memoization AmE , auch: memoisation BrE

Another type of caching is storing computed results that will likely be needed again, or memoization.

For any , will give the Fibonacci number using dynamic programming and memoization.

It is the most straight-forward solution however, and can be sped up considerably using memoization.

Memoization is used to ensure that every hash table is only computed once.

You can easily accelerate this function via memoization yet lazy evaluation still wins.

Others can be more complicated and cannot be implemented as a recursive function with memoization.

In-table memoization can be employed to achieve this.

This approach is similar to memoization, which avoids repeating the calculation of the key corresponding to a specific input value.

In a backtracking scenario with such memoization, the parsing process is as follows:

Dynamic programming and memoization go together.

Alternatively, memoization could be used.

The best known example that takes advantage of memoization is an algorithm that computes the Fibonacci numbers.

The benefits of precomputation and memoization can be nullified by randomizing the hashing process.

Associative arrays have many applications including such fundamental programming patterns as memoization and the decorator pattern.

In the context of some logic programming languages, memoization is also known as tabling; see also lookup table.

Applications of automatic memoization have also been formally explored in the study of term rewriting and artificial intelligence.

The difference between dynamic programming and straightforward recursion is in caching or memoization of recursive calls.

A practical method of computing functions similar to Ackermann's is to use memoization of intermediate results.

For example, an object that uses memoization to cache the results of expensive computations could still be considered an immutable object.

In such cases it may be worth identifying and saving the solutions to these overlapping subproblems, a technique commonly known as memoization.

Memory functions use a dynamic programming technique called memoization in order to relieve the inefficiency of recursion that might occur.

Michie invented the memoization technique.

One simple solution is called memoization: each time we compute the minimum cost needed to multiply out a specific subsequence, we save it.

In this way, memoization allows a function to become more time-efficient the more often it is called, thus resulting in eventual overall speed up.

OCaml memoization - Implemented as a Camlp4 syntax extension.