It's not a misspelling of the word memorization, though in a way it has something in common. There are many types of cache (hardware caches, network caches, software caches…), with different applications and performances. Just a bit different. Caching stores recent past work. It might be quite a long term on a server side, but cannot and should not be a real long term cache on a client side. Memoization is used to avoid recomputing expensive things and make rendering faster. Memoization even sounds like a moment. Every computer has a number of caches built into the hardware and operating system to optimize memory access, speedup … Memoization is similar to caching with regards to memory storage. Memoization. This is a top-down approach, and it has extensive recursive calls. In Dynamic Programming (Dynamic Tables), you break the complex problem into smaller problems and solve each of the problems once. Memoization effectively refers to remembering ("memoization" → "memorandum" → to be remembered) results of method calls based on the method inputs and then returning the remembered result rather than computing the result again. You could configure lodash cache to limit the number of saved values. It can change the asymptotic complexity of some algorithms. Long story short, but memoization is not a cache, not a persistent cache. The semantics of a "buffer" and a "cache" are not totally different; even so, there are fundamental differences in intent between the process of caching and the process of buffering. While caching can refer in general to any storing technique (like HTTP caching) for future use, memoizing specifically involves caching the return values of a function. Memoization and caching to me are almost the same thing, both involving storing precomputed results under a key, unless by caching you get concerned about the physical CPU caches and worry about cache alignment. Reasons that pure functions produce an output which depends on the input without changing the program's state (side effects). You can think of it as a cache for method results. Following best practices, memoization should be implemented on pure functions. Caching the results of some operation is so prevalent in computer science that the world would slow down considerably without it. Memoization vs. Caching. Memoization with Decorators Definition of Memoization. Not cache, let’s call it kashe. 1 Memoization vs Caching. It's based on the Latin word memorandum, meaning "to be remembered". The term "memoization" was introduced by Donald Michie in the year 1968. The core concept of caching is keeping in a high-speed data structure the results of past work in hopes that you’ll use that data again soon. I would argue that in a frontend application the best limit for a memoize cache is just one value: the latest computed one. In software, this takes two forms: Memoizing stores all past work. In this post I will discuss one type of software cache: memoization. Using a cache that by default can create leaks is thus not recommended. Buffer vs. cache. As memoization trades space for speed, memoization should be used in functions that have a limited input range so as to aid faster checkups. Memoization is a software cache technique in which the results of functions are saved in a cache. Memoization is like caching. 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. Memoization. Both techniques attempt to increase efficiency by reducing the number of calls to computationally expensive code. When to memoize your functions Although it might look like memoization can be used with all functions, it actually has limited use cases: Memoization vs Caching. 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