memoization

What is the difference between memoization and dynamic programming?

由 与世无争的帅哥 提交于 2019-11-26 13:57:33
What is the difference between memoization and dynamic programming? I think dynamic programming is a subset of memoization. Is it right? What is difference between memoization and dynamic programming? Memoization is a term describing an optimization technique where you cache previously computed results, and return the cached result when the same computation is needed again. Dynamic programming is a technique for solving problems of recursive nature, iteratively and is applicable when the computations of the subproblems overlap. Dynamic programming is typically implemented using tabulation, but

How does Data.MemoCombinators work?

由 ぃ、小莉子 提交于 2019-11-26 12:38:07
问题 I\'ve been looking at the source for Data.MemoCombinators but I can\'t really see where the heart of it is. Please explain to me what the logic is behind all of these combinators and the mechanics of how they actually work to speed up your program in real world programming. I\'m looking for specifics for this implementation, and optionally comparison/contrast with other Haskell approaches to memoization. I understand what memoization is and am not looking for a description of how it works in

When is memoization automatic in GHC Haskell?

由 不打扰是莪最后的温柔 提交于 2019-11-26 10:13:22
I can't figure out why m1 is apparently memoized while m2 is not in the following: m1 = ((filter odd [1..]) !!) m2 n = ((filter odd [1..]) !! n) m1 10000000 takes about 1.5 seconds on the first call, and a fraction of that on subsequent calls (presumably it caches the list), whereas m2 10000000 always takes the same amount of time (rebuilding the list with each call). Any idea what's going on? Are there any rules of thumb as to if and when GHC will memoize a function? Thanks. GHC does not memoize functions. It does, however, compute any given expression in the code at most once per time that

How is this fibonacci-function memoized?

由 ε祈祈猫儿з 提交于 2019-11-26 10:08:50
By what mechanism is this fibonacci-function memoized? fib = (map fib' [0..] !!) where fib' 1 = 1 fib' 2 = 1 fib' n = fib (n-2) + fib (n-1) And on a related note, why is this version not? fib n = (map fib' [0..] !! n) where fib' 1 = 1 fib' 2 = 1 fib' n = fib (n-2) + fib (n-1) Will Ness The evaluation mechanism in Haskell is by-need : when a value is needed, it is calculated, and kept ready in case it is asked for again. If we define some list, xs=[0..] and later ask for its 100th element, xs!!99 , the 100th slot in the list gets "fleshed out", holding the number 99 now, ready for next access.

Does Python intern strings?

由 丶灬走出姿态 提交于 2019-11-26 09:12:25
问题 In Java, explicitly declared Strings are interned by the JVM, so that subsequent declarations of the same String results in two pointers to the same String instance, rather than two separate (but identical) Strings. For example: public String baz() { String a = \"astring\"; return a; } public String bar() { String b = \"astring\" return b; } public void main() { String a = baz() String b = bar() assert(a == b) // passes } My question is, does CPython (or any other Python runtime) do the same

Numpy Pure Functions for performance, caching

由 狂风中的少年 提交于 2019-11-26 09:11:39
问题 I\'m writing some moderately performance critical code in numpy. This code will be in the inner most loop, of a computation that\'s run time is measured in hours. A quick calculation suggest that this code will be executed up something like 10^12 times, in some variations of the calculation. So the function is to calculate sigmoid(X) and another to calculate its derivative (gradient). Sigmoid has the property that for y=sigmoid(x), dy/dx= y(1-y) In python for numpy this looks like: sigmoid =

Options for caching / memoization / hashing in R

由 和自甴很熟 提交于 2019-11-26 08:52:27
问题 I am trying to find a simple way to use something like Perl\'s hash functions in R (essentially caching), as I intended to do both Perl-style hashing and write my own memoisation of calculations. However, others have beaten me to the punch and have packages for memoisation. The more I dig, the more I find, e.g. memoise and R.cache , but differences aren\'t readily clear. In addition, it\'s not clear how else one can get Perl-style hashes (or Python-style dictionaries) and write one\'s own

Writing Universal memoization function in C++11

由 跟風遠走 提交于 2019-11-26 05:18:52
问题 Looking for a way to implement a universal generic memoization function which will take a function and return the memoized version of the same? Looking for something like @memo (from Norving\'s site)decorator in python. def memo(f): table = {} def fmemo(*args): if args not in table: table[args] = f(*args) return table[args] fmemo.memo = table return fmemo Going more general, is there a way to express generic and reusable decorators in C++, possibly using the new features of C++11? 回答1: A

What is the difference between memoization and dynamic programming?

由 微笑、不失礼 提交于 2019-11-26 02:59:47
问题 What is the difference between memoization and dynamic programming? I think dynamic programming is a subset of memoization. Is it right? 回答1: What is difference between memoization and dynamic programming? Memoization is a term describing an optimization technique where you cache previously computed results, and return the cached result when the same computation is needed again. Dynamic programming is a technique for solving problems of recursive nature, iteratively and is applicable when the

What is the difference between bottom-up and top-down?

由 送分小仙女□ 提交于 2019-11-26 02:15:40
问题 The bottom-up approach (to dynamic programming) consists in first looking at the \"smaller\" subproblems, and then solve the larger subproblems using the solution to the smaller problems. The top-down consists in solving the problem in a \"natural manner\" and check if you have calculated the solution to the subproblem before. I\'m a little confused. What is the difference between these two? 回答1: rev4: A very eloquent comment by user Sammaron has noted that, perhaps, this answer previously
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