\nLet’s imagine a function called F1. Let’s further imagine that F1 uses a lot of resources – calls to it take a lot of computation power.<\/p>\n
In such a situation, you may want to store any result you get from calling F1. That way, any subsequent time you need that same answer, it need only be looked up in a table rather than recalculated by F1. A computer scientist would refer to said table as a cache. Furthermore, the idea of storing results from F1 in a cache is called memoization.<\/p>\n
\nMemoization is commonly used: a simple example is that your browser keeps a cache where it stores image files, etc, that it’s loaded from webpages you’ve visited. Those files are easier to reload from the cache than from the online source, so when you return to that site, it loads more quickly.<\/p>\n
An obvious trade-off of memoization is that it occupies extra memory. However, memory is cheap nowadays, whereas processing demands continue to increase. Hence, memoization is likely to increase.<\/p>\n
Source:<\/p>\n
codeburst.io<\/a><\/p>\nJack of Oracle Tutoring by Jack and Diane,<\/a> Campbell River, BC.\n","protected":false},"excerpt":{"rendered":" Tutoring computer science, terms arise. The tutor mentions memoization. Let’s imagine a function called F1. Let’s further imagine that F1 uses a lot of resources – calls to it take a lot of computation power. In such a situation, you …<\/p>\n