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  1. In comes memoization, a way for our function to remember (cache) the results.[1]
  2. Wikipedia entry on memoization says that it is an optimization technique to speed up programs by storing results of expensive function calls.[2]
  3. This article demonstrates Memoization in JavaScript.[3]
  4. We begin with a barebone JavaScript implementation of Memoization.[3]
  5. Memoization is a caching technique for functions.[3]
  6. The following memoization wrapper is one simple way to implement memoization.[3]
  7. The theory behind memoization is that if you have a function you need to call several times in one request, it would only be calculated the first time that function is called with those arguments.[4]
  8. Memoization lets us cache the values of slow functions.[5]
  9. Remember, memoization is the process of caching returned values from a function for particular in input.[5]
  10. And that’s it: our new memoization function is ready.[5]
  11. First, memoization is a time-space tradeoff: we save some time in exchange for chewing up a little extra memory to store all our cached data.[5]
  12. A clasic example of function which greatelly benefits from memoization is a recursively defined Fibonacci number function.[6]
  13. And this is precisely what we have to do - use two memoization caches![6]
  14. Normally you don't have to modify your monadic function definition to run ArrayCache -based memoization: just use appropriate eval* or run* function.[6]
  15. : I was just playing around with some code, and came up with this simple memoization procedure.[7]
  16. Memoization is the act of storing the result of a function call after we run it, in the function itself.[8]
  17. Memoization works if the result of calling a function with the same set of arguments results in the same output.[8]
  18. So, database queries, network requests, writing to files and other non-pure operations cannot be optimized with memoization.[8]
  19. Put simply, memoization is saving a method's return value so it does not have to be recomputed each time.[9]
  20. When using memoization there are some questions we need to ask ourselves: How often is the value accessed?[9]
  21. If so the memoization probably needs to take this into account.[9]
  22. Most of the time memoization is done at the instance level, meaning we use an instance variable to hold the computed value.[9]
  23. The Seven Bridges Platform enables memoization by caching results at the most basic level: that of individual tools.[10]
  24. Memoization enables researchers to avoid duplicate computations across related workflows.[10]
  25. Memoization enables more efficient exploration by allowing researchers to run a workflow with one set of parameters and then rerun it repeatedly with changed values.[10]
  26. For more information on how to utilize memoization, please visit the memoization documentation.[10]
  27. Memoization is a strategy for preventing values to be computed multiple times.[11]
  28. The problem here is that memoization only applies at the top level.[11]
  29. We need to somehow be able to embed the memoization into the recursive calls.[11]
  30. This optimization is called memoization.[12]
  31. In computing, memoization is an optimization technique used primarily to speed up computer programs by having function calls avoid repeating the calculation of results for previously processed inputs.[13]
  32. Although related to caching, memoization refers to a specific case of this optimization, distinguishing it from forms of caching such as buffering or page replacement.[13]
  33. In the context of some logic programming languages, memoization is also known as tabling; see also lookup table.[13]
  34. Memoization is a term introduced by Donald Michie in 1968, which comes from the latin word memorandum (to be remembered).[14]
  35. Memoization is a method used in computer science to speed up calculations by storing (remembering) past calculations.[14]
  36. Let’s now walk through the steps of implementing the memoization method.[14]
  37. Next, we will define our memoization function.[14]
  38. Abstract : Memoization is the technique of saving result of executions so that future executions can be omitted when the input set repeats.[15]
  39. In this paper, we focus on software memoization for procedural languages like C and Fortran, at the granularity of a function.[15]
  40. As far as users are concerned, enabling memoization is as simple as setting an environment variable.[15]
  41. On standard benchmark applications that extensively call the transcendental functions we report memoization benefits of upto 50% on Intel Ivy bridge and upto 10% on ARM Cortex-A9.[15]
  42. We study how the adoption of an evaluation mechanism with sharing and memoization impacts the class of functions which can be computed in polynomial time.[16]
  43. Memoization is a higher order function that caches another function.[17]
  44. Implement some kind of memoization system yourself, whether you’re doing a higher-order function or, maybe, you’re an object-oriented language.[17]
  45. However, in memoization , caching is always typed, which means f(3) and f(3.0) will be treated as different calls and cached separately.[18]
  46. Unlike lru_cache , memoization is designed to be highly extensible, which make it easy for developers to add and integrate any caching algorithms (beyond FIFO, LRU and LFU) into this library.[18]
  47. for functions with unhashable arguments, the default setting may not enable memoization to work properly.[18]
  48. By default, memoization tries to combine all your function arguments and calculate its hash value using hash() .[18]
  49. memoizedFcn = memoize( fh ) adds memoization semantics to the input function handle, and returns a MemoizedFunction object.[19]
  50. The memoization of a function is associated with the input function and not with the MemoizedFunction object.[19]
  51. With memoization, when a function is provided an input, it does the required computation and stores the result to cache before returning the value.[20]
  52. The concept of memoization in JavaScript is built majorly on two concepts.[20]
  53. Now let’s see how memoization utilizes these concept using some more code samples.[20]
  54. Now we’ve seen just how much memoization can impact the performance of our applications on a functional level.[20]
  55. Memoization is best technique to save on memory or CPU cycles when we deal with repeated operations.[21]
  56. Memoization is a way to lower a function's time cost in exchange for space cost; that is, memoized functions become optimized for speed in exchange for a higher use of computer memory space.[22]
  57. Memoization is heavily used in compilers for functional programming languages, which often use call by name evaluation strategy.[22]
  58. The techniques employed by Peter Norvig have application not only in Common Lisp (the language in which his paper demonstrated automatic memoization), but also in various other programming languages.[22]
  59. In those languages that allow closures, memoization can be effected implicitly via a functor factory that returns a wrapped memoized function object in a decorator pattern.[22]
  60. As mentioned earlier, memoization reminds us dynamic programming.[23]
  61. Although DP typically uses bottom-up approach and saves the results of the sub-problems in an array table, while memoization uses top-down approach and saves the results in a hash table.[23]
  62. Memoization solves the problem “top-down” by maintaining a map of already solved sub-problems.[23]
  63. Memoization is a technique of caching function results “in” the function itself to make the function have memory, and the callers won’t need to know if the function is memoized or not.[23]
  64. There are several excellent articles that talk about the optimization technique called memoization.[24]
  65. What we’re going to do is give you a brief overview of what memoization is.[24]
  66. Let’s break down exactly what memoization is doing by looking at a very simple and impractical example.[24]
  67. Memoization becomes demystified when you boil it down to key-value pairs.[24]
  68. Since only one parameter is non-constant, this method is known as 1-D memoization.[25]
  69. So this problem has Overlapping Substructure property and recomputation of same subproblems can be avoided by either using Memoization or Tabulation.[25]
  70. A common point of observation to use memoization in the recursive code will be the two non-constant arguments M and N in every function call.[25]
  71. The function has 4 arguments, but 2 arguments are constant which do not affect the Memoization.[25]
  72. One important use of hash tables is for memoization , in which a previously computed result is stored in the table and retrieved later.[26]
  73. A good choice of progress measure, not only here but also for many uses of memoization, is the number of nonempty entries in the table (i.e. entries that contain Some integer value rather than None ).[26]
  74. We can use memoization to turn this into a linear-time algorithm.[26]
  75. Why was memoization so effective for solving this problem?[26]

