46, 9 Feb 2004 (UTC)
The page says "i dont know" in those terms, which is not only weirdly misplaced, but also improperly formatted. Someone check.
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
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!
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 ...
Someprogramming languages can automaticallymemoize the result of a function call with a particular set of arguments, in order to speed upcall-by-name evaluation (this mechanism is referred to ascall-by-need).
This is technically correct but quite misleadingly worded. In call-by-need, the value at aparticular call site may be (but need not be) memoized. If I call the same function with the same arguments but from a different call site, the result will be computed again. That is, rather than a memo table, call-by-need at most provides a cached value for a particular function call.
For example, in Haskell, a "lazy" language with call-by-need, we can define factorial recursively as
factn=ifn==0then1elsen*fact(n-1)
If we then call it from a single call site
sum(map(\_->fact5)[1..3])
the call tofact 5 and its child calls will each be evaluated but once, even though the lambda appears to be evaluated three times.
However, in
fact5+fact5+fact5
each call tofact 5 and children will be evaluated separately: no memoization will occur.
This distinction makes call-by-need useless for memoization when decomposing a typical recursion: the subproblems are all called from separate call sites, and thus no memoization will occur.
Some languages make it possible portably (e.g.Scheme,Common Lisp,Perl orD).
Citations needed, at the least. Futures / promises are not a memoization mechanism, but a manual implementation of the call-by-need caching described above. In general Scheme and Perl, at least, are strict languages as far as I am aware.Bart Massey (talk)20:03, 19 February 2025 (UTC)[reply]
This section describes a dynamic programming approach, in which the state of a partially-filled matrix specifies, for each column, the number of 0's and 1's to be placed in that column. This simplifies the problem, but not nearly enough to compute all of the terms included in the OEIS. In the case of an 18 by 18 matrix, this approach requires 44,926,130,009,521,017 different states. For example, after filling one row there are 48,620 different states. Each of these states has (9, 8) nine times and (8, 9) nine times, and there is one way to reach each of these states.
A better approach is to realize that these states are essentially all the same. We can count matrices without knowing which columns are (9, 8) and which columns are (8, 9). I would describe all of these as a single state (9, 9, 0, 0, 0, 0, 0, 0, 0, 0), indicating 9 columns with 0 ones, 9 columns with 1 one, and 0 columns with 2, 3, 4, 5, 6, 7, 8, or 9 ones. There are 48,620 ways to reach this state.
After filling the second row, there are 10 different states: (0, 18, 0, 0, 0, 0, 0, 0, 0, 0), (1, 16, 1, 0, 0, 0, 0, 0, 0, 0), (2, 14, 2, 0, 0, 0, 0, 0, 0, 0), ..., (9, 0, 9, 0, 0, 0, 0, 0, 0, 0). This approach uses a total of 194,182 states to count about 2.2*1072 matrices.David.wasserman (talk)01:06, 19 May 2025 (UTC)[reply]
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