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Iterative design has long been used in engineering fields. One example is the plan–do–check–act cycle implemented in the 1960s. Most New product development or existing product improvement programs have a checking loop which is used for iterative purposes.
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
Iterative and incremental development is any combination of both iterative design (or iterative method) and incremental build model for development. Usage of the term began in software development , with a long-standing combination of the two terms iterative and incremental [ 1 ] having been widely suggested for large development efforts.
The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative – parts of the process often need to be repeated many times before another can be entered – though the part(s) that get iterated and the number of such cycles in any given project may vary.
PDCA or plan–do–check–act (sometimes called plan–do–check–adjust) is an iterative design and management method used in business for the control and continual improvement of processes and products. [1] It is also known as the Shewhart cycle, or the control circle/cycle. Another version of this PDCA cycle is OPDCA. [2]
In mathematics, iteration may refer to the process of iterating a function, i.e. applying a function repeatedly, using the output from one iteration as the input to the next. Iteration of apparently simple functions can produce complex behaviors and difficult problems – for examples, see the Collatz conjecture and juggler sequences.
In numerical analysis, fixed-point iteration is a method of computing fixed points of a function.. More specifically, given a function defined on the real numbers with real values and given a point in the domain of , the fixed-point iteration is + = (), =,,, … which gives rise to the sequence,,, … of iterated function applications , (), (()), … which is hoped to converge to a point .
For example, the dynamic programming algorithms described in the next section require an explicit model, and Monte Carlo tree search requires a generative model (or an episodic simulator that can be copied at any state), whereas most reinforcement learning algorithms require only an episodic simulator.