소스

  1. ↑ How to use Memoize to cache JavaScript function results and speed up your code
  2. ↑ Memoization in C# - Functional approach (Part 1)
  3. ↑ 3.0 3.1 3.2 3.3 Memoization in JavaScript, Angular and React
  4. ↑ django-memoize — django-memoize 2.1.0 documentation
  5. ↑ 5.0 5.1 5.2 5.3 Using memoization to speed up slow functions – Hacking with Swift+
  6. ↑ 6.0 6.1 6.2 monad-memo: Memoization monad transformer
  7. ↑ memoizing
  8. ↑ 8.0 8.1 8.2 Memoization in JavaScript
  9. ↑ 9.0 9.1 9.2 9.3 Speeding up Rails with Memoization
  10. ↑ 10.0 10.1 10.2 10.3 How Memoization Enhances Efficiency for Large Scale Genomic Analysis Research Projects
  11. ↑ 11.0 11.1 11.2 Notes on Computing
  12. ↑ JavaScript: The Good Parts [Book]
  13. ↑ 13.0 13.1 13.2 What does memoization mean?
  14. ↑ 14.0 14.1 14.2 14.3 Memoization in Python
  15. ↑ 15.0 15.1 15.2 15.3 Intercepting Functions for Memoization: A Case Study Using Transcendental Functions
  16. ↑ On sharing, memoization, and polynomial time ☆
  17. ↑ 17.0 17.1 What is memoization?
  18. ↑ 18.0 18.1 18.2 18.3 memoization
  19. ↑ 19.0 19.1 Add memoization semantics to function handle
  20. ↑ 20.0 20.1 20.2 20.3 Understanding Memoization In JavaScript
  21. ↑ memoize-fs
  22. ↑ 22.0 22.1 22.2 22.3 Memoization
  23. ↑ 23.0 23.1 23.2 23.3 WTF is Memoization
  24. ↑ 24.0 24.1 24.2 24.3 Understanding JavaScript Memoization In 3 Minutes
  25. ↑ 25.0 25.1 25.2 25.3 Memoization (1D, 2D and 3D)
  26. ↑ 26.0 26.1 26.2 26.3 Lecture 22: Memoization

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  • [{'LEMMA': 'memoization'}]
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