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A simple dynamic array can be constructed by allocating an array of fixed-size, typically larger than the number of elements immediately required. The elements of the dynamic array are stored contiguously at the start of the underlying array, and the remaining positions towards the end of the underlying array are reserved, or unused.
Dynamic languages provide flexibility. This allows developers to write more adaptable and concise code. For instance, in a dynamic language, a variable can start as an integer. It can later be reassigned to hold a string without explicit type declarations. This feature of dynamic typing enables more fluid and less restrictive coding.
Multiple dispatch or multimethods is a feature of some programming languages in which a function or method can be dynamically dispatched based on the run-time (dynamic) type or, in the more general case, some other attribute of more than one of its arguments. [1]
As long as the new class definition had the same class and method names, the code would still work. In this sense it is similar to the traditional definition of late binding. Currently, it is popular to use the term late binding in Java programming as a synonym for dynamic dispatch.
The purpose of dynamic dispatch is to defer the selection of an appropriate implementation until the run time type of a parameter (or multiple parameters) is known. Dynamic dispatch is different from late binding (also known as dynamic binding). Name binding associates a name with an operation. A polymorphic operation has several ...
Dynamic compilation is a process used by some programming language implementations to gain performance during program execution. Although the technique originated in Smalltalk , [ 1 ] the best-known language that uses this technique is Java .
From a dynamic programming point of view, Dijkstra's algorithm for the shortest path problem is a successive approximation scheme that solves the dynamic programming functional equation for the shortest path problem by the Reaching method. [8] [9] [10] In fact, Dijkstra's explanation of the logic behind the algorithm, [11] namely Problem 2.
JIT compilation is a form of dynamic compilation, and allows adaptive optimization such as dynamic recompilation and microarchitecture-specific speedups. [nb 1] [3] Interpretation and JIT compilation are particularly suited for dynamic programming languages, as the runtime system can handle late-bound data types and enforce security guarantees